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lang-main/test-notebooks/misc.ipynb
Florian Förster 9197146d2c enhanced timeline, improved handling of odd cases
2024-07-24 16:49:19 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c930ce7b-e060-4021-b97c-192045f17122",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "af118d77-d87a-4687-be5b-e810a24c403e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 06:14:08 +0000 | io:INFO | Loaded TOML config file successfully.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from lang_main import io\n",
"from lang_main.analysis.graphs import rescale_edge_weights, get_graph_metadata\n",
"\n",
"from pathlib import Path\n",
"import pickle\n",
"import base64\n",
"import os\n",
"from logging import NullHandler\n",
"\n",
"import numpy as np\n",
"import networkx as nx\n",
"\n",
"import py4cytoscape as p4c\n",
"#import py4cytoscape.py4cytoscape_logger_settings as p4c_logging\n",
"#p4c.set_summary_logger(False)\n",
"#p4c_logging._SUMMARY_LOG_LEVEL = 'ERROR'\n",
"# p4c_logging._DETAIL_LOG_LEVEL = 'ERROR'\n",
"#p4c.py4cytoscape_logger.detail_logger.setLevel('ERROR')\n",
"#p4c.py4cytoscape_logger.detail_logger.removeHandler(p4c.py4cytoscape_logger.detail_handler)\n",
"#p4c.py4cytoscape_logger.detail_logger.addHandler(NullHandler())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4256081a-6364-4e8f-8cfd-799912ca6b94",
"metadata": {},
"outputs": [],
"source": [
"res_path = Path(r'A:\\Arbeitsaufgaben\\lang-main\\scripts\\results\\test_20240619')\n",
"assert res_path.exists()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e9a92ad6-5e63-49c4-b9e7-9f81da8549fe",
"metadata": {},
"outputs": [],
"source": [
"#obj = 'TK-GRAPH_POSTPROCESSING.pkl'\n",
"obj = 'TK-GRAPH_ANALYSIS.pkl'\n",
"load_pth = res_path / obj\n",
"assert load_pth.exists()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c2421d89-ed8c-41dd-b363-ad5b5b716704",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 06:14:16 +0000 | io:INFO | Loaded file successfully.\n"
]
}
],
"source": [
"ret = io.load_pickle(load_pth)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ca25a7f2-84af-4b5e-89d6-b139fca35617",
"metadata": {},
"outputs": [],
"source": [
"tkg = ret[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ff7e7ab6-67d9-4a2c-b668-cf10740f7542",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TokenGraph(name: TokenGraph, number of nodes: 158, number of edges: 192)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "66901689-8b95-400a-b2fb-11d3ea215512",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg.rescaled_weights"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "842e01fa-29cd-4028-9461-c7af24e01c33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'degree_weighted': 186350}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg.nodes['Wartungstätigkeit']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8d36d22e-73fd-44fe-ab08-98f8186bc6b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'degree_weighted': 186350}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg.undirected.nodes['Wartungstätigkeit']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1e61aca3-efea-4e38-8174-5ca4b2585256",
"metadata": {},
"outputs": [],
"source": [
"obj = 'TK-GRAPH_POSTPROCESSING.pkl'\n",
"# obj = 'TK-GRAPH_ANALYSIS.pkl'\n",
"load_pth = res_path / obj\n",
"assert load_pth.exists()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5d83c04c-03ab-4086-a4e9-ae430e4c6090",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-19 05:56:10 +0000 | io:INFO | Loaded file successfully.\n"
]
}
],
"source": [
"ret = io.load_pickle(load_pth)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4718b54e-0891-4f70-8c67-90c439bc8bfd",
"metadata": {},
"outputs": [],
"source": [
"tkg = ret[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ddcb4ff0-eac4-45ba-9c6e-83ada4b0276c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TokenGraph(name: TokenGraph, number of nodes: 6859, number of edges: 25499)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3d514552-3b55-41d1-af80-a1b559711608",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tkg.rescaled_weights"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b73844e0-4242-4a8c-b552-48f10df34cc0",
"metadata": {},
"outputs": [],
"source": [
"directed, undirected = tkg.rescale_edge_weights()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "593b9f87-4e9f-45e4-9367-55347924357b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'num_nodes': 6859,\n",
" 'num_edges': 25499,\n",
" 'min_edge_weight': 0.0952,\n",
" 'max_edge_weight': 1.0,\n",
" 'node_memory': 433996,\n",
" 'edge_memory': 1427944,\n",
" 'total_memory': 1861940}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"directed.metadata_directed"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "aed4354a-69e4-4215-bd4b-a6c7c37c3ac5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'num_nodes': 6859,\n",
" 'num_edges': 24796,\n",
" 'min_edge_weight': 1,\n",
" 'max_edge_weight': 92690,\n",
" 'node_memory': 433996,\n",
" 'edge_memory': 1388576,\n",
" 'total_memory': 1822572}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"directed.metadata_undirected"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "587de2ae-26ed-42f5-a8bd-104f9cbf1490",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'num_nodes': 6859,\n",
" 'num_edges': 24796,\n",
" 'min_edge_weight': 0.0952,\n",
" 'max_edge_weight': 1.0,\n",
" 'node_memory': 433996,\n",
" 'edge_memory': 1388576,\n",
" 'total_memory': 1822572}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_graph_metadata(undirected)"
]
},
{
"cell_type": "markdown",
"id": "859be6a3-a919-433a-a0a7-f4d74cdc6bf7",
"metadata": {},
"source": [
"break_early = False\n",
"i = 0\n",
"for idx, (node1, node2) in enumerate(list(Gtest.edges)):\n",
" if break_early and i == 10:\n",
" break\n",
" Gtest[node1][node2]['weight'] = adjusted_weights[idx]\n",
" \n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f381b25a-6149-4a2a-876c-4cbd8bb9bd04",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wartungstätigkeit Vorgabe 1.0\n",
"Wartungstätigkeit Maschinenhersteller 1.0\n",
"Wartungstätigkeit Maschinenbediener 0.8215\n",
"Wartungstätigkeit Laserabteilung 0.8215\n",
"Wartungstätigkeit Arbeitsplan 0.8219\n",
"Wartungstätigkeit abarbeiten 0.8215\n",
"Wartungstätigkeit Webmaschinenkontrollliste 0.2534\n",
"Wartungstätigkeit sehen 0.2534\n",
"Vorgabe Maschinenhersteller 1.0\n",
"Vorgabe Wartungsplan 0.9181\n"
]
}
],
"source": [
"break_early = True\n",
"i = 0\n",
"for n1, n2, w in directed.edges.data('weight'):\n",
" if break_early and i == 10:\n",
" break\n",
" print(n1, n2, w)\n",
"\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a7929935-3bd2-4eb8-907c-4d37251f11ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wartungstätigkeit Vorgabe 1.0\n",
"Wartungstätigkeit Maschinenhersteller 1.0\n",
"Wartungstätigkeit sehen 0.2534\n",
"Wartungstätigkeit Maschinenbediener 0.8215\n",
"Wartungstätigkeit Laserabteilung 0.8215\n",
"Wartungstätigkeit Arbeitsplan 0.8219\n",
"Wartungstätigkeit abarbeiten 0.8215\n",
"Wartungstätigkeit Webmaschinenkontrollliste 0.2534\n",
"Vorgabe Maschinenhersteller 1.0\n",
"Vorgabe Wartungsplan 0.9181\n"
]
}
],
"source": [
"break_early = True\n",
"i = 0\n",
"for n1, n2, w in undirected.edges.data('weight'):\n",
" if break_early and i == 10:\n",
" break\n",
" print(n1, n2, w)\n",
"\n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2e2cbbe-68ef-4ea0-9ed0-b114be1efd08",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "55665c2f-9a86-47f4-9125-557666e1f541",
"metadata": {},
"source": [
"---\n",
"\n",
"# Load re-scaled Token Graph"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2a3be1eb-b289-46ab-8d70-53110ad2806c",
"metadata": {},
"outputs": [],
"source": [
"#obj = 'TK-GRAPH_POSTPROCESSING.pkl'\n",
"obj = 'TK-GRAPH_ANALYSIS_RESCALED.pkl'\n",
"load_pth = res_path / obj\n",
"assert load_pth.exists()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "64d8ba18-b1e2-470d-8bf5-9dd7cfec31de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 06:14:31 +0000 | io:INFO | Loaded file successfully.\n"
]
}
],
"source": [
"ret = io.load_pickle(load_pth)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d80522a0-c13a-42d3-af9d-8e10914c7831",
"metadata": {},
"outputs": [],
"source": [
"tk_resc = ret[1]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4a19d096-27f8-4626-97ee-31c0f84a294f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'num_nodes': 158,\n",
" 'num_edges': 189,\n",
" 'min_edge_weight': 0.0952,\n",
" 'max_edge_weight': 1.0,\n",
" 'node_memory': 9908,\n",
" 'edge_memory': 10584,\n",
" 'total_memory': 20492}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_graph_metadata(tk_resc)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "86fe9b96-2e96-4a6c-a511-6a9c16b8fd63",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wartungstätigkeit 3.1190000474452972\n",
"Vorgabe 4.145399987697601\n",
"Maschinenhersteller 2.0\n",
"Sichtkontrolle 0.8227999806404114\n",
"Reinigung 1.7093999981880188\n",
"Überprüfung 2.0071999728679657\n",
"Ölabscheider 0.7318999767303467\n",
"Kontrolle 6.2471999898552895\n",
"C-Anlage 0.6929000020027161\n",
"Stabbreithalter 0.5758000016212463\n"
]
}
],
"source": [
"break_early = True\n",
"n = 10\n",
"\n",
"for idx, (node, weighted_degree) in enumerate(tk_resc.degree(weight='weight')):\n",
" if break_early and idx == n:\n",
" break\n",
" print(node, weighted_degree)"
]
},
{
"cell_type": "code",
"execution_count": 312,
"id": "420fd2db-98d0-48df-8a01-b4355778a6e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Wartungstätigkeit': 3.1190000474452972,\n",
" 'Vorgabe': 4.145399987697601,\n",
" 'Maschinenhersteller': 2.0,\n",
" 'Sichtkontrolle': 0.8227999806404114,\n",
" 'Reinigung': 1.7093999981880188,\n",
" 'Überprüfung': 2.0071999728679657,\n",
" 'Ölabscheider': 0.7318999767303467,\n",
" 'Kontrolle': 6.2471999898552895,\n",
" 'C-Anlage': 0.6929000020027161,\n",
" 'Stabbreithalter': 0.5758000016212463,\n",
" 'Scharniere': 0.7002999782562256,\n",
" '--': 0.7002999782562256,\n",
" 'Schließvorrichtung': 0.7059999704360962,\n",
" 'Schloß': 0.7059999704360962,\n",
" 'Kompressorstation': 0.5514000058174133,\n",
" 'Wasseraufbereitungsanlage': 0.5105999708175659,\n",
" 'Heizungsanlage': 0.5101000070571899,\n",
" 'Druckkontrolle': 1.140199989080429,\n",
" 'bar': 1.2935999631881714,\n",
" 'machen': 1.4854000210762024,\n",
" 'gegebenenfalls': 0.4934000074863434,\n",
" 'Filter': 0.4934000074863434,\n",
" 'sauber': 0.4986000061035156,\n",
" 'Leiter': 0.6482000052928925,\n",
" 'Analyse': 0.42980000376701355,\n",
" 'Kesselwasser': 0.42980000376701355,\n",
" 'überprüfen': 0.42980000376701355,\n",
" 'Wasserverbrauch': 0.42980000376701355,\n",
" 'auffüllen': 0.7113999724388123,\n",
" 'Desifektionsmittel': 0.35569998621940613,\n",
" 'Aschenbecher': 0.7113999724388123,\n",
" 'leeren': 0.35569998621940613,\n",
" 'Wartung': 0.46550001204013824,\n",
" 'Toilette': 0.2621000111103058,\n",
" 'Wartungsplan': 2.1448000073432922,\n",
" 'sehen': 3.650799944996834,\n",
" 'Extradatum': 3.2020999789237976,\n",
" 'Kompensator': 0.20739999413490295,\n",
" 'Verschleiß': 0.6553999930620193,\n",
" 'Dichtigkeit': 0.4373999983072281,\n",
" 'Kühlturm': 0.20340000092983246,\n",
" 'schmieren': 1.4377999901771545,\n",
" 'Rieme': 0.4068000018596649,\n",
" 'Maschinenbediener': 0.5580000281333923,\n",
" 'Laserabteilung': 0.5580000281333923,\n",
" 'Arbeitsplan': 0.7273000031709671,\n",
" 'abarbeiten': 0.7170000076293945,\n",
" 'Küsters-Anlage': 0.6922999918460846,\n",
" 'Anlage': 0.6487999856472015,\n",
" 'Leckage': 0.7812000215053558,\n",
" 'prüfen': 1.5511000156402588,\n",
" 'abschmieren': 0.4147999882698059,\n",
" 'Lager': 0.4318999946117401,\n",
" 'Campen-Aufwickler': 0.20739999413490295,\n",
" 'Linearkugellager': 0.4399999976158142,\n",
" 'Campen-Abwickler': 0.20739999413490295,\n",
" 'Wumag-Trockner': 0.20739999413490295,\n",
" 'Gesamtanlage': 0.3847000002861023,\n",
" 'Beschädigung': 0.8136000037193298,\n",
" 'usw.': 0.8136000037193298,\n",
" 'Stand': 0.9007999897003174,\n",
" 'Stöppel': 0.6636999845504761,\n",
" '-Leiterprüfung': 0.8335999846458435,\n",
" 'Herr': 5.480200096964836,\n",
" 'Buschmann': 1.108900010585785,\n",
" 'derzeit': 1.4514999985694885,\n",
" 'Förster': 1.411899983882904,\n",
" 'terminieren': 0.44359999895095825,\n",
" 'reparieren': 0.1459999978542328,\n",
" 'Akku': 0.1282999962568283,\n",
" 'Firma': 3.6372000351548195,\n",
" 'Hawker': 0.1282999962568283,\n",
" 'Prüfung': 0.23270000517368317,\n",
" 'V': 0.1177000030875206,\n",
" 'Erste-Hilfe-Koffer': 0.11500000208616257,\n",
" 'orange': 0.11500000208616257,\n",
" 'Blombe': 0.46000000834465027,\n",
" 'vorhanden': 0.11500000208616257,\n",
" 'bitte': 0.11500000208616257,\n",
" 'Ticket': 0.48810001462697983,\n",
" 'Magazin': 0.48810001462697983,\n",
" 'Leiterprüfung': 0.19040000438690186,\n",
" 'Arbeit': 0.09520000219345093,\n",
" 'Abteilungsleiter': 0.48180001229047775,\n",
" 'Email': 1.6289000436663628,\n",
" 'Eigenverantwortlichkeit': 1.9827000498771667,\n",
" 'Mithilfe': 1.7449000477790833,\n",
" 'Graf': 1.884600043296814,\n",
" 'informieren': 0.5197000131011009,\n",
" 'Pflasterschrank': 0.11500000208616257,\n",
" 'Bedarf': 0.8617000207304955,\n",
" 'Verbandsmaterial': 0.3450000062584877,\n",
" 'Auflistung': 0.23000000417232513,\n",
" 'finden': 0.5750000104308128,\n",
" 'Extradate': 0.3450000062584877,\n",
" 'intern': 0.23000000417232513,\n",
" 'Objekt': 0.23000000417232513,\n",
" 'Wartungsarbeit': 0.4415999948978424,\n",
" 'Einrichtung': 0.2759999930858612,\n",
" 'Luftdruckkontrolle': 0.2759999930858612,\n",
" 'Abschmierung': 0.49140000343322754,\n",
" 'Ventilator': 0.49140000343322754,\n",
" 'Motor': 0.9828000068664551,\n",
" 'durchführen': 0.24570000171661377,\n",
" 'Monat': 0.4219000041484833,\n",
" 'Erledigungsdatum': 0.24570000171661377,\n",
" 'anschreiben': 0.24570000171661377,\n",
" 'Wechseln': 0.504800021648407,\n",
" 'V-Röhre': 0.2524000108242035,\n",
" 'Betriebsstunde': 0.2524000108242035,\n",
" 'Wäscherkontrolle': 0.49140000343322754,\n",
" 'Sitz': 0.15800000727176666,\n",
" 'Verschmutzung': 0.1256999969482422,\n",
" 'Sicherstellung': 0.1256999969482422,\n",
" 'Ausblasöffnung': 0.1256999969482422,\n",
" 'Fremdkörper': 0.1256999969482422,\n",
" 'anfragen': 0.45570001006126404,\n",
" 'Termin': 0.45570001006126404,\n",
" 'Menzel': 0.5950000286102295,\n",
" 'Vorbelegung': 1.5947999954223633,\n",
" 'Stehlagergehäuse': 0.4966000020503998,\n",
" 'M': 0.1177000030875206,\n",
" 'Moser': 0.2371000051498413,\n",
" 'Lagerung': 1.2455999702215195,\n",
" 'Palette': 0.6974999904632568,\n",
" 'Fach': 0.5480999797582626,\n",
" 'Hochregal': 0.5480999797582626,\n",
" 'Halle': 0.5480999797582626,\n",
" 'so': 0.5480999797582626,\n",
" 'zulässig': 0.5480999797582626,\n",
" 'tauschen': 0.22450000047683716,\n",
" 'reinigen': 0.530799999833107,\n",
" 'Rauwalze': 1.255899965763092,\n",
" 'Einziehwalze': 1.2551999688148499,\n",
" 'neu': 2.048299953341484,\n",
" 'überziehen': 0.573199987411499,\n",
" 'erfolgen': 0.11500000208616257,\n",
" 'Absprache': 0.09809999912977219,\n",
" 'Baugruppe': 0.704800009727478,\n",
" 'Pos.-Nr': 0.352400004863739,\n",
" 'Nr.': 0.5286000072956085,\n",
" 'Stückliste': 0.5286000072956085,\n",
" 'E-Nummer': 0.1762000024318695,\n",
" 'Bezeichnung': 0.1762000024318695,\n",
" 'verbauen': 0.352400004863739,\n",
" 'Hersteller': 0.1762000024318695,\n",
" 'Anzahl': 0.1762000024318695,\n",
" 'Schmierstoffmenge': 0.1762000024318695,\n",
" 'max.': 0.1762000024318695,\n",
" 'Wartungsintervall': 0.1762000024318695,\n",
" 'Wechselintervall': 0.1762000024318695,\n",
" 'Rollenkette-zweifach': 0.1762000024318695,\n",
" 'Öl': 0.1762000024318695,\n",
" 'E50': 0.1762000024318695,\n",
" 'Woche': 0.1762000024318695,\n",
" 'Kettbaum': 0.6245999932289124,\n",
" 'Gewind': 0.6347000002861023,\n",
" 'nachschneiden': 0.6347000002861023}"
]
},
"execution_count": 312,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict(tk_resc.degree(weight='weight'))"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "cafbd812-3292-4610-8fb4-0e230a3e63f4",
"metadata": {},
"outputs": [],
"source": [
"nx.set_node_attributes(tk_resc, dict(tk_resc.degree(weight='weight')), name='weight_degree')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"id": "fac462a9-4fe0-408b-9fea-9bf3b05ac7a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'weight_degree': 3.1190000474452972}\n",
"{'weight_degree': 4.145399987697601}\n",
"{'weight_degree': 2.0}\n",
"{'weight_degree': 0.8227999806404114}\n",
"{'weight_degree': 1.7093999981880188}\n",
"{'weight_degree': 2.0071999728679657}\n",
"{'weight_degree': 0.7318999767303467}\n",
"{'weight_degree': 6.2471999898552895}\n",
"{'weight_degree': 0.6929000020027161}\n",
"{'weight_degree': 0.5758000016212463}\n"
]
}
],
"source": [
"break_early = True\n",
"n = 10\n",
"\n",
"for idx, node in enumerate(tk_resc.nodes):\n",
" if break_early and idx == n:\n",
" break\n",
" print(tk_resc.nodes[node])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86699c31-5679-4baa-a52a-d7e97dd77761",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 103,
"id": "781d2906-f2cb-447a-b8b9-c82d9ae7e29f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are connected to Cytoscape!\n"
]
},
{
"data": {
"text/plain": [
"'You are connected to Cytoscape!'"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#import py4cytoscape as p4c\n",
"p4c.cytoscape_ping()"
]
},
{
"cell_type": "code",
"execution_count": 164,
"id": "2262166f-8fc6-468d-a808-1ff79ac0a70a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['lang_main']"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_collection_list()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "635ef0b3-0e22-4565-92ba-c1e50ff1c6ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.networks.delete_all_networks()"
]
},
{
"cell_type": "code",
"execution_count": 182,
"id": "2db0ecc6-15aa-49a7-baca-bee8a3faa27e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.networks.delete_network('test3')"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "986a01f1-1b98-4d6b-bf4b-83d0ef306425",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 208,
"id": "fcc82b82-1bb5-484f-9d49-75de18ebddd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.networks.delete_all_networks()"
]
},
{
"cell_type": "code",
"execution_count": 209,
"id": "28b25e27-ed77-4c23-84d5-e3dec004e4fe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying default style...\n",
"Applying preferred layout\n"
]
},
{
"data": {
"text/plain": [
"20743"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"p4c.create_network_from_networkx(tk_resc, title=BASE_NAME, collection='lang_main')"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "a4871473-83b3-44ed-a6b0-45453975cdd6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'networkTitle': 'test (undirected)',\n",
" 'nodeCount': '158',\n",
" 'edgeCount': '189',\n",
" 'avNeighbors': '2.3684210526315788',\n",
" 'diameter': '10',\n",
" 'radius': '5',\n",
" 'avSpl': '3.7965860597439547',\n",
" 'cc': '0.3375',\n",
" 'density': '0.06401137980085347',\n",
" 'heterogeneity': '1.0891156226526975',\n",
" 'centralization': '0.38888888888888895',\n",
" 'ncc': '27',\n",
" 'time': '0.003'}"
]
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.tools.analyze_network(directed=False)"
]
},
{
"cell_type": "markdown",
"id": "9c0dc5a0-0686-459b-9e98-50ab8238ecb2",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "70104956-06d4-461b-ab4d-312d868f6e98",
"metadata": {},
"outputs": [],
"source": [
"BASE_NETWORK_NAME = 'test'\n",
"\n",
"def import_to_cytoscape(graph):\n",
" p4c.networks.delete_all_networks()\n",
" p4c.create_network_from_networkx(graph, title=BASE_NETWORK_NAME, collection='lang_main')\n",
" p4c.tools.analyze_network(directed=False)\n",
"\n",
"\n",
"def reset_current_network_to_base():\n",
" p4c.set_current_network(BASE_NETWORK_NAME)\n",
"\n",
"\n",
"def export_network_to_image(filename, filetype='SVG', network_name=BASE_NETWORK_NAME):\n",
" target_folder = Path.cwd() / 'results'\n",
" if not target_folder.exists():\n",
" target_folder.mkdir(parents=True)\n",
" file_pth = target_folder / filename\n",
"\n",
" text_as_font = True\n",
" if filetype == 'SVG':\n",
" text_as_font = False\n",
"\n",
" p4c.export_image(filename=str(file_pth), type=filetype, network=network_name, overwrite_file=True, all_graphics_details=True, export_text_as_font=text_as_font, page_size='A4')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2c240f53-0f6c-4de3-adcb-7be4f051ca2a",
"metadata": {},
"outputs": [],
"source": [
"LAYOUT_NAME = 'force-directed'\n",
"LAYOUT_PROPERTIES = {\n",
" 'numIterations': 1000,\n",
" 'defaultSpringCoefficient': 1e-4,\n",
" 'defaultSpringLength': 45,\n",
" 'defaultNodeMass': 11,\n",
" 'isDeterministic': True,\n",
" 'singlePartition': False,\n",
"}\n",
"PATH_STYLESHEET = Path('lang_main.xml')\n",
"STYLESHEET_NAME = 'lang_main'\n",
"\n",
"def layout_network(layout_name=LAYOUT_NAME, layout_properties=LAYOUT_PROPERTIES, network_name=BASE_NETWORK_NAME):\n",
" p4c.set_layout_properties(layout_name, layout_properties)\n",
" p4c.layout_network(layout_name=layout_name, network=network_name)\n",
" p4c.fit_content(selected_only=False, network=network_name)\n",
"\n",
"\n",
"def apply_style_to_network(pth_to_stylesheet=PATH_STYLESHEET, network_name=BASE_NETWORK_NAME):\n",
" styles_avail = p4c.get_visual_style_names()\n",
" if STYLESHEET_NAME not in styles_avail:\n",
" p4c.import_visual_styles(pth_to_stylesheet)\n",
"\n",
" p4c.set_visual_style(STYLESHEET_NAME, network=network_name)\n",
" p4c.fit_content(selected_only=False, network=network_name)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9759f36d-761f-45fc-a9d2-157aef08c1bf",
"metadata": {},
"outputs": [],
"source": [
"SELECTION_PROPERTY = 'node_selection'\n",
"SELECTION_NUMBER = 5\n",
"ITER_NEIGHBOUR_DEPTH = 2\n",
"\n",
"def get_sub_node_selection(network_name=BASE_NETWORK_NAME):\n",
" node_table = p4c.get_table_columns(network=network_name)\n",
" node_table['stress_norm'] = node_table['Stress'] / node_table['Stress'].max()\n",
" node_table[SELECTION_PROPERTY] = node_table['weight_degree'] * node_table['BetweennessCentrality'] * node_table['stress_norm']\n",
" node_table = node_table.sort_values(by=SELECTION_PROPERTY, ascending=False)\n",
" node_table_choice = node_table.iloc[:SELECTION_NUMBER,:]\n",
"\n",
" return node_table_choice['SUID'].to_list()\n",
"\n",
"\n",
"def select_neighbours_of_node(node, network_name=BASE_NETWORK_NAME):\n",
" p4c.clear_selection(network=network_name)\n",
" p4c.select_nodes(node, network=network_name)\n",
"\n",
" for _ in range(ITER_NEIGHBOUR_DEPTH):\n",
" _ = p4c.select_first_neighbors(network=network_name)\n",
"\n",
" _ = p4c.select_edges_connecting_selected_nodes()\n",
"\n",
"\n",
"def make_subnetwork(index, network_name=BASE_NETWORK_NAME, export_image=True):\n",
" subnetwork_name = network_name + f'_sub_{index+1}'\n",
" p4c.create_subnetwork(nodes='selected', edges='selected', subnetwork_name=subnetwork_name, network=network_name)\n",
" p4c.set_current_network(subnetwork_name)\n",
" p4c.fit_content(selected_only=False, network=network_name)\n",
" if export_image:\n",
" export_network_to_image(filename=subnetwork_name, network_name=subnetwork_name)\n",
"\n",
"\n",
"def build_subnetworks(nodes_to_analyse, network_name=BASE_NETWORK_NAME, export_image=True):\n",
" for idx, node in enumerate(nodes_to_analyse):\n",
" select_neighbours_of_node(node=node, network_name=network_name)\n",
" make_subnetwork(index=idx, network_name=network_name, export_image=export_image)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c7dc0828-ea07-4bfe-b8e7-2dc97a5107db",
"metadata": {},
"outputs": [],
"source": [
"data = p4c.get_table_columns()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d7117506-7f2d-4a0d-ac19-cd55996bdfd6",
"metadata": {},
"outputs": [],
"source": [
"data['test2'] = data['degree_weighted'] * 1000"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "1e2389d4-283b-458a-a6a0-dfbf5ee1a00e",
"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>SUID</th>\n",
" <th>shared name</th>\n",
" <th>name</th>\n",
" <th>selected</th>\n",
" <th>AverageShortestPathLength</th>\n",
" <th>BetweennessCentrality</th>\n",
" <th>ClosenessCentrality</th>\n",
" <th>ClusteringCoefficient</th>\n",
" <th>Degree</th>\n",
" <th>Eccentricity</th>\n",
" <th>...</th>\n",
" <th>PartnerOfMultiEdgedNodePairs</th>\n",
" <th>Radiality</th>\n",
" <th>SelfLoops</th>\n",
" <th>Stress</th>\n",
" <th>TopologicalCoefficient</th>\n",
" <th>id</th>\n",
" <th>degree_weighted</th>\n",
" <th>row.names</th>\n",
" <th>test</th>\n",
" <th>test2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>257</td>\n",
" <td>anschreiben</td>\n",
" <td>anschreiben</td>\n",
" <td>False</td>\n",
" <td>1.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.571429</td>\n",
" <td>0.000000</td>\n",
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" <td>0.812500</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>anschreiben</td>\n",
" <td>0.2457</td>\n",
" <td>257</td>\n",
" <td>245.700002</td>\n",
" <td>245.700002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>259</th>\n",
" <td>259</td>\n",
" <td>Erledigungsdatum</td>\n",
" <td>Erledigungsdatum</td>\n",
" <td>False</td>\n",
" <td>1.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.571429</td>\n",
" <td>0.000000</td>\n",
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" <td>0</td>\n",
" <td>0.812500</td>\n",
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" <td>0.000000</td>\n",
" <td>Erledigungsdatum</td>\n",
" <td>0.2457</td>\n",
" <td>259</td>\n",
" <td>245.700002</td>\n",
" <td>245.700002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261</th>\n",
" <td>261</td>\n",
" <td>Monat</td>\n",
" <td>Monat</td>\n",
" <td>False</td>\n",
" <td>4.945946</td>\n",
" <td>0.054054</td>\n",
" <td>0.202186</td>\n",
" <td>0.000000</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
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" <td>0</td>\n",
" <td>0.753378</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>0.500000</td>\n",
" <td>Monat</td>\n",
" <td>0.4219</td>\n",
" <td>261</td>\n",
" <td>421.900004</td>\n",
" <td>421.900004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>263</td>\n",
" <td>durchführen</td>\n",
" <td>durchführen</td>\n",
" <td>False</td>\n",
" <td>5.918919</td>\n",
" <td>0.000000</td>\n",
" <td>0.168950</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
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" <td>0</td>\n",
" <td>0.692568</td>\n",
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" <td>263</td>\n",
" <td>245.700002</td>\n",
" <td>245.700002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>265</td>\n",
" <td>Motor</td>\n",
" <td>Motor</td>\n",
" <td>False</td>\n",
" <td>1.000000</td>\n",
" <td>0.833333</td>\n",
" <td>1.000000</td>\n",
" <td>0.166667</td>\n",
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" <td>982.800007</td>\n",
" <td>982.800007</td>\n",
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" <tr>\n",
" <th>247</th>\n",
" <td>247</td>\n",
" <td>Sitz</td>\n",
" <td>Sitz</td>\n",
" <td>False</td>\n",
" <td>3.000000</td>\n",
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" <td>0.333333</td>\n",
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" <td>5</td>\n",
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" <td>0</td>\n",
" <td>0.666667</td>\n",
" <td>0</td>\n",
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" <td>0.000000</td>\n",
" <td>Sitz</td>\n",
" <td>0.1580</td>\n",
" <td>247</td>\n",
" <td>158.000007</td>\n",
" <td>158.000007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>249</td>\n",
" <td>Wäscherkontrolle</td>\n",
" <td>Wäscherkontrolle</td>\n",
" <td>False</td>\n",
" <td>3.459459</td>\n",
" <td>0.000000</td>\n",
" <td>0.289062</td>\n",
" <td>1.000000</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.846284</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.666667</td>\n",
" <td>Wäscherkontrolle</td>\n",
" <td>0.4914</td>\n",
" <td>249</td>\n",
" <td>491.400003</td>\n",
" <td>491.400003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td>251</td>\n",
" <td>Betriebsstunde</td>\n",
" <td>Betriebsstunde</td>\n",
" <td>False</td>\n",
" <td>1.500000</td>\n",
" <td>0.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.750000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>Betriebsstunde</td>\n",
" <td>0.2524</td>\n",
" <td>251</td>\n",
" <td>252.400011</td>\n",
" <td>252.400011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>253</th>\n",
" <td>253</td>\n",
" <td>V-Röhre</td>\n",
" <td>V-Röhre</td>\n",
" <td>False</td>\n",
" <td>1.500000</td>\n",
" <td>0.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.750000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>V-Röhre</td>\n",
" <td>0.2524</td>\n",
" <td>253</td>\n",
" <td>252.400011</td>\n",
" <td>252.400011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>255</td>\n",
" <td>Wechseln</td>\n",
" <td>Wechseln</td>\n",
" <td>False</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0.000000</td>\n",
" <td>Wechseln</td>\n",
" <td>0.5048</td>\n",
" <td>255</td>\n",
" <td>504.800022</td>\n",
" <td>504.800022</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>158 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" SUID shared name name selected \\\n",
"257 257 anschreiben anschreiben False \n",
"259 259 Erledigungsdatum Erledigungsdatum False \n",
"261 261 Monat Monat False \n",
"263 263 durchführen durchführen False \n",
"265 265 Motor Motor False \n",
".. ... ... ... ... \n",
"247 247 Sitz Sitz False \n",
"249 249 Wäscherkontrolle Wäscherkontrolle False \n",
"251 251 Betriebsstunde Betriebsstunde False \n",
"253 253 V-Röhre V-Röhre False \n",
"255 255 Wechseln Wechseln False \n",
"\n",
" AverageShortestPathLength BetweennessCentrality ClosenessCentrality \\\n",
"257 1.750000 0.000000 0.571429 \n",
"259 1.750000 0.000000 0.571429 \n",
"261 4.945946 0.054054 0.202186 \n",
"263 5.918919 0.000000 0.168950 \n",
"265 1.000000 0.833333 1.000000 \n",
".. ... ... ... \n",
"247 3.000000 0.000000 0.333333 \n",
"249 3.459459 0.000000 0.289062 \n",
"251 1.500000 0.000000 0.666667 \n",
"253 1.500000 0.000000 0.666667 \n",
"255 1.000000 1.000000 1.000000 \n",
"\n",
" ClusteringCoefficient Degree Eccentricity ... \\\n",
"257 0.000000 1 2 ... \n",
"259 0.000000 1 2 ... \n",
"261 0.000000 2 9 ... \n",
"263 0.000000 1 10 ... \n",
"265 0.166667 4 1 ... \n",
".. ... ... ... ... \n",
"247 0.000000 1 5 ... \n",
"249 1.000000 2 7 ... \n",
"251 0.000000 1 2 ... \n",
"253 0.000000 1 2 ... \n",
"255 0.000000 2 1 ... \n",
"\n",
" PartnerOfMultiEdgedNodePairs Radiality SelfLoops Stress \\\n",
"257 0 0.812500 0 0 \n",
"259 0 0.812500 0 0 \n",
"261 0 0.753378 0 72 \n",
"263 0 0.692568 0 0 \n",
"265 0 1.000000 0 10 \n",
".. ... ... ... ... \n",
"247 0 0.666667 0 0 \n",
"249 0 0.846284 0 0 \n",
"251 0 0.750000 0 0 \n",
"253 0 0.750000 0 0 \n",
"255 0 1.000000 0 2 \n",
"\n",
" TopologicalCoefficient id degree_weighted row.names \\\n",
"257 0.000000 anschreiben 0.2457 257 \n",
"259 0.000000 Erledigungsdatum 0.2457 259 \n",
"261 0.500000 Monat 0.4219 261 \n",
"263 0.000000 durchführen 0.2457 263 \n",
"265 0.500000 Motor 0.9828 265 \n",
".. ... ... ... ... \n",
"247 0.000000 Sitz 0.1580 247 \n",
"249 0.666667 Wäscherkontrolle 0.4914 249 \n",
"251 0.000000 Betriebsstunde 0.2524 251 \n",
"253 0.000000 V-Röhre 0.2524 253 \n",
"255 0.000000 Wechseln 0.5048 255 \n",
"\n",
" test test2 \n",
"257 245.700002 245.700002 \n",
"259 245.700002 245.700002 \n",
"261 421.900004 421.900004 \n",
"263 245.700002 245.700002 \n",
"265 982.800007 982.800007 \n",
".. ... ... \n",
"247 158.000007 158.000007 \n",
"249 491.400003 491.400003 \n",
"251 252.400011 252.400011 \n",
"253 252.400011 252.400011 \n",
"255 504.800022 504.800022 \n",
"\n",
"[158 rows x 24 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "facae316-6acb-4094-9eef-19bead44a813",
"metadata": {},
"source": [
"---\n",
"\n",
"1. import network\n",
"2. layouting\n",
"3. apply styles\n",
"4. export image\n",
"5. build subgraphs\n",
" 1. get candidates\n",
" 2. build subnetwork\n",
" 3. export subnetwork"
]
},
{
"cell_type": "code",
"execution_count": 305,
"id": "583d304d-571f-43f5-b8eb-905a01ddaec4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying default style...\n",
"Applying preferred layout\n"
]
}
],
"source": [
"import_to_cytoscape(tk_resc)"
]
},
{
"cell_type": "code",
"execution_count": 306,
"id": "b13f2eb2-fa1e-495c-8e77-e992f78a69b6",
"metadata": {},
"outputs": [],
"source": [
"layout_network()"
]
},
{
"cell_type": "code",
"execution_count": 307,
"id": "60c1ca0f-ac73-4545-bffa-7eb656ade8fa",
"metadata": {},
"outputs": [],
"source": [
"apply_style_to_network()"
]
},
{
"cell_type": "code",
"execution_count": 308,
"id": "8cc5f1c0-0bb4-4e77-ba70-83ef449f37c8",
"metadata": {},
"outputs": [],
"source": [
"export_network_to_image(filename=BASE_NETWORK_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "040c4439-3c46-4d48-92ed-af9614f90cb0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 309,
"id": "da0f7a3f-c7e5-4c50-85d9-16a90ff69011",
"metadata": {},
"outputs": [],
"source": [
"nodes_to_analyse = get_sub_node_selection()"
]
},
{
"cell_type": "code",
"execution_count": 310,
"id": "27a9cbc3-d852-4f5d-a6ee-92f97cf10db1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No nodes selected.\n",
"No nodes selected.\n",
"No nodes selected.\n",
"No nodes selected.\n"
]
}
],
"source": [
"build_subnetworks(nodes_to_analyse=nodes_to_analyse, export_image=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39a61348-f2b6-4bf8-bc71-5d77e38bef47",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 40,
"id": "fae73cc5-ac29-463d-8ea3-87a1c8072932",
"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>SUID</th>\n",
" <th>shared name</th>\n",
" <th>name</th>\n",
" <th>selected</th>\n",
" <th>id</th>\n",
" <th>degree_weighted</th>\n",
" <th>AverageShortestPathLength</th>\n",
" <th>ClusteringCoefficient</th>\n",
" <th>ClosenessCentrality</th>\n",
" <th>IsSingleNode</th>\n",
" <th>...</th>\n",
" <th>Stress</th>\n",
" <th>Degree</th>\n",
" <th>BetweennessCentrality</th>\n",
" <th>NeighborhoodConnectivity</th>\n",
" <th>NumberOfDirectedEdges</th>\n",
" <th>NumberOfUndirectedEdges</th>\n",
" <th>Radiality</th>\n",
" <th>TopologicalCoefficient</th>\n",
" <th>stress_norm</th>\n",
" <th>node_selection</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16385</th>\n",
" <td>16385</td>\n",
" <td>Fremdkörper</td>\n",
" <td>Fremdkörper</td>\n",
" <td>False</td>\n",
" <td>Fremdkörper</td>\n",
" <td>0.1257</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16130</th>\n",
" <td>16130</td>\n",
" <td>Aschenbecher</td>\n",
" <td>Aschenbecher</td>\n",
" <td>False</td>\n",
" <td>Aschenbecher</td>\n",
" <td>0.7114</td>\n",
" <td>1.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.75</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.666667</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0.833333</td>\n",
" <td>0.5</td>\n",
" <td>0.002275</td>\n",
" <td>0.001079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16388</th>\n",
" <td>16388</td>\n",
" <td>anfragen</td>\n",
" <td>anfragen</td>\n",
" <td>False</td>\n",
" <td>anfragen</td>\n",
" <td>0.4557</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16133</th>\n",
" <td>16133</td>\n",
" <td>leeren</td>\n",
" <td>leeren</td>\n",
" <td>False</td>\n",
" <td>leeren</td>\n",
" <td>0.3557</td>\n",
" <td>2.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.50</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16391</th>\n",
" <td>16391</td>\n",
" <td>Termin</td>\n",
" <td>Termin</td>\n",
" <td>False</td>\n",
" <td>Termin</td>\n",
" <td>0.4557</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16121</th>\n",
" <td>16121</td>\n",
" <td>Wasserverbrauch</td>\n",
" <td>Wasserverbrauch</td>\n",
" <td>False</td>\n",
" <td>Wasserverbrauch</td>\n",
" <td>0.4298</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16379</th>\n",
" <td>16379</td>\n",
" <td>Sicherstellung</td>\n",
" <td>Sicherstellung</td>\n",
" <td>False</td>\n",
" <td>Sicherstellung</td>\n",
" <td>0.1257</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16124</th>\n",
" <td>16124</td>\n",
" <td>auffüllen</td>\n",
" <td>auffüllen</td>\n",
" <td>False</td>\n",
" <td>auffüllen</td>\n",
" <td>0.7114</td>\n",
" <td>1.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.75</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.666667</td>\n",
" <td>1.5</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0.833333</td>\n",
" <td>0.5</td>\n",
" <td>0.002275</td>\n",
" <td>0.001079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16382</th>\n",
" <td>16382</td>\n",
" <td>Ausblasöffnung</td>\n",
" <td>Ausblasöffnung</td>\n",
" <td>False</td>\n",
" <td>Ausblasöffnung</td>\n",
" <td>0.1257</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16127</th>\n",
" <td>16127</td>\n",
" <td>Desifektionsmittel</td>\n",
" <td>Desifektionsmittel</td>\n",
" <td>False</td>\n",
" <td>Desifektionsmittel</td>\n",
" <td>0.3557</td>\n",
" <td>2.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.50</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>158 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" SUID shared name name selected \\\n",
"16385 16385 Fremdkörper Fremdkörper False \n",
"16130 16130 Aschenbecher Aschenbecher False \n",
"16388 16388 anfragen anfragen False \n",
"16133 16133 leeren leeren False \n",
"16391 16391 Termin Termin False \n",
"... ... ... ... ... \n",
"16121 16121 Wasserverbrauch Wasserverbrauch False \n",
"16379 16379 Sicherstellung Sicherstellung False \n",
"16124 16124 auffüllen auffüllen False \n",
"16382 16382 Ausblasöffnung Ausblasöffnung False \n",
"16127 16127 Desifektionsmittel Desifektionsmittel False \n",
"\n",
" id degree_weighted AverageShortestPathLength \\\n",
"16385 Fremdkörper 0.1257 1.000000 \n",
"16130 Aschenbecher 0.7114 1.333333 \n",
"16388 anfragen 0.4557 1.000000 \n",
"16133 leeren 0.3557 2.000000 \n",
"16391 Termin 0.4557 1.000000 \n",
"... ... ... ... \n",
"16121 Wasserverbrauch 0.4298 1.000000 \n",
"16379 Sicherstellung 0.1257 1.000000 \n",
"16124 auffüllen 0.7114 1.333333 \n",
"16382 Ausblasöffnung 0.1257 1.000000 \n",
"16127 Desifektionsmittel 0.3557 2.000000 \n",
"\n",
" ClusteringCoefficient ClosenessCentrality IsSingleNode ... Stress \\\n",
"16385 0.0 1.00 False ... 0 \n",
"16130 0.0 0.75 False ... 4 \n",
"16388 0.0 1.00 False ... 0 \n",
"16133 0.0 0.50 False ... 0 \n",
"16391 0.0 1.00 False ... 0 \n",
"... ... ... ... ... ... \n",
"16121 0.0 1.00 False ... 0 \n",
"16379 0.0 1.00 False ... 0 \n",
"16124 0.0 0.75 False ... 4 \n",
"16382 0.0 1.00 False ... 0 \n",
"16127 0.0 0.50 False ... 0 \n",
"\n",
" Degree BetweennessCentrality NeighborhoodConnectivity \\\n",
"16385 1 0.000000 1.0 \n",
"16130 2 0.666667 1.5 \n",
"16388 1 0.000000 1.0 \n",
"16133 1 0.000000 2.0 \n",
"16391 1 0.000000 1.0 \n",
"... ... ... ... \n",
"16121 1 0.000000 1.0 \n",
"16379 1 0.000000 1.0 \n",
"16124 2 0.666667 1.5 \n",
"16382 1 0.000000 1.0 \n",
"16127 1 0.000000 2.0 \n",
"\n",
" NumberOfDirectedEdges NumberOfUndirectedEdges Radiality \\\n",
"16385 0 1 1.000000 \n",
"16130 0 2 0.833333 \n",
"16388 0 1 1.000000 \n",
"16133 0 1 0.500000 \n",
"16391 0 1 1.000000 \n",
"... ... ... ... \n",
"16121 0 1 1.000000 \n",
"16379 0 1 1.000000 \n",
"16124 0 2 0.833333 \n",
"16382 0 1 1.000000 \n",
"16127 0 1 0.500000 \n",
"\n",
" TopologicalCoefficient stress_norm node_selection \n",
"16385 0.0 0.000000 0.000000 \n",
"16130 0.5 0.002275 0.001079 \n",
"16388 0.0 0.000000 0.000000 \n",
"16133 0.0 0.000000 0.000000 \n",
"16391 0.0 0.000000 0.000000 \n",
"... ... ... ... \n",
"16121 0.0 0.000000 0.000000 \n",
"16379 0.0 0.000000 0.000000 \n",
"16124 0.5 0.002275 0.001079 \n",
"16382 0.0 0.000000 0.000000 \n",
"16127 0.0 0.000000 0.000000 \n",
"\n",
"[158 rows x 23 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = p4c.get_table_columns()\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "4b1e0799-59ac-431d-9e3f-8752bb0a4be5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"min_val=0.0, max_val=3.008924891341149\n"
]
}
],
"source": [
"min_val = data['node_selection'].min()\n",
"max_val = data['node_selection'].max()\n",
"print(f'{min_val=}, {max_val=}')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "6d2e939c-7218-4449-a0de-8679f57f43b2",
"metadata": {},
"outputs": [],
"source": [
"scheme = p4c.scheme_c_number_continuous(start_value=15, end_value=40)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "17f6c3f1-4584-47ed-b563-2e71b6850184",
"metadata": {},
"outputs": [],
"source": [
"node_size_map = p4c.gen_node_size_map('node_selection', number_scheme=scheme, mapping_type='c', style_name='lang_main', default_number=18)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "842b04be-f434-4530-afac-22c892e1fc83",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'table_column': 'node_selection',\n",
" 'table_column_values': [0.0, 1.5044624456705744, 3.008924891341149],\n",
" 'sizes': [15, 27.5, 40],\n",
" 'mapping_type': 'c',\n",
" 'default_size': 18,\n",
" 'style_name': 'lang_main',\n",
" 'network': None,\n",
" 'base_url': 'http://127.0.0.1:1234/v1'}"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"node_size_map"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "34dd3ada-c65b-4914-a918-e1798ef4e88b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.set_node_size_mapping(**node_size_map)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5b6c04b-dfe1-4cfc-bf0a-8251a9367a81",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"id": "74749a78-1afb-4158-8ab4-05b18b59e39e",
"metadata": {},
"outputs": [],
"source": [
"test = dict()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9ec4f934-8ea5-401f-9f2f-86de478cec01",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jo\n"
]
}
],
"source": [
"if not test:\n",
" print('jo')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "97905d05-483c-4e9a-b88d-00353ada870b",
"metadata": {},
"outputs": [],
"source": [
"from lang_main.render.cytoscape import layout_network"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "900868b3-7e3a-44c7-bef4-53c5a3e63e63",
"metadata": {},
"outputs": [],
"source": [
"layout_network()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3369d18-0e29-4dc3-a7ba-bab8b356282f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 296,
"id": "1dbbf2a3-6de3-4557-8966-40f12c55d755",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"41497"
]
},
"execution_count": 296,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IDX = 0\n",
"nodes_to_select[IDX]"
]
},
{
"cell_type": "code",
"execution_count": 297,
"id": "3b3eebe1-90e8-429d-8099-96f6e59f7e18",
"metadata": {},
"outputs": [],
"source": [
"select_neighbours_of_node(nodes_to_select[IDX])"
]
},
{
"cell_type": "code",
"execution_count": 300,
"id": "c1b46f42-5837-4a77-9d7c-5780bd5057be",
"metadata": {},
"outputs": [],
"source": [
"build_subnetwork(IDX)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0558b387-e1c4-43b8-aa3c-4d3a182856f4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f215f7a-2cec-4ea8-8fc7-6b217fb3df7f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "de481ea5-db5d-46ba-9eed-e7da476d895f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "261bf119-4757-4840-a0b9-0d3d4fb77dd9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b5dfc7f-44fd-4f7b-a8a8-0cd3c73449b4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 190,
"id": "2ce067f3-9647-489d-934e-fcdf1a2561f1",
"metadata": {},
"outputs": [],
"source": [
"node_table = p4c.get_table_columns(network=BASE_NAME)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"id": "f4490242-0aac-46af-a913-08a627815587",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(node_table)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"id": "9bf840a7-e533-42e2-936a-2678f6bfc4ca",
"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>SUID</th>\n",
" <th>shared name</th>\n",
" <th>name</th>\n",
" <th>selected</th>\n",
" <th>id</th>\n",
" <th>weight_degree</th>\n",
" <th>AverageShortestPathLength</th>\n",
" <th>ClusteringCoefficient</th>\n",
" <th>ClosenessCentrality</th>\n",
" <th>IsSingleNode</th>\n",
" <th>...</th>\n",
" <th>SelfLoops</th>\n",
" <th>Eccentricity</th>\n",
" <th>Stress</th>\n",
" <th>Degree</th>\n",
" <th>BetweennessCentrality</th>\n",
" <th>NeighborhoodConnectivity</th>\n",
" <th>NumberOfDirectedEdges</th>\n",
" <th>NumberOfUndirectedEdges</th>\n",
" <th>Radiality</th>\n",
" <th>TopologicalCoefficient</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18610</th>\n",
" <td>18610</td>\n",
" <td>Kontrolle</td>\n",
" <td>Kontrolle</td>\n",
" <td>False</td>\n",
" <td>Kontrolle</td>\n",
" <td>6.2472</td>\n",
" <td>2.270270</td>\n",
" <td>0.025000</td>\n",
" <td>0.440476</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1062</td>\n",
" <td>16</td>\n",
" <td>0.797297</td>\n",
" <td>1.812500</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0.920608</td>\n",
" <td>0.091346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18778</th>\n",
" <td>18778</td>\n",
" <td>Herr</td>\n",
" <td>Herr</td>\n",
" <td>False</td>\n",
" <td>Herr</td>\n",
" <td>5.4802</td>\n",
" <td>3.114286</td>\n",
" <td>0.294872</td>\n",
" <td>0.321101</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>962</td>\n",
" <td>15</td>\n",
" <td>0.402857</td>\n",
" <td>4.692308</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0.837363</td>\n",
" <td>0.329670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18799</th>\n",
" <td>18799</td>\n",
" <td>Firma</td>\n",
" <td>Firma</td>\n",
" <td>False</td>\n",
" <td>Firma</td>\n",
" <td>3.6372</td>\n",
" <td>3.571429</td>\n",
" <td>0.127273</td>\n",
" <td>0.280000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1328</td>\n",
" <td>11</td>\n",
" <td>0.401681</td>\n",
" <td>2.818182</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0.802198</td>\n",
" <td>0.223140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18694</th>\n",
" <td>18694</td>\n",
" <td>sehen</td>\n",
" <td>sehen</td>\n",
" <td>False</td>\n",
" <td>sehen</td>\n",
" <td>3.6508</td>\n",
" <td>3.114286</td>\n",
" <td>0.333333</td>\n",
" <td>0.321101</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1034</td>\n",
" <td>7</td>\n",
" <td>0.281793</td>\n",
" <td>4.571429</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.837363</td>\n",
" <td>0.268908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18712</th>\n",
" <td>18712</td>\n",
" <td>schmieren</td>\n",
" <td>schmieren</td>\n",
" <td>False</td>\n",
" <td>schmieren</td>\n",
" <td>1.4378</td>\n",
" <td>2.621622</td>\n",
" <td>0.066667</td>\n",
" <td>0.381443</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>626</td>\n",
" <td>6</td>\n",
" <td>0.469970</td>\n",
" <td>4.333333</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.898649</td>\n",
" <td>0.183333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19048</th>\n",
" <td>19048</td>\n",
" <td>E50</td>\n",
" <td>E50</td>\n",
" <td>False</td>\n",
" <td>E50</td>\n",
" <td>0.1762</td>\n",
" <td>4.243243</td>\n",
" <td>0.000000</td>\n",
" <td>0.235669</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.797297</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19045</th>\n",
" <td>19045</td>\n",
" <td>Öl</td>\n",
" <td>Öl</td>\n",
" <td>False</td>\n",
" <td>Öl</td>\n",
" <td>0.1762</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19042</th>\n",
" <td>19042</td>\n",
" <td>Rollenkette-zweifach</td>\n",
" <td>Rollenkette-zweifach</td>\n",
" <td>False</td>\n",
" <td>Rollenkette-zweifach</td>\n",
" <td>0.1762</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19039</th>\n",
" <td>19039</td>\n",
" <td>Wechselintervall</td>\n",
" <td>Wechselintervall</td>\n",
" <td>False</td>\n",
" <td>Wechselintervall</td>\n",
" <td>0.1762</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18943</th>\n",
" <td>18943</td>\n",
" <td>Menzel</td>\n",
" <td>Menzel</td>\n",
" <td>False</td>\n",
" <td>Menzel</td>\n",
" <td>0.5950</td>\n",
" <td>4.542857</td>\n",
" <td>0.000000</td>\n",
" <td>0.220126</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>11.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.727473</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>158 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" SUID shared name name selected \\\n",
"18610 18610 Kontrolle Kontrolle False \n",
"18778 18778 Herr Herr False \n",
"18799 18799 Firma Firma False \n",
"18694 18694 sehen sehen False \n",
"18712 18712 schmieren schmieren False \n",
"... ... ... ... ... \n",
"19048 19048 E50 E50 False \n",
"19045 19045 Öl Öl False \n",
"19042 19042 Rollenkette-zweifach Rollenkette-zweifach False \n",
"19039 19039 Wechselintervall Wechselintervall False \n",
"18943 18943 Menzel Menzel False \n",
"\n",
" id weight_degree AverageShortestPathLength \\\n",
"18610 Kontrolle 6.2472 2.270270 \n",
"18778 Herr 5.4802 3.114286 \n",
"18799 Firma 3.6372 3.571429 \n",
"18694 sehen 3.6508 3.114286 \n",
"18712 schmieren 1.4378 2.621622 \n",
"... ... ... ... \n",
"19048 E50 0.1762 4.243243 \n",
"19045 Öl 0.1762 1.000000 \n",
"19042 Rollenkette-zweifach 0.1762 1.000000 \n",
"19039 Wechselintervall 0.1762 1.000000 \n",
"18943 Menzel 0.5950 4.542857 \n",
"\n",
" ClusteringCoefficient ClosenessCentrality IsSingleNode ... \\\n",
"18610 0.025000 0.440476 False ... \n",
"18778 0.294872 0.321101 False ... \n",
"18799 0.127273 0.280000 False ... \n",
"18694 0.333333 0.321101 False ... \n",
"18712 0.066667 0.381443 False ... \n",
"... ... ... ... ... \n",
"19048 0.000000 0.235669 False ... \n",
"19045 0.000000 1.000000 False ... \n",
"19042 0.000000 1.000000 False ... \n",
"19039 0.000000 1.000000 False ... \n",
"18943 0.000000 0.220126 False ... \n",
"\n",
" SelfLoops Eccentricity Stress Degree BetweennessCentrality \\\n",
"18610 0 5 1062 16 0.797297 \n",
"18778 1 6 962 15 0.402857 \n",
"18799 0 7 1328 11 0.401681 \n",
"18694 0 6 1034 7 0.281793 \n",
"18712 0 6 626 6 0.469970 \n",
"... ... ... ... ... ... \n",
"19048 0 8 0 1 0.000000 \n",
"19045 0 1 0 1 0.000000 \n",
"19042 0 1 0 1 0.000000 \n",
"19039 0 1 0 1 0.000000 \n",
"18943 0 8 0 1 0.000000 \n",
"\n",
" NeighborhoodConnectivity NumberOfDirectedEdges \\\n",
"18610 1.812500 0 \n",
"18778 4.692308 0 \n",
"18799 2.818182 0 \n",
"18694 4.571429 0 \n",
"18712 4.333333 0 \n",
"... ... ... \n",
"19048 4.000000 0 \n",
"19045 1.000000 0 \n",
"19042 1.000000 0 \n",
"19039 1.000000 0 \n",
"18943 11.000000 0 \n",
"\n",
" NumberOfUndirectedEdges Radiality TopologicalCoefficient \n",
"18610 16 0.920608 0.091346 \n",
"18778 14 0.837363 0.329670 \n",
"18799 11 0.802198 0.223140 \n",
"18694 7 0.837363 0.268908 \n",
"18712 6 0.898649 0.183333 \n",
"... ... ... ... \n",
"19048 1 0.797297 0.000000 \n",
"19045 1 1.000000 0.000000 \n",
"19042 1 1.000000 0.000000 \n",
"19039 1 1.000000 0.000000 \n",
"18943 1 0.727473 0.000000 \n",
"\n",
"[158 rows x 21 columns]"
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"node_table.sort_values(by='Degree', ascending=False)"
]
},
{
"cell_type": "markdown",
"id": "b4ebe14c-2b23-4da2-8be6-2223add935af",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 193,
"id": "7386bd66-1924-4199-84d2-a6d1a66def40",
"metadata": {},
"outputs": [],
"source": [
"node_table['stress_norm'] = node_table['Stress'] / node_table['Stress'].max()"
]
},
{
"cell_type": "code",
"execution_count": 194,
"id": "9422c085-e30e-4418-8c74-26d89edf0ad4",
"metadata": {},
"outputs": [],
"source": [
"node_table['w_deg with betweenness'] = node_table['weight_degree'] * node_table['BetweennessCentrality'] * node_table['stress_norm']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9795c94-cb76-452e-9eda-2285d050fd1b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 195,
"id": "a2fb09ff-bf2e-48d1-9ceb-777efe0b0b40",
"metadata": {},
"outputs": [],
"source": [
"node_table_sorted = node_table.sort_values(by='w_deg with betweenness', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 196,
"id": "6ae4b7b9-75db-4b80-bd46-53ea25b06829",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>SUID</th>\n",
" <th>shared name</th>\n",
" <th>name</th>\n",
" <th>selected</th>\n",
" <th>id</th>\n",
" <th>weight_degree</th>\n",
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" <th>ClusteringCoefficient</th>\n",
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" <th>Degree</th>\n",
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" <th>NeighborhoodConnectivity</th>\n",
" <th>NumberOfDirectedEdges</th>\n",
" <th>NumberOfUndirectedEdges</th>\n",
" <th>Radiality</th>\n",
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" <th>w_deg with betweenness</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>18610</th>\n",
" <td>18610</td>\n",
" <td>Kontrolle</td>\n",
" <td>Kontrolle</td>\n",
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" <td>Kontrolle</td>\n",
" <td>6.2472</td>\n",
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" <td>1.812500</td>\n",
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" <td>16</td>\n",
" <td>0.920608</td>\n",
" <td>0.091346</td>\n",
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" <td>3.008925</td>\n",
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" <tr>\n",
" <th>18585</th>\n",
" <td>18585</td>\n",
" <td>Wartungstätigkeit</td>\n",
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" <td>Wartungstätigkeit</td>\n",
" <td>3.1190</td>\n",
" <td>2.714286</td>\n",
" <td>0.133333</td>\n",
" <td>0.368421</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>1758</td>\n",
" <td>6</td>\n",
" <td>0.571429</td>\n",
" <td>3.000000</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.868132</td>\n",
" <td>0.242424</td>\n",
" <td>1.000000</td>\n",
" <td>1.782286</td>\n",
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" <tr>\n",
" <th>18778</th>\n",
" <td>18778</td>\n",
" <td>Herr</td>\n",
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" <td>False</td>\n",
" <td>Herr</td>\n",
" <td>5.4802</td>\n",
" <td>3.114286</td>\n",
" <td>0.294872</td>\n",
" <td>0.321101</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>962</td>\n",
" <td>15</td>\n",
" <td>0.402857</td>\n",
" <td>4.692308</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0.837363</td>\n",
" <td>0.329670</td>\n",
" <td>0.547213</td>\n",
" <td>1.208102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18799</th>\n",
" <td>18799</td>\n",
" <td>Firma</td>\n",
" <td>Firma</td>\n",
" <td>False</td>\n",
" <td>Firma</td>\n",
" <td>3.6372</td>\n",
" <td>3.571429</td>\n",
" <td>0.127273</td>\n",
" <td>0.280000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>1328</td>\n",
" <td>11</td>\n",
" <td>0.401681</td>\n",
" <td>2.818182</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0.802198</td>\n",
" <td>0.223140</td>\n",
" <td>0.755404</td>\n",
" <td>1.103640</td>\n",
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" <th>18592</th>\n",
" <td>18592</td>\n",
" <td>Vorgabe</td>\n",
" <td>Vorgabe</td>\n",
" <td>False</td>\n",
" <td>Vorgabe</td>\n",
" <td>4.1454</td>\n",
" <td>2.885714</td>\n",
" <td>0.400000</td>\n",
" <td>0.346535</td>\n",
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" <td>...</td>\n",
" <td>1106</td>\n",
" <td>5</td>\n",
" <td>0.315406</td>\n",
" <td>4.600000</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0.854945</td>\n",
" <td>0.383333</td>\n",
" <td>0.629124</td>\n",
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"</div>"
],
"text/plain": [
" SUID shared name name selected \\\n",
"18610 18610 Kontrolle Kontrolle False \n",
"18585 18585 Wartungstätigkeit Wartungstätigkeit False \n",
"18778 18778 Herr Herr False \n",
"18799 18799 Firma Firma False \n",
"18592 18592 Vorgabe Vorgabe False \n",
"\n",
" id weight_degree AverageShortestPathLength \\\n",
"18610 Kontrolle 6.2472 2.270270 \n",
"18585 Wartungstätigkeit 3.1190 2.714286 \n",
"18778 Herr 5.4802 3.114286 \n",
"18799 Firma 3.6372 3.571429 \n",
"18592 Vorgabe 4.1454 2.885714 \n",
"\n",
" ClusteringCoefficient ClosenessCentrality IsSingleNode ... Stress \\\n",
"18610 0.025000 0.440476 False ... 1062 \n",
"18585 0.133333 0.368421 False ... 1758 \n",
"18778 0.294872 0.321101 False ... 962 \n",
"18799 0.127273 0.280000 False ... 1328 \n",
"18592 0.400000 0.346535 False ... 1106 \n",
"\n",
" Degree BetweennessCentrality NeighborhoodConnectivity \\\n",
"18610 16 0.797297 1.812500 \n",
"18585 6 0.571429 3.000000 \n",
"18778 15 0.402857 4.692308 \n",
"18799 11 0.401681 2.818182 \n",
"18592 5 0.315406 4.600000 \n",
"\n",
" NumberOfDirectedEdges NumberOfUndirectedEdges Radiality \\\n",
"18610 0 16 0.920608 \n",
"18585 0 6 0.868132 \n",
"18778 0 14 0.837363 \n",
"18799 0 11 0.802198 \n",
"18592 0 5 0.854945 \n",
"\n",
" TopologicalCoefficient stress_norm w_deg with betweenness \n",
"18610 0.091346 0.604096 3.008925 \n",
"18585 0.242424 1.000000 1.782286 \n",
"18778 0.329670 0.547213 1.208102 \n",
"18799 0.223140 0.755404 1.103640 \n",
"18592 0.383333 0.629124 0.822570 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"node_table_sorted.head()"
]
},
{
"cell_type": "code",
"execution_count": 197,
"id": "a5f22fd0-e002-43ec-8dc3-99eb52f8bcd2",
"metadata": {},
"outputs": [],
"source": [
"node_table_choice = node_table_sorted.iloc[:5,:]"
]
},
{
"cell_type": "code",
"execution_count": 198,
"id": "c123cdd7-f000-4a60-9dc2-73b8a4410e31",
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>18585</th>\n",
" <td>18585</td>\n",
" <td>Wartungstätigkeit</td>\n",
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" <td>False</td>\n",
" <td>Wartungstätigkeit</td>\n",
" <td>3.1190</td>\n",
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" <td>0.133333</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18778</th>\n",
" <td>18778</td>\n",
" <td>Herr</td>\n",
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" <td>False</td>\n",
" <td>Herr</td>\n",
" <td>5.4802</td>\n",
" <td>3.114286</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18799</th>\n",
" <td>18799</td>\n",
" <td>Firma</td>\n",
" <td>Firma</td>\n",
" <td>False</td>\n",
" <td>Firma</td>\n",
" <td>3.6372</td>\n",
" <td>3.571429</td>\n",
" <td>0.127273</td>\n",
" <td>0.280000</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>1328</td>\n",
" <td>11</td>\n",
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" <td>0.755404</td>\n",
" <td>1.103640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18592</th>\n",
" <td>18592</td>\n",
" <td>Vorgabe</td>\n",
" <td>Vorgabe</td>\n",
" <td>False</td>\n",
" <td>Vorgabe</td>\n",
" <td>4.1454</td>\n",
" <td>2.885714</td>\n",
" <td>0.400000</td>\n",
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" <td>1106</td>\n",
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"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" SUID shared name name selected \\\n",
"18610 18610 Kontrolle Kontrolle False \n",
"18585 18585 Wartungstätigkeit Wartungstätigkeit False \n",
"18778 18778 Herr Herr False \n",
"18799 18799 Firma Firma False \n",
"18592 18592 Vorgabe Vorgabe False \n",
"\n",
" id weight_degree AverageShortestPathLength \\\n",
"18610 Kontrolle 6.2472 2.270270 \n",
"18585 Wartungstätigkeit 3.1190 2.714286 \n",
"18778 Herr 5.4802 3.114286 \n",
"18799 Firma 3.6372 3.571429 \n",
"18592 Vorgabe 4.1454 2.885714 \n",
"\n",
" ClusteringCoefficient ClosenessCentrality IsSingleNode ... Stress \\\n",
"18610 0.025000 0.440476 False ... 1062 \n",
"18585 0.133333 0.368421 False ... 1758 \n",
"18778 0.294872 0.321101 False ... 962 \n",
"18799 0.127273 0.280000 False ... 1328 \n",
"18592 0.400000 0.346535 False ... 1106 \n",
"\n",
" Degree BetweennessCentrality NeighborhoodConnectivity \\\n",
"18610 16 0.797297 1.812500 \n",
"18585 6 0.571429 3.000000 \n",
"18778 15 0.402857 4.692308 \n",
"18799 11 0.401681 2.818182 \n",
"18592 5 0.315406 4.600000 \n",
"\n",
" NumberOfDirectedEdges NumberOfUndirectedEdges Radiality \\\n",
"18610 0 16 0.920608 \n",
"18585 0 6 0.868132 \n",
"18778 0 14 0.837363 \n",
"18799 0 11 0.802198 \n",
"18592 0 5 0.854945 \n",
"\n",
" TopologicalCoefficient stress_norm w_deg with betweenness \n",
"18610 0.091346 0.604096 3.008925 \n",
"18585 0.242424 1.000000 1.782286 \n",
"18778 0.329670 0.547213 1.208102 \n",
"18799 0.223140 0.755404 1.103640 \n",
"18592 0.383333 0.629124 0.822570 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"node_table_choice"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "6f68b890-6c80-4750-a35c-ee29b594462d",
"metadata": {},
"outputs": [],
"source": [
"nodes_to_select = node_table_choice['SUID'].to_list()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"id": "6d94b18f-2590-4d1b-b165-300466bfd1b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.clear_selection()"
]
},
{
"cell_type": "code",
"execution_count": 239,
"id": "62cf521b-e709-4086-9f3d-3e3f8dd9faaa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{}"
]
},
"execution_count": 239,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.select_nodes(nodes_to_select[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdeeccd4-3e7c-4c76-a024-f45540142c4a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 202,
"id": "a371ce7a-d9b9-4158-a0cb-797834a16f97",
"metadata": {},
"outputs": [],
"source": [
"iter_depth = 2\n",
"\n",
"for _ in range(iter_depth):\n",
" _ = p4c.select_first_neighbors()"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "cce979e2-65f1-4896-8758-659ff21c315b",
"metadata": {},
"outputs": [],
"source": [
"_ = p4c.select_edges_connecting_selected_nodes()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23157e10-02c6-4adb-8a36-4bdbfe5f9f4b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 206,
"id": "2038fd62-b75f-4ba1-b258-53789b4665b7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20402"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.create_subnetwork(nodes='selected', edges='selected', subnetwork_name='test_sub_1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12828271-fea9-4e4a-98c3-87860992a6db",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f367b253-2172-4dce-9379-a3ed95f3368e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "49ad6a55-2a12-44c4-a193-2fbb3da84bcf",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 205,
"id": "7de95550-c077-467f-a962-5e84ddf430c7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{}"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.fit_content(selected_only=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b73934d1-f16a-4ef9-8969-b1be8e7310f5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "783cb7d6-c6b1-4a9b-8019-02ce2628dde9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 314,
"id": "697647da-3b8b-4029-bec9-af2e2bb6984a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['attribute-circle',\n",
" 'attribute-grid',\n",
" 'attributes-layout',\n",
" 'circular',\n",
" 'cose',\n",
" 'degree-circle',\n",
" 'force-directed',\n",
" 'force-directed-cl',\n",
" 'fruchterman-rheingold',\n",
" 'grid',\n",
" 'hierarchical',\n",
" 'isom',\n",
" 'kamada-kawai',\n",
" 'stacked-node-layout']"
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(list(p4c.get_layout_name_mapping().values()))"
]
},
{
"cell_type": "code",
"execution_count": 186,
"id": "fa15c057-d453-4c7e-8af2-18220ea90651",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['numIterations',\n",
" 'defaultSpringCoefficient',\n",
" 'defaultSpringLength',\n",
" 'defaultNodeMass',\n",
" 'isDeterministic',\n",
" 'singlePartition']"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_layout_property_names('force-directed')"
]
},
{
"cell_type": "code",
"execution_count": 259,
"id": "6f48d9cc-f527-4b33-9477-60636a480371",
"metadata": {},
"outputs": [],
"source": [
"LAYOUT_NAME = 'force-directed'\n",
"LAYOUT_PROPERTIES = {\n",
" 'numIterations': 1000,\n",
" 'defaultSpringCoefficient': 1e-4,\n",
" 'defaultSpringLength': 45,\n",
" 'defaultNodeMass': 11,\n",
" 'isDeterministic': True,\n",
" 'singlePartition': False,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 188,
"id": "d542b2ea-11d5-4802-b1b9-65e5e12f6d38",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.set_layout_properties('force-directed', layout_props)\n",
"#p4c.get_layout_property_type('kamada-kawai', 'randomize')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8eb62660-27a6-43a0-be19-4c4479eac8d7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 189,
"id": "c1069996-b638-4bfa-8ad4-f750a33022d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{}"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.layout_network(layout_name='force-directed', network='test3')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7f5c1e6-8e32-4e90-b531-38611fef85ce",
"metadata": {},
"outputs": [],
"source": [
"p4c.fit_content(selected_only=False)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "8802c969-c3f4-433a-8cab-c6704fa03039",
"metadata": {},
"outputs": [],
"source": [
"# visual style gets always imported with increasing index,\n",
"# later check if style in Cytoscape is already available\n",
"styles_avail = p4c.get_visual_style_names()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1b6023ef-b4a2-4cf3-92ef-01419fc5258a",
"metadata": {},
"outputs": [],
"source": [
"if 'lang_main' not in styles_avail:\n",
" p4c.import_visual_styles('lang_main.xml')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3e27695b-5b26-4176-9bc9-adb91d848025",
"metadata": {},
"outputs": [],
"source": [
"assert 'lang_main' in p4c.get_visual_style_names()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4083b72d-f321-4489-be19-6167f13ab226",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"id": "59234fed-cc38-4ee3-9ef8-9180e7785ea5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'message': 'Visual Style applied.'}"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.set_visual_style('lang_main')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2653d1af-b3a5-4da7-9066-0940cb913dab",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd5fd222-75fd-4523-8401-a4a37c6010fd",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 79,
"id": "d972ff5a-e695-43b6-b8c5-ab295fd5de3d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.notebook_export_show_image()"
]
},
{
"cell_type": "markdown",
"id": "a45fce84-bbb9-476a-8be3-5eb7b082afed",
"metadata": {},
"source": [
"- graph properties in Cytoscape or pre-calculated?\n",
"- node sizes depending on graph properties\n",
"- edge width depending on graph properties"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3796359f-3112-4bbf-ae76-e52b3602c9f0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c8cba3d9-0145-4b37-9ebf-07f0e3c61815",
"metadata": {},
"source": [
"---\n",
"\n",
"# Py4Cytoscape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fcd9247f-c4f9-4f73-9fd3-2ab56700073f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | Calling cytoscape_ping()\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀCalling cytoscape_version_info(base_url='http://127.0.0.1:1234/v1')\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀǀCalling cyrest_get('version', base_url='http://127.0.0.1:1234/v1')\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀǀHTTP GET(http://127.0.0.1:1234/v1/version)\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀǀOK[200], content: {\"apiVersion\":\"v1\",\"cytoscapeVersion\":\"3.10.2\"}\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀǀReturning 'cyrest_get': {'apiVersion': 'v1', 'cytoscapeVersion': '3.10.2'}\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀReturning 'cytoscape_version_info': {'apiVersion': 'v1', 'cytoscapeVersion': '3.10.2', 'automationAPIVersion': '1.9.0', 'py4cytoscapeVersion': '1.9.0'}\n",
"You are connected to Cytoscape!\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | Returning 'cytoscape_ping': 'You are connected to Cytoscape!'\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | --------------------\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | Calling cytoscape_version_info()\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀCalling cyrest_get('version', base_url='http://127.0.0.1:1234/v1')\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀHTTP GET(http://127.0.0.1:1234/v1/version)\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀOK[200], content: {\"apiVersion\":\"v1\",\"cytoscapeVersion\":\"3.10.2\"}\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | ǀReturning 'cyrest_get': {'apiVersion': 'v1', 'cytoscapeVersion': '3.10.2'}\n",
"2024-07-10 11:19:15 +0000 | py4cytoscape_logger:DEBUG | Returning 'cytoscape_version_info': {'apiVersion': 'v1', 'cytoscapeVersion': '3.10.2', 'automationAPIVersion': '1.9.0', 'py4cytoscapeVersion': '1.9.0'}\n",
"2024-07-10 11:19:16 +0000 | py4cytoscape_logger:DEBUG | --------------------\n"
]
},
{
"data": {
"text/plain": [
"{'apiVersion': 'v1',\n",
" 'cytoscapeVersion': '3.10.2',\n",
" 'automationAPIVersion': '1.9.0',\n",
" 'py4cytoscapeVersion': '1.9.0'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import py4cytoscape as p4c\n",
"dir(p4c)\n",
"p4c.cytoscape_ping()\n",
"p4c.cytoscape_version_info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b9290659-e33c-47fc-8d89-7aa3dd6e843a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"nodes = pd.DataFrame(data={'id': [\"node 0\",\"node 1\",\"node 2\",\"node 3\"], 'group': [\"A\",\"A\",\"B\",\"B\"], 'score': [20,10,15,5]})\n",
"edges = pd.DataFrame(data={'source': [\"node 0\",\"node 0\",\"node 0\",\"node 2\"], 'target': [\"node 1\",\"node 2\",\"node 3\",\"node 3\"], 'interaction': [\"inhibits\",\"interacts\",\"activates\",\"interacts\"], 'weight': [5.1,3.0,5.2,9.9]})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "979d6def-83ac-47f6-ac6f-0d20ddf48d48",
"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>id</th>\n",
" <th>group</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>node 0</td>\n",
" <td>A</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>node 1</td>\n",
" <td>A</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>node 2</td>\n",
" <td>B</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>node 3</td>\n",
" <td>B</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id group score\n",
"0 node 0 A 20\n",
"1 node 1 A 10\n",
"2 node 2 B 15\n",
"3 node 3 B 5"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nodes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "81702429-5735-48de-96a4-1f32c7c7d68c",
"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>source</th>\n",
" <th>target</th>\n",
" <th>interaction</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>node 0</td>\n",
" <td>node 1</td>\n",
" <td>inhibits</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>node 0</td>\n",
" <td>node 2</td>\n",
" <td>interacts</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>node 0</td>\n",
" <td>node 3</td>\n",
" <td>activates</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>node 2</td>\n",
" <td>node 3</td>\n",
" <td>interacts</td>\n",
" <td>9.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source target interaction weight\n",
"0 node 0 node 1 inhibits 5.1\n",
"1 node 0 node 2 interacts 3.0\n",
"2 node 0 node 3 activates 5.2\n",
"3 node 2 node 3 interacts 9.9"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edges"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6b29d561-fffd-4a5b-91c1-8fb6a075ae4f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying default style...\n",
"Applying preferred layout\n"
]
},
{
"data": {
"text/plain": [
"128"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.create_network_from_data_frames(nodes, edges, title=\"my first network\", collection=\"DataFrame Example\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2e6878db-40c0-4ae6-89d6-9b1a5e50baaf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.notebook_export_show_image()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "66128f17-16eb-43d3-9d63-bbac3f8f803a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'message': 'Visual Style applied.'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.set_visual_style('Marquee')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ca0cc760-74e4-4c4a-b78a-c932ab16ab06",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.notebook_export_show_image()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "24c29cb3-cf64-4fad-8b1a-b0962f1005b4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'message': 'Visual Style applied.'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_name = \"myStyle\"\n",
"defaults = {'NODE_SHAPE': \"diamond\", 'NODE_SIZE': 30, 'EDGE_TRANSPARENCY': 120, 'NODE_LABEL_POSITION': \"W,E,c,0.00,0.00\"}\n",
"nodeLabels = p4c.map_visual_property('node label', 'id', 'p') #'p' means 'passthrough' mapping\n",
"edgeWidth = p4c.map_visual_property('edge width', 'weight', 'p') #'p' means 'passthrough' mapping\n",
"p4c.create_visual_style(style_name, defaults, [nodeLabels, edgeWidth])\n",
"p4c.set_visual_style(style_name)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1dfb553a-2367-463e-8a3d-0d232c2aedd0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.notebook_export_show_image()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5d535abb-83f0-40c8-b781-5b3129183ebf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Applying default style...\n",
"Applying preferred layout\n"
]
},
{
"data": {
"text/plain": [
"397"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nodes = pd.DataFrame(data={'id': [\"A\", \"B\", \"C\", \"D\"]})\n",
"edges = pd.DataFrame(data={'source': [\"C\", \"B\", \"B\", \"B\"], 'target': [\"D\", \"A\", \"D\", \"C\"]})\n",
"\n",
"p4c.create_network_from_data_frames(nodes, edges, title=\"simple network\", collection=\"Biological Example\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bb8bf383-7760-434e-ab3d-71b976024006",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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NlA1Y4+7gGKcfkFiO347OXajCwqY9QaLk7B0+mSV65DrE/hF3TgGF3SgbsEr7wKhafIlPOFxOTqUdd6AKE7lYfOXKlYEy8bBJxZ4gUTK/mjdJSDBHblHMYQfKBqwjbwadur/YXM5pVU5FPtXa0TjofcKFxJOCMF4WFixYEPo9QaKipW808FomRCKnBgM2oGzAan3DY6qtf1Rd7x4hE2Txhu1q2vxCL9MXrlQ1bT2BmTBHrsvopGAkldzlKiMjI1YM5Fa7EwnLniBRcYGbW5AJklvVa75cgEiibACOkwuE/Qu8w4cPmyNwXHNzs/YaOXr0qDkSVzL2BFm/fn3cPUGi4mQrZYNMnEHucQELUDYAx8k7xf7TX+SuQnKhMDDu7Nmz2mJfisOjSvaeIHKh+8hI+O/oc6J1KLDAJGQ8g1wkDgtQNgB459z7F25y61JgnNxNyv/66O7uNkcSJpF7gsipX3JL382bN4d2TxDKBokXygZsQNkAoG7fvq0t0uSWp8C4FStWxF4bcg1GqiVjT5B169aFYk8QygaJF8oGbEDZAOAxb23a1tZmjsBR/tObioqKzMNpYcueIJQNEi+UDdiAsgHAI3cQ8i++ZNEFyGlN/tdFWVmZORIqcrF4bW2td6OD0tJSNW/evECxmEqSvScIZYPEC2UDNqBsAPDIO8WzZ8+OLbLknV7bbjOKqZPTjPyL7wsXLpgjoRfmPUEoGyReKBuwAWUDQMzBgwe1RZUszuC28vJy7TUhC2tbhGFPEMoGiRfKBmxA2QAQI3cZ8l94KztFw21yRyj/gvph38GPilTvCULZIPFC2YANKBsANLLA8i+W5HahcFdBQUHstSDv3LsomXuCbK68HlhgEjIeygZsQNkAoGlsbNQWRvKuLNwkt5z1vxa2bt1qjjgtEXuCZBRtDywwCRkPZQM2oGwACJB9DGKLoYyMwKkfcIPsbeFfGHMNz4ub6p4glA0SL5QN2ICyASBAThfxL4hk8zO4RxbM/teBfOqFqYu3Jwhlg8QLZQM2oGwACJDNzOS88vFFptylZ2RkxByD5eSdeX/ZmMzdlTB59+7dU7tqbgcWmISMh7IBG1A2ADyQvAPrX2jKu7Jwy5IlS2LPv9xVCYnH3ahIvFA2YAPKBoAHkotf/bf8XL58uTkCi8mpP/5rDWQ3biQeZYPEC2UDNqBsAJiQ7JLs/3RDbv8JN8g1Bv7nXvaJQOK5WDb+ZcMp9Vt/841AfueL/6H+4J8z1F9kr1b/ebA58HUuhrIBG1A2AEyorq5OW3DKOfxwg5w253/uZUM6JJ6LZUPKxGOPPRY3T7zsSfWhz31VZZy8G/h6l0LZgA0oGwAmJLtFz5kzJ7bglB2k+/r6zDFYyLxmp7Oz0xxBArhcNt7w9veov8pbG8ufz1ih/uQ/Zqv3fvyz6vHHn/Bm3v27n1SZZ7oDf4YroWzABpQNAHEdO3ZMW3QePXrUHIGF5Pas48+53KYVyeFy2Xj2Ax8JHBvPl1buVy976mlv7k//c37guCuhbMAGlA0AccknGfKJxvjCs6CgwPvEA3Yb3wdCsmrVKvMwEoSyETw+nk9+Z6439+M//XOBY66EsgEbUDYAvKitW7dqn27U1taaI7CI7Bjvf753795tjiBBKBvB4+PJONWpnnrmVd7sv246EzjuQigbsAFlA8CLamlp0RafRUVF5ggsImXS/3xXVlaaI0gQykbwuD9v+ZXf8GY//f3FgWMuhLIBG1A2AExKYWGhtgBtb283R2CJw4cPa8+1lE0kB2UjeNyf9/zep7zZj//r9MAxF0LZgA0oGwAm5cKFC5xa44gNGzbEnmfZ2G9kZMQcQYJQNoLH/fmfv/9pb/b3vvy9wDEXQtmADSgbACZFFpx5eXmxRWhOTo4aGhoyx2CBefPmxZ7nRYsWmYeRQJSN4HF/3vbrv+PN/sk3ZwWOuRDKBmxA2QAwaQcOHNA+3Th9+rQ5gogbHBxU06ZNiz3HmzdvNkeQQJSN4HF/Xv0Tb/Bm/37pzsAxF0LZgA0oGwAmTe5SlJGRwbveFmtqatIK5fHjx80RJBBlI3h8PF8pPuLNPfmKZ9QPjrcFjrsQygZsQNkAMCWlpaXaYvT69evmCCLs1KlT2vN79epVcwQJRNkIHpdknu1Rz/7ab3tz7/vkFwLHXQllAzagbACYkmvXrmmLUbmYGPbYtm2b9vzKpo5IHspG8PjXNp1V7/rIH3szz/zYa9W39jYEZlwJZQM2oGwAmLKFCxfGFqNyWtW9e/fMEUTU8uXLY89tfn6+eRgJ5nLZePLpV6rXv/UdsbzuTb+gnn71a7xjXtF4zevUl1YdCHy9S6FswAaUDQBTZp5qU15ebo4ggsbGxtSMGTNiz+u6devMESSYy2XDzOOPP+GVjTe95/3qd7/0HfXd8huBr3UtlA3YgLIBYMrkjkXZ2dmxRemsWbPU6OioOYaIaWtr00qk3H0MyeVi2SCTD2UDNqBsAHgou3bt0hamFy9eNEcQMfIc+p/T6upqcwQJRtkg8ULZgA0oGwAeyp07d7SF6YoVK8wRRMy+ffu057S9vd0cQYJRNki8UDZgA8oGgIe2Zs0abXF669YtcwQRUlxcHHsus7KyzMNIAsoGiRfKBmxA2QDw0C5fvqyVDbltKqJLrr0Zfy4LCwvNw0gCygaJF8oGbEDZAPDQ5O5FBQUFsQWq3Mmov7/fHEME9PT0aMVxx44d5giSgLJB4oWyARtQNgA8kiNHjmiL1OPHj5sjiIArV65oz+OZM2fMESQBZYPEC2UDNqBsAHgkvb29KjMzM7ZInTt3rjmCCDh27JhWNm7cuGGOIAkoGyReKBuwAWUDwCPbvHmztlCtr683RxBymzZtij1/06ZNU0NDQ+YIkoCyQeKFsgEbUDYAPLLm5matbKxdu9YcQcgtXLgw9vzNnz/fPIwkoWyQeKFswAaUDQAJsWzZMu2d8bt375ojCKnh4WGVkZERe/42btxojiBJKBskXigbsAFlA0BCVFZWap9u7N271xxBSN28eVN77o4ePWqOIEkoGyReKBuwAWUDQELIu+MzZ86MLVhzc3O9f4fwO3v2rFY2uOYmdSgbJF4oG7ABZQNAwuzbt09btJ47d84cQQjt3LlTe966u7vNESQJZYPEC2UDNqBsAEiYzs5O73qN8UXrkiVLzBGE0IoVK2LPWV5ennkYSUTZIPFC2YANKBsAEqqkpER7l7ypqckcQchkZ2fHnq+ioiLzMJKIskHiZXCUsoHoo2wASKiGhgatbMj+DQivjo4O7fkqKyszR5BEVe3DgQUmIZKcyl7z5QJEEmUDQMLJPg3ji9fp06ernp4ecwQhcenSJa1sXLhwwRxBEjX1jAYWmYRIllzuN18uQCRRNgAk3IkTJ7QF7KFDh8wRhER5ebn2XLW2tpojSCI5Syb/Ql9goUnI/uYh8+UCRBJlA0DCDQwMqKysrNgCNj8/X42OjppjCAH/NTaZmZlqbIxzxFPt6C2u2yB6cit7VdcgfxdhB8oGgKTYvn279o65nK6D8CkoKIg9R0uXLjUPIwWG7vfw5TX9gQUncTcVrXyqAXtQNgAkhZyO4y8bq1atMkeQZv39/dpztHXrVnMEKSLvYi+5ROEgvaqsedB8eQCRRtkAkDRSMPyLWa4HCJdr165pz8/JkyfNEaTQwMiY2tU0qLIrgwtQYn/mXexTFzuGzZcFEHmUDQBJU11drS1md+zYYY4gjSoqKrTnp7Gx0RxBGtwbGlNn7gyrnfeLx4aGAVVcn5osOtumMstqYsk/1hSYIYnN89cH1b7mQVXfNaLYvw+2omwASBq5KHz27NmxxaxcNC4XjyMctmzZopUNnht3VVZWqmnTpmmvhyNHjphjADBllA0ASSW3vfUvYOS2uAiHJUuWxJ6XOXPmmIfhiIsXLwaKxoYNG7gzGYCEoGwASKru7m5vY7/xRYxs+If0k0+d/M9LaWmpOQIH1NTUqIyMDK1orFu3jltVA0gYygaApNu0aZO2mGloaDBHkGK3b9/WnhM2XnTPlStXtMIpWbNmjRoZGTFHAeChUTYAJF1TU5O2oFm/fr05ghSrqqrSnhN5hxvukDuRzZgxQ3sNrFy5Ug0Nsb8DgMSibABICf/1AXJ+eGdnpzmCFNqzZ4+20OT5cMeNGze8mzX4n//ly5erwUH2dwCQeJQNAClx9uxZbXGzb98+cwQptHr16thzkZOTYx6GpVpaWrzn2/93Ud4IkA0eASAZKBsAUmJ4eFjl5ubGFjgzZ87k3PA08j8X7O7uBtlUU/7e+YvGwoULVW9vrzkKAAlD2QCQMuapO3LdAFKvq6tLex52795tjsAy7e3tKi8vT3ve582b590tDgCSibIBIGU6Ojq0+/kvW7bMHEEK1NbWaotO2dAN9rp7967Kz8/XnvOCggKvdAJAslE2AKTU2rVrtUVPc3OzOYIkO3z4sPYcyHn8sJMUCtmw0f98z5492ysgAJAKlA0AKVVXV6ctfLZs2WKOIMlkd+jxx1/2WeDaGTv19PR4p0r5/77JqVR37twxRwEgaSgbAFJu7ty5scVPZmYmF6immH8BumjRIvMwLNDX1+dd/O0vGnJTANnMEQBSibIBIOWOHz+uLYKOHj1qjiBJZC8F/3UzmzdvNkcQcQMDA2rp0qXa37Hs7Gx18+ZNcxQAko6yASDl5J7+/t2L5WLVsbExcwxJYO7mLsUP9pAyKRv0+Z9j+bsmzzsApANlA0BabN26VVsQ1dTUmCNIglOnTmmP+9WrV80RRJTsZSN7pvifXzlNkecYQDpRNgCkxa1bt7RF0Zo1a8wRJMG2bdu0x13O7Uf0yUX+RUVF2nMrF//X19ebowCQUpQNAGlTWFioLY64S07y+U+xkb0XEH2jo6OqpKRE+7uUkZGhLl++bI4CQMpRNgCkzYULF7QF0q5du8wRJJBcF+O/VmbdunXmCCJGntONGzdqf4/kBgDydwsAwoCyASBt5NSPWbNmxRZJcsccucAVySGfHPkXpQcOHDBHEDGyT43/OZWcO3fOHAOAtKFsAEgrWfD6F0qnT582R5AgFy9e1B7r6upqcwQRsn379kDROHnypDkGAGlF2QCQVvfu3fPOLx9fLMlGZEiOffv2aQvT9vZ2cwQRsWfPnkDROHbsmDkGAGlH2QCQdhs2bNAWTdeuXTNHkADFxcWxxzgrK8s8jIgwPw2UHDx40BwDgFCgbABIu+vXr2sLJykfSDz/9TFyJzBEz5EjRwJFo6yszBwDgNCgbAAIBTl9anzxJKdVyelVSJyenh5tgbpjxw5zBCFXUVERKBrcwQ1A2FE2AISCXBjuX0Rxp6TEunLlivb4njlzxhxBiMnzZRaNrVu3mmMAEDqUDQChILe8lVvfji+k5JQfuTUuEkMuHvYvVG/cuGGOIKSqqqq8vTP8z99zzz3n7bEBAGFH2QAQGnJKiH9BxcZkibNp06bY4yoL16GhIXMEISS3JzaLRmlpKUUDQGRQNgCEhrnpHBcxJ47/mpj58+ebhxFCtbW12m2hJWvXruUTPwCRQtkAECpr1qzRFlctLS3mCKZoeHhYW7Ru3LjRHEHINDQ0qMzMTO3vwurVq73nEgCihLIBIFRqamq0BRYXwT66mzdvao+p3D4V4SW3gp4xY4b2nK1YsYJT3wBEEmUDQKjIuegFBQWxRZYsuvr7+80xTMHZs2e1hWt9fb05gpCQC/dlw0X/87Vs2TI1MDBgjgJAJFA2AITO0aNHtcXW8ePHzRFMwc6dO7XHs7u72xxBCMgpgzk5OdpztXjxYtXX12eOAkBkUDYAhE5vb692vvrcuXO5+84jkFNwxh/LvLw88zBCoK2tTc2cOVMrGgsWLPD+LgBAlFE2AITSli1btIVXXV2dOYJJ8u9fUlRUZB5GmrW3t3v7yvhf71Kw7927Z44CQORQNgCEUnNzs7b4klt+Yuru3r2rPY5lZWXmCNKos7NT5efna8+RXLMk/x4AbEDZABBacmHs+AJMNjbr6OgwR/AiLl++rC1k2SgxPOSTC/kEw//8zJ49m9c5AKtQNgCEVlVVlbYQ27NnjzmCF1FeXq49hq2treYI0qCnp8fbXNH/3Mg1G3LtBgDYhLIBILRkp2T/RbNypx72GpiakpKS2OMnF91zoX36yd2lFi1apBWN3NxcdevWLXMUACKPsgEg1Pbt26ctymTPCEyef8+SpUuXmoeRYrJfhjwP/te0XMAv1ygBgI0oGwBCraury7teY3xhJvsOYHJkM0T/opbd2NNLPpUrLCzUnhPZtLKxsdEcBQBrUDYAhN769eu1BVpTU5M5gge4du2a9ridPHnSHEGKDA8Pq9WrV2vPh5zW1tDQYI4CgFUoGwBCTxZk/kXapk2bzBE8QEVFhfa48Q56esi1R8XFxdpzMX36dPaOAeAEygaASPDfuUcWat3d3eYIDObGiHK9AFJLLsg3P5nLyMhQly5dMkcBwEqUDQCRcOLECW3BdujQIXMEhiVLlsQerzlz5piHkWRSNORTOP/rVq4/On/+vDkKANaibACIBHlXPisrK7Zok83PRkdHzTH8iDw28gnQ+ONVWlpqjiDJnn/+ea1oSLibGgDXUDYARMaOHTu0hVt1dbU5gh+5ffu29lgdPHjQHEES7dy5M1A05NM5AHANZQNAZMju1/7F28qVK80R/Ii5+3pNTY05giTZu3dvoGgcPXrUHAMAJ1A2AETKqlWrtEWcFBAE7dmzR3ucOjs7zREkQXl5eaBoyL8DAFdRNgBEitzFx7+Q2759uzmC+/x7OuTk5JiHkQTy6YVZNORTDgBwGWUDQKTIhc/5+fmxxZxcNM4tXYNyc3Njj5F8GoTkMu+WJpFrjADAdZQNAJEjt731L+q48FbX1dWlPT67d+82R5BAcocps2jInagAAJQNABHU09Oj3dZVNvzDC2Rnav/Ct7Ky0hxBgsieGbJ3hv/xlr01ZI8NAABlA0BEPffcc9oCr6GhwRxx1uHDh7XHpqWlxRxBAsj1Q7IbuP+xlt3C2f8FAF5A2QAQSTdu3NAWeSUlJeaIszZs2BB7XGQxPDIyYo7gEcmnR/5P1yTFxcU81gBgoGwAiKwlS5bEFnpyKgu3d/2hefPmxR6XRYsWmYfxiK5evaoyMzO1oiEX4Q8PD5ujAOA8ygaAyDp37py24CsrKzNHnDM4OKhdQ7B582ZzBI+gsbFRzZgxQ3vdFRYWqqGhIXMUAKAoGwAiTN5J9t/idebMmc6/u9zU1KQthI8fP26O4CE1Nzer7Oxs7fFdunQpt14GgDgoGwAiTTZN8y/+XL/z0qlTp7THQ075waO7deuWVmwlcopaX1+fOQoA8KFsAIi0u3fvaqcNLVu2zBxxyrZt27QFMYvhR9fW1qby8vK0x1Vutyy3YAYAxEfZABB5a9eu1RaCcrqLq5YvXx57HGSndTyajo4ONXv2bO31NWfOHHXv3j1zFADwAJQNAJFXX1+vLQZdvShaNpLzX7y8bt06cwRTIDuxFxQUaK8tKXDc9QwAJo+yAcAKc+fOjS0I5bakvb295oj17ty5oy2MDxw4YI5gkrq7u7XXlGTWrFmqvb3dHAUAxEHZAGAFueuSf2F45MgRc8R6Fy9e1B6D6upqcwSTIEV1wYIF2mMpdzprbW01RwEAL4KyAcAK/f392ilEcvqLnFbkkn379mkLZN6Fnzp5HS1evFh7HHNyclRLS4s5CgCYBMoGAGuYd2K6fPmyOWK14uLi2O+elZVlHsaLkP0y5G5m/teQPI43btwwRwEAk0TZAGAN2QvBv1Bcs2aNOWI1uaZg/HeXXa0xebID+IoVK7TXj3xSdv36dXMUADAFlA0AVjEXjHLRtAtkzwf/771jxw5zBBOQXeelmPofP7nJwJUrV8xRAMAUUTYAWMW8SHrXrl3miJUaGhq03/vMmTPmCB5gdHTUu0Ww/7HLyMhQtbW15igA4CFQNgBYRRaP/tOJsrOz1eDgoDlmnWPHjmkLZq4zeHFyA4HS0lLtcZPd6LmLFwAkDmUDgHXKy8u1BeSpU6fMEets2rRJWzDLNQiYmBSN5557LlA0qqqqzFEAwCOgbACwzr1797xTYcYXkQsXLjRHrCO/4/jvO3/+fPMwDOadyySnT582xwAAj4iyAcBKGzZs0BaS165dM0esIRc4+8vVxo0bzRH4yHU8ZtGoqKgwxwAACUDZAGAluWWpfzEp5+bb6ubNm9rv6uLu6ZNlbnwoOXz4sDkGAEgQygYAay1atCi2oJR3/ru6uswRK5w9e1ZbPNfX15sjuO/gwYOBorF//35zDACQQJQNANaSc/D9C8sDBw6YI1bYuXOn9nt2d3ebI84z79Yl2bNnjzkGAEgwygYAa8kdmXJycmKLy7y8PDUyMmKORZ5/I0P5HaGTu5GZRWP79u3mGAAgCSgbAKy2e/dubZF54cIFcyTyZC+R8d+vqKjIPOy0c+fOebe09b8GtmzZYo4BAJKEsgHAau3t7dpCc/ny5eZIpN29e1f7/crKyswRZ0mxNIuG3KVM9tgAAKQGZQOA9eTdfv+Cs6WlxRyJrMuXL2u/m42f3DwMeVz8twOWlJSUeDvMAwBSh7IBwHq1tbXaovP55583RyLL3C29tbXVHHGO3I1r+vTp2uMihdPG63UAIOwoGwCsJ6fNzJkzJ7bwzMzMVH19feZYJMm79f7fy/VThGTzRnkc/EVj5cqV3saHAIDUo2wAcMLRo0e1BajcCtUGBQUFsd9p6dKl5mGnNDU1qaysLO15lmt0BgcHzVEAQIpQNgA4QT7J8L/jLZ90RP1TgP7+fm1hvXXrVnPEGbKLuv+uXJIlS5Z4jxEAIH0oGwCcIbc89S9G6+rqzJFIkVOG/L/PyZMnzREn3L59W+Xm5mqPxcKFC1Vvb685CgBIMcoGAGfIu9/+BWlxcbE5EikVFRXa79PY2GiOWO/OnTveRob+x2HevHnsog4AIUHZAOAUOYd/fFEqezB0dHSYI5FhflIzMDBgjlhN9hiZPXu29hjI6XFdXV3mKAAgTSgbAJxy/vx5bXG6Z88ecyQy5JoE/yLbJVIo/BfHS/Lz870CAgAID8oGAKfIXgv+025ycnLU0NCQORZ6sjmdfy+J0tJSc8RacoqUnCrlLxrynMpu8QCAcKFsAHDO/v37tYXqmTNnzJHQk4ui/b/DwYMHzREryUXfcvG3/3eXi8Pl8QAAhA9lA4Bz5BScjIyM2GJ18eLF5kjoVVVVaQvumpoac8Q6chtb/6ljEvlkSi78BwCEE2UDgJPWr1+vLVqjdicnudbE//N3dnaaI1aRjfn8F/dLZAO/GzdumKMAgBChbABw0tWrV7WF68aNG82RUFu9erX27r7N5JqalStXas+XbNAo+4wAAMKNsgHAWQsWLIgtXuW0qijtzeDfxG7VqlXmYWvIBf1FRUVa0ZAL4+vr681RAEAIUTYAOEt23PYvYqNykbVcc+L/uXfv3m2OWEHuuFVSUqL9rlIKXbg+BQBsQdkA4Cy5DkDO+x9fyMoGcbLADbu6ujptAV5ZWWmORN7Y2JjasGGD9nvKJowXL140RwEAIUbZAOC0nTt3agva6upqcyR0Dh8+rP3MLS0t5kikSdHYvHlzoGjYWKoAwHaUDQBOa2tr0xa1ciFy2Pnf8ZfTiuS6Bpts375de04kp06dMscAABFA2QDgPP+dnSRh3yDOv3v2okWLzMORJtefmEXj+PHj5hgAICIoGwCcd+nSJW1xK++sh5VcZyKnFI3/rHK6kS3Mnd0lhw4dMscAABFC2QDgPLlGID8/P7bAnTFjhhoYGDDHQqGpqcnKd/3N61Ak+/btM8cAABFD2QAAFVzsVlRUmCOhINcu+H9O2Zww6qQwmUVj165d5hgAIIIoGwBwX09Pj7dZ3PhiV66LCKNt27Zpi/K+vj5zJFJOnz4dKBryOwIA7EDZAIAfee6557RF75UrV8yRtFu+fHns55NTv6JMbmXrv/5EItegyGltAAA7UDYA4Edu3LihLXzXrVtnjqSVLMLlepKw/nxTIfuZmEVDbulL0QAAu1A2AMBn6dKlscWvLIY7OzvNkbS5c+eOtjiXuzdFUU1Njbc/iFnsorB7OwBgaigbAOBz7tw5bRFcVlZmjqTNxYsXtZ8tCrudm+TUNP+1MZI1a9ao4eFhcxQAYAHKBgD4yKI3Nzc3thCWfw7LQlhuBetfpLe3t5sjoXb9+nXtNDDJihUr1NDQkDkKALAEZQMADHv37tUWxHIhcxgUFxfHfqasrKxIXd8g18PIz+x/XOVid9mkEABgL8oGABju3r2rXbws13GEwaxZs2I/U2FhoXk4tFpaWlROTo5WNBYvXqz6+/vNUQCAZSgbAPAAcsGyf3Es78ynk+wD4v95duzYYY6EUmtrq5o5c6b2sy9YsED19vaaowAAC1E2AOAB5EJm/wJZ9uBIp4aGBu3nOXPmjDkSOnJNif/TGMncuXNVd3e3OQoAsBRlAwAmILuIjy+S5Q5K8ulCuhw7dkxbtKf7k5YXI7cMlk0H/T9zQUGB6urqMkcBABajbADABCoqKrTF8uHDh82RlPHvbi7Xk4T5Dk5SKObMmaM9drNnz1YdHR3mKADAcpQNAJiAXMDsv1WrvFOfrjtALVy4MPZzzJ8/3zwcGvLpj/x8/qKRl5fnbUgIAHAPZQMA4ti2bZu2cL58+bI5knSyz4d/x+2NGzeaI6HQ19enFi1apD1esk/J7du3zVEAgCMoGwAQhyyU/Yvn1atXmyNJd/PmTe1nOHLkiDmSdgMDA94tgv0/Z3Z2tmpubjZHAQAOoWwAwItYuXKltohua2szR5Lq7Nmz2vevr683R9JKrh+RDfr8P6OcftbU1GSOAgAcQ9kAgBdRXV2tLaR37txpjiSVfD//9w/TrWPlFK9Vq1ZpP19mZqa6evWqOQoAcBBlAwBexOjoqHc3pfHFtJweNDg4aI4lzYoVK2LfWy62DouRkRFVXFysFQ25RXBdXZ05CgBwFGUDACbh4MGD2qL65MmT5kjSSLkZ/75FRUXm4bSQArZ+/XrtMZGL2C9dumSOAgAcRtkAgEmQU5f8d4RasGCBOZIUd+/e1Rb0e/fuNUdSTm7/K3fE8v9csvfHhQsXzFEAgOMoGwAwSeYCOxXXJcitdv3fMwwL+ueff177mSRyETsAACbKBgBMUmNjo7bALi0tNUcSrry8XPuera2t5khK7dixI1A0UnlKGQAgWigbADAF/k3r5LSqrq4ucyShSkpKYt9P7vIk10qky549ewJF4+jRo+YYAAAxlA0AmIIzZ85oi+39+/ebIwlVUFAQ+16yaV66HDhwIFA05KJ5AADioWwAwBTIBnY5OTmxBbfcilZuAZsM/f392uJ+69at5khKyI7lZtEoKyszxwAACKBsAMAUmacTnT9/3hxJiGvXrmnfJx3XRpw4cSJQNFK9qSEAILooGwAwRe3t7d6tXscX38uXLzdHEqKiokJb5MsF6qlknjImSdenKwCAaKJsAMBDMHfOvnnzpjnyyMxbzA4MDJgjSVNVVaUVKsmmTZu8PTYAAJgsygYAPITa2lptIS7FINGWLFkS+/PnzJljHk6a6upqbQNDidzml6IBAJgqygYAPARZeEsBGF+My21p+/r6zLGHJre4nT59urbYTwUpUf7vK1m7dm3SLoIHANiNsgEAD+nYsWPaolz+/0S5ffu29men4jazDQ0NXmnyf9/Vq1er4eFhcxQAgEmhbADAQ5JPMvyLc/mkI1GnGsk1E/5Ff01NjTmSUHLx+YwZM7TvWVhY6N3qFwCAh0XZAIBHYF7ELachJYJ5e93Ozk5zJGGam5tVdna29v2WLVuW0gvSAQB2omwAwCNoaWnRFulFRUXmyEOR05fG/0zZRDBZbt26pW1SKFm8eHFCrz8BALiLsgEAj0j22RhfqMvtYmUfjkeVm5sb+zNXrVplHk6ItrY2NXPmTK1ozJ8/X/X09JijAAA8FMoGADwi2UHcv2DfvXu3OTIlXV1dCf3zHqSjo0PNmjVL+z5z585V9+7dM0cBAHholA0AeERyW9i8vLzYol0+lXiUC8Xr6uq0ElBZWWmOPDJzU8L8/PykXhcCAHATZQNAKNzsHVV7bwyqlbX9akF1X+Qy60yHmnGsJRbz+FQyp+qe9mfNu9ATmHnU5Jxojf35WcdvJeV7xEthTb/a2TSornezfwcA2IyyASCt+kfG1KarA2rGuV7iaNZeGVDdQw//SRAAILwoGwDSpnd4TC2+1BdYfBL3Mvdin+oYoHAAgG0oGwDSZk1df2DRSdzNksv9api+AQBWoWwASIuLHcOBxSYhFbfZsRwAbELZAJAWfKpBHpSF1WwmCAA2oWwASLnBUaWyKoMLTUIkdwc5lwoAbEHZAJBy7QOjgQUmIePhdrgAYA/KBoCUa+mlbJCJU9dF2QAAW1A2AKQcZYPEC2UDAOxB2QCQcpQNEi+UDQCwB2UDQMpRNki8UDYAwB6UDQApR9kg8ULZAAB7UDYApBxlg8QLZQMA7EHZAJBylA0SL5QNALAHZQNAylE2SLxQNgDAHpQNAClH2SDxQtkAAHtQNgCkHGWDxAtlAwDsQdkAkHKUDRIvlA0AsAdlA0DKUTZIvFA2AMAelA0AKUfZIPFC2QAAe1A2AKQcZYPEC2UDAOxB2QCQcpQNEi+UDQCwB2UDQMpRNki8UDYAwB6UDQAp52rZ+PKag+q3/uYbgXzki99Uf/j1LPV3i7arjFOdga9zLZQNALAHZQNAyrlaNv7kP2arxx57LG5e+8a3qn9aezTwtS6FsgEA9qBsAEg518vGW375N9Rf5a2N5c8zC9Uf/VuOevMvf9A7/qrX/aT6/tHbga93JZQNALAHZQNAyrleNn7pY38WOCaZfuaeeuMv/oo386nvLQgcdyWUDQCwB2UDQMpRNh5cNiS//Xf/7s3I/zWPuRLKBgDYg7IBIOUoGxOXjfd+/LPejJxaZR5zJZQNALAHZQNAylE29LIx/XSX+tbeK+oP/mW6evyJl6o3vO3dKuPk3cDXuxLKBgDYg7IBIOVcLxvxItdsfOdAY+BrXQplAwDsQdkAkHKul42nnnmV+u8/9wuxvPaNb1FPv/o1scLx9g9+VH3vcEvg610JZQMA7EHZAJByrpcN8zSq8Xx98zn11v/1m7HCYR53JZQNALAHZQNAylE2Hlw2JN/Zf1098bInvbl/23ohcNyFUDYAwB6UDQApR9mYuGxI5LQqmfvbhdsCx1wIZQMA7EHZAJBylI2Jy8Z/HrqpXvryp7y5r206GzjuQigbAGAPygaAlKNsPLhs/PuOS+odv/n73ozc/tY87kooGwBgD8oGgJRzvWw89cr/pl7/1ndo+fGf/jn1kpe8xDv+5NOvVF9eczDw9a6EsgEA9qBsAEg518uGGSkZT77iGfUTb36b+sBnvujsheHjoWwAgD0oGwBSztWyQSYXygYA2IOyASDlKBskXigbAGAPygaAlKNskHihbACAPSgbAFKOskHihbIBAPagbABIOcoGiRfKBgDYg7IBIOUoGyReKBsAYA/KBoCUo2yQeKFsAIA9KBsAUo6yQeKFsgEA9qBsAEg5ygaJF8oGANiDsgEg5SgbJF4oGwBgD8oGgJSjbJB4oWwAgD0oGwBSjrJB4oWyAQD2oGwASDnKBokXygYA2IOyASDlKBskXigbAGAPygaAlKNskHihbACAPSgbAFLudh9lg0ycK/coGwBgC8oGgJTrGR4LLDAJGc+t+2UUAGAHygaAtJh3sS+wyCRkZlWvut9FAQCWoGwASIt9zUOBhSYhz18fNF8qAIAIo2wASIve4TGVd55PN8gLyansVe0DfKwBADahbABIm9rOEZX1gEUncTNn7wybLxEAQMRRNgCk1eX7hUPO0zcXnsSdZFdSNADAVpQNAGnXNTimdjQOqrwqTqtyKVIyN18bUHcGuPsUANiKsgEgNEbGlGrpG1X1XSPqfPswsTRy+tzN3lE1TMcAAOtRNgAAAAAkBWUDAAAAQFJQNgAAAAAkxf8H42UII6Vb/7EAAAAASUVORK5CYII=",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.notebook_export_show_image()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1c4ab4f0-1f4d-4b9e-8b56-ec0f648531cb",
"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>SUID</th>\n",
" <th>shared name</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>selected</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>427</th>\n",
" <td>427</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>430</th>\n",
" <td>430</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>433</th>\n",
" <td>433</td>\n",
" <td>C</td>\n",
" <td>C</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>436</th>\n",
" <td>436</td>\n",
" <td>D</td>\n",
" <td>D</td>\n",
" <td>D</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SUID shared name id name selected\n",
"427 427 A A A False\n",
"430 430 B B B False\n",
"433 433 C C C False\n",
"436 436 D D D False"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_table_columns()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0a7b318d-b4e1-4eef-ab13-14e9b7bfa80e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['SUID', 'shared name', 'id', 'name', 'selected']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_table_column_names()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "29ba0973-1bc8-4987-b4ff-f3e50f9df573",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Attribute Circle Layout': 'attribute-circle',\n",
" 'Stacked Node Layout': 'stacked-node-layout',\n",
" 'Attribute Grid Layout': 'attribute-grid',\n",
" 'Degree Sorted Circle Layout': 'degree-circle',\n",
" 'Circular Layout': 'circular',\n",
" 'Group Attributes Layout': 'attributes-layout',\n",
" 'Edge-weighted Spring Embedded Layout': 'kamada-kawai',\n",
" 'Prefuse Force Directed Layout': 'force-directed',\n",
" 'Compound Spring Embedder (CoSE)': 'cose',\n",
" 'Grid Layout': 'grid',\n",
" 'Hierarchical Layout': 'hierarchical',\n",
" 'Edge-weighted Force directed (BioLayout)': 'fruchterman-rheingold',\n",
" 'Inverted Self-Organizing Map Layout': 'isom',\n",
" 'Prefuse Force Directed OpenCL Layout': 'force-directed-cl'}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_layout_name_mapping()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "63aba4a3-f53f-4eb0-ab6b-17bbd1b58e78",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['m_averageIterationsPerNode',\n",
" 'm_nodeDistanceStrengthConstant',\n",
" 'm_nodeDistanceRestLengthConstant',\n",
" 'm_disconnectedNodeDistanceSpringStrength',\n",
" 'm_disconnectedNodeDistanceSpringRestLength',\n",
" 'm_anticollisionSpringStrength',\n",
" 'm_layoutPass',\n",
" 'singlePartition',\n",
" 'unweighted',\n",
" 'randomize']"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_layout_property_names('kamada-kawai')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "eec4f428-3abd-40da-a291-112b3e0d6f65",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'boolean'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_layout_property_type('kamada-kawai', 'randomize')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "98eb2412-5f24-4180-beac-2cadac9bb35e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.get_layout_property_value('kamada-kawai', 'randomize')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3bdb2b5f-609b-4bbf-8e42-a8ec07e5bda2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"id": "fc3d4dd1-a132-4f08-adc3-add2eb25cc27",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "11beffb4-a21e-4b73-9c0c-a0ec57513ebd",
"metadata": {},
"outputs": [],
"source": [
"tp = Path.cwd()\n",
"file = tp / 'test.svg'"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "03696e83-9c87-4ec3-bba2-024f4747385a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'file': 'A:\\\\Arbeitsaufgaben\\\\lang-main\\\\test-notebooks.xml'}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.export_visual_styles(str(tp))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "0e4068b3-7bf9-4093-8887-02b677a76fc1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'file': 'A:\\\\Arbeitsaufgaben\\\\lang-main\\\\test-notebooks\\\\test.svg'}"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p4c.export_image(str(file), type='SVG')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be448cd8-022c-446b-9294-2d00bc445054",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "52792182-c8bc-4973-9682-36360604705b",
"metadata": {},
"source": [
"---\n",
"\n",
"# Find reason that TokenGraph weight sometimes is zero"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7e5c6d2c-2558-4c6a-9e0b-19e36279b4ce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 14:22:25 +0000 | io:INFO | Loaded TOML config file successfully.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
" _torch_pytree._register_pytree_node(\n",
"A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from lang_main.constants import SAVE_PATH_FOLDER, SPCY_MODEL\n",
"from lang_main.types import EntryPoints\n",
"from lang_main import io\n",
"from lang_main.analysis import tokens, graphs"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f4cad9da-6570-41c5-9adb-032052e34a7d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 14:22:30 +0000 | io:INFO | Loaded file successfully.\n"
]
}
],
"source": [
"p_df = io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE)\n",
"(data,) = io.load_pickle(p_df)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c7c683a5-a6db-42b0-84f0-7806d30bd46f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>VorgangsID</th>\n",
" <th>ObjektID</th>\n",
" <th>HObjektText</th>\n",
" <th>ObjektArtID</th>\n",
" <th>ObjektArtText</th>\n",
" <th>VorgangsTypID</th>\n",
" <th>VorgangsTypName</th>\n",
" <th>VorgangsDatum</th>\n",
" <th>VorgangsStatusId</th>\n",
" <th>VorgangsPrioritaet</th>\n",
" <th>VorgangsBeschreibung</th>\n",
" <th>VorgangsOrt</th>\n",
" <th>VorgangsArtText</th>\n",
" <th>ErledigungsDatum</th>\n",
" <th>ErledigungsArtText</th>\n",
" <th>ErledigungsBeschreibung</th>\n",
" <th>MPMelderArbeitsplatz</th>\n",
" <th>MPAbteilungBezeichnung</th>\n",
" <th>Arbeitsbeginn</th>\n",
" <th>ErstellungsDatum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>53</td>\n",
" <td>244</td>\n",
" <td>285 C, Webmaschine, SG 220 EMS</td>\n",
" <td>5</td>\n",
" <td>Greifer-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2019-03-19</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Kupplung schleift</td>\n",
" <td>NaN</td>\n",
" <td>Kupplung defekt</td>\n",
" <td>2019-03-20</td>\n",
" <td>Reparatur UTT</td>\n",
" <td>NaN</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>NaT</td>\n",
" <td>2019-03-19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>58</td>\n",
" <td>257</td>\n",
" <td>107, Webmaschine, OM 220 EOS</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2019-03-21</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Gegengewicht wieder anbringen</td>\n",
" <td>NaN</td>\n",
" <td>Gegengewicht an der Webmaschine abgefallen</td>\n",
" <td>2019-03-21</td>\n",
" <td>Reparatur UTT</td>\n",
" <td>Schraube ausgebohrt\\nGegengewicht wieder angeb...</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2019-03-21</td>\n",
" <td>2019-03-21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81</td>\n",
" <td>138</td>\n",
" <td>00138, Schärmaschine 9,</td>\n",
" <td>16</td>\n",
" <td>Schärmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2019-03-25</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>da ist etwas gebrochen. (Herr Heininger)</td>\n",
" <td>NaN</td>\n",
" <td>zentrale Bremsenverstellung linke Gatterseite ...</td>\n",
" <td>2019-03-25</td>\n",
" <td>Reparatur UTT</td>\n",
" <td>Bolzen gebrochen. Bolzen neu angefertig und di...</td>\n",
" <td>Vorwerk</td>\n",
" <td>Vorwerk</td>\n",
" <td>2019-03-25</td>\n",
" <td>2019-03-25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>82</td>\n",
" <td>0</td>\n",
" <td>Warenschau allgemein</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2019-03-25</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Klappbügel Portalkran H31 defekt</td>\n",
" <td>Warenschau allgemein</td>\n",
" <td>Allgemeine Reparaturarbeiten</td>\n",
" <td>2019-03-25</td>\n",
" <td>Reparatur UTT</td>\n",
" <td>Feder ausgetauscht</td>\n",
" <td>Warenschau</td>\n",
" <td>Warenschau</td>\n",
" <td>2019-03-25</td>\n",
" <td>2019-03-25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>76</td>\n",
" <td>0</td>\n",
" <td>Neben der Türe</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2019-03-22</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Schraube nix mer gut</td>\n",
" <td>Neben der Türe</td>\n",
" <td>Kettbaum</td>\n",
" <td>2019-03-25</td>\n",
" <td>Reparatur UTT</td>\n",
" <td>Schrauben ausgebohrt\\t\\nGewinde nachgeschnitten\\t</td>\n",
" <td>Vorwerk</td>\n",
" <td>Vorwerk</td>\n",
" <td>2019-03-25</td>\n",
" <td>2019-03-22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VorgangsID ObjektID HObjektText ObjektArtID \\\n",
"0 53 244 285 C, Webmaschine, SG 220 EMS 5 \n",
"1 58 257 107, Webmaschine, OM 220 EOS 3 \n",
"2 81 138 00138, Schärmaschine 9, 16 \n",
"3 82 0 Warenschau allgemein 0 \n",
"4 76 0 Neben der Türe 0 \n",
"\n",
" ObjektArtText VorgangsTypID VorgangsTypName \\\n",
"0 Greifer-Webmaschine 3 Reparaturauftrag (Portal) \n",
"1 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"2 Schärmaschine 3 Reparaturauftrag (Portal) \n",
"3 NaN 3 Reparaturauftrag (Portal) \n",
"4 NaN 3 Reparaturauftrag (Portal) \n",
"\n",
" VorgangsDatum VorgangsStatusId VorgangsPrioritaet \\\n",
"0 2019-03-19 5 0 \n",
"1 2019-03-21 5 0 \n",
"2 2019-03-25 5 0 \n",
"3 2019-03-25 5 0 \n",
"4 2019-03-22 5 0 \n",
"\n",
" VorgangsBeschreibung VorgangsOrt \\\n",
"0 Kupplung schleift NaN \n",
"1 Gegengewicht wieder anbringen NaN \n",
"2 da ist etwas gebrochen. (Herr Heininger) NaN \n",
"3 Klappbügel Portalkran H31 defekt Warenschau allgemein \n",
"4 Schraube nix mer gut Neben der Türe \n",
"\n",
" VorgangsArtText ErledigungsDatum \\\n",
"0 Kupplung defekt 2019-03-20 \n",
"1 Gegengewicht an der Webmaschine abgefallen 2019-03-21 \n",
"2 zentrale Bremsenverstellung linke Gatterseite ... 2019-03-25 \n",
"3 Allgemeine Reparaturarbeiten 2019-03-25 \n",
"4 Kettbaum 2019-03-25 \n",
"\n",
" ErledigungsArtText ErledigungsBeschreibung \\\n",
"0 Reparatur UTT NaN \n",
"1 Reparatur UTT Schraube ausgebohrt\\nGegengewicht wieder angeb... \n",
"2 Reparatur UTT Bolzen gebrochen. Bolzen neu angefertig und di... \n",
"3 Reparatur UTT Feder ausgetauscht \n",
"4 Reparatur UTT Schrauben ausgebohrt\\t\\nGewinde nachgeschnitten\\t \n",
"\n",
" MPMelderArbeitsplatz MPAbteilungBezeichnung Arbeitsbeginn ErstellungsDatum \n",
"0 Weberei Weberei NaT 2019-03-19 \n",
"1 Weberei Weberei 2019-03-21 2019-03-21 \n",
"2 Vorwerk Vorwerk 2019-03-25 2019-03-25 \n",
"3 Warenschau Warenschau 2019-03-25 2019-03-25 \n",
"4 Vorwerk Vorwerk 2019-03-25 2019-03-22 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "aba15410-474a-476d-bad5-c5ded56322be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2024-07-24 14:22:30 +0000 | io:INFO | Loaded file successfully.\n"
]
}
],
"source": [
"p_tl = io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_POST)\n",
"cands, texts = io.load_pickle(p_tl)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6c3d5ae4-d28b-4322-a41b-d0bfc647081b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7552,\n",
" 8192,\n",
" 119558,\n",
" 647,\n",
" 2310,\n",
" 48781,\n",
" 66323,\n",
" 8214,\n",
" 5405,\n",
" 108961,\n",
" 91173,\n",
" 2985,\n",
" 3881,\n",
" 9917,\n",
" 66751,\n",
" 85442,\n",
" 118602,\n",
" 7243,\n",
" 62416,\n",
" 979,\n",
" 214,\n",
" 103,\n",
" 123111,\n",
" 81133,\n",
" 88558,\n",
" 14319,\n",
" 14834,\n",
" 2424,\n",
" 101497,\n",
" 25341,\n",
" 69375)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cands[1654][1]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "62ef88ef-c1cb-4dc9-b88d-be335984b393",
"metadata": {},
"outputs": [],
"source": [
"def pre_filter_data(\n",
" data,\n",
" idx,\n",
" obj_id,\n",
"):\n",
" idx = int(idx)\n",
" obj_id = int(obj_id)\n",
" # data = data.copy()\n",
" cands_for_obj_id = cands[obj_id]\n",
" cands_choice = cands_for_obj_id[int(idx) - 1]\n",
" # data\n",
" data = data.loc[list(cands_choice)].sort_index() # type: ignore\n",
"\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0433246a-174b-4e6d-b432-9b3cd8861677",
"metadata": {},
"outputs": [],
"source": [
"# filtered = pre_filter_data(data, 2, 1654)\n",
"# filtered = pre_filter_data(data, 1, 1809)\n",
"filtered = pre_filter_data(data, 1, 59)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7c90c295-c7bc-4c99-ab74-a30f4015cde8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>VorgangsID</th>\n",
" <th>ObjektID</th>\n",
" <th>HObjektText</th>\n",
" <th>ObjektArtID</th>\n",
" <th>ObjektArtText</th>\n",
" <th>VorgangsTypID</th>\n",
" <th>VorgangsTypName</th>\n",
" <th>VorgangsDatum</th>\n",
" <th>VorgangsStatusId</th>\n",
" <th>VorgangsPrioritaet</th>\n",
" <th>VorgangsBeschreibung</th>\n",
" <th>VorgangsOrt</th>\n",
" <th>VorgangsArtText</th>\n",
" <th>ErledigungsDatum</th>\n",
" <th>ErledigungsArtText</th>\n",
" <th>ErledigungsBeschreibung</th>\n",
" <th>MPMelderArbeitsplatz</th>\n",
" <th>MPAbteilungBezeichnung</th>\n",
" <th>Arbeitsbeginn</th>\n",
" <th>ErstellungsDatum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>139361</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2021-09-16</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab muss getauscht werden</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2021-09-15</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>UTT-Reparatur</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2021-09-15</td>\n",
" <td>2021-09-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6096</th>\n",
" <td>151017</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2022-02-10</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2022-02-10</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2022-02-10</td>\n",
" <td>2022-02-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10470</th>\n",
" <td>587652</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2023-06-13</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2023-06-13</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2023-06-13</td>\n",
" <td>2023-06-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53974</th>\n",
" <td>260534</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2022-06-15</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2022-06-15</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2022-06-15</td>\n",
" <td>2022-06-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107542</th>\n",
" <td>531473</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2023-05-08</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2023-05-08</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>Sichtkontrolle durchgeführt\\nstab wurde getaus...</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2023-05-08</td>\n",
" <td>2023-05-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122411</th>\n",
" <td>513489</td>\n",
" <td>59</td>\n",
" <td>514 C , Webmaschine, DL 280 EMS Breite 280 Bj....</td>\n",
" <td>3</td>\n",
" <td>Luft-Webmaschine</td>\n",
" <td>3</td>\n",
" <td>Reparaturauftrag (Portal)</td>\n",
" <td>2023-02-15</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>NaN</td>\n",
" <td>Stabbreithalter Reparatur</td>\n",
" <td>2023-02-15</td>\n",
" <td>Intern UTT - Reparatur</td>\n",
" <td>Stab wurde getauscht</td>\n",
" <td>Weberei</td>\n",
" <td>Weberei</td>\n",
" <td>2023-02-15</td>\n",
" <td>2023-02-15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" VorgangsID ObjektID \\\n",
"188 139361 59 \n",
"6096 151017 59 \n",
"10470 587652 59 \n",
"53974 260534 59 \n",
"107542 531473 59 \n",
"122411 513489 59 \n",
"\n",
" HObjektText ObjektArtID \\\n",
"188 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"6096 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"10470 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"53974 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"107542 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"122411 514 C , Webmaschine, DL 280 EMS Breite 280 Bj.... 3 \n",
"\n",
" ObjektArtText VorgangsTypID VorgangsTypName \\\n",
"188 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"6096 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"10470 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"53974 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"107542 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"122411 Luft-Webmaschine 3 Reparaturauftrag (Portal) \n",
"\n",
" VorgangsDatum VorgangsStatusId VorgangsPrioritaet \\\n",
"188 2021-09-16 5 0 \n",
"6096 2022-02-10 5 0 \n",
"10470 2023-06-13 5 0 \n",
"53974 2022-06-15 5 0 \n",
"107542 2023-05-08 5 0 \n",
"122411 2023-02-15 5 0 \n",
"\n",
" VorgangsBeschreibung VorgangsOrt VorgangsArtText \\\n",
"188 Stab muss getauscht werden NaN Stabbreithalter Reparatur \n",
"6096 Stab wurde getauscht NaN Stabbreithalter Reparatur \n",
"10470 Stab wurde getauscht NaN Stabbreithalter Reparatur \n",
"53974 Stab wurde getauscht NaN Stabbreithalter Reparatur \n",
"107542 Stab wurde getauscht NaN Stabbreithalter Reparatur \n",
"122411 Stab wurde getauscht NaN Stabbreithalter Reparatur \n",
"\n",
" ErledigungsDatum ErledigungsArtText \\\n",
"188 2021-09-15 Intern UTT - Reparatur \n",
"6096 2022-02-10 Intern UTT - Reparatur \n",
"10470 2023-06-13 Intern UTT - Reparatur \n",
"53974 2022-06-15 Intern UTT - Reparatur \n",
"107542 2023-05-08 Intern UTT - Reparatur \n",
"122411 2023-02-15 Intern UTT - Reparatur \n",
"\n",
" ErledigungsBeschreibung \\\n",
"188 UTT-Reparatur \n",
"6096 Stab wurde getauscht \n",
"10470 Stab wurde getauscht \n",
"53974 Stab wurde getauscht \n",
"107542 Sichtkontrolle durchgeführt\\nstab wurde getaus... \n",
"122411 Stab wurde getauscht \n",
"\n",
" MPMelderArbeitsplatz MPAbteilungBezeichnung Arbeitsbeginn \\\n",
"188 Weberei Weberei 2021-09-15 \n",
"6096 Weberei Weberei 2022-02-10 \n",
"10470 Weberei Weberei 2023-06-13 \n",
"53974 Weberei Weberei 2022-06-15 \n",
"107542 Weberei Weberei 2023-05-08 \n",
"122411 Weberei Weberei 2023-02-15 \n",
"\n",
" ErstellungsDatum \n",
"188 2021-09-16 \n",
"6096 2022-02-10 \n",
"10470 2023-06-13 \n",
"53974 2022-06-15 \n",
"107542 2023-05-08 \n",
"122411 2023-02-15 "
]
},
"execution_count": 15,
"metadata": {},
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}
],
"source": [
"filtered"
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},
{
"cell_type": "code",
"execution_count": 10,
"id": "51c14b60-a979-4097-a364-3585cbb00b8b",
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{
"data": {
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"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",
" dtype='object')"
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},
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"metadata": {},
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],
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"filtered.columns"
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},
{
"cell_type": "code",
"execution_count": 11,
"id": "a70700f3-f872-409b-9f2b-d6e36bed71f1",
"metadata": {},
"outputs": [],
"source": [
"filtered['delta'] = filtered['ErledigungsDatum'] - filtered['ErstellungsDatum']\n",
"filtered['delta'] = filtered['delta'].dt.days"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f13099a1-539d-4f92-8c18-315b02a061af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 70,
"id": "65a14689-081d-42ed-be6a-e49731a14f59",
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "03201127-e31f-43a0-9580-f74c52052ac9",
"metadata": {},
"outputs": [],
"source": [
"MARKERS = {\n",
" 'size': 8,\n",
" 'color': 'red',\n",
" 'symbol': 'cross',\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "d221446d-6c83-45be-9fcb-fd926ac2a925",
"metadata": {},
"outputs": [],
"source": [
"HOVER_DATA_DELTA = {\n",
" 'ErstellungsDatum': '|%d.%m.%Y',\n",
" 'ErledigungsDatum': '|%d.%m.%Y',\n",
" 'VorgangsDatum': '|%d.%m.%Y',\n",
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"}"
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},
{
"cell_type": "code",
"execution_count": 116,
"id": "0990eb39-a5ae-4f05-88d8-1a2624284a2d",
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"2021-12-16T00:00:00",
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[
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"2022-01-26T00:00:00",
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"2021-12-15T00:00:00",
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[
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"2020-03-23T00:00:00",
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[
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"2020-06-24T00:00:00",
"Kettbaum Gewinde defekt"
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[
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"2021-01-06T00:00:00",
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[
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"2022-09-12T00:00:00",
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[
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"2020-01-09T00:00:00",
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[
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[
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"2021-07-06T00:00:00",
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[
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"2020-02-20T00:00:00",
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"2022-04-01T00:00:00",
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[
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"2021-05-05T00:00:00",
"Adabter Scheibe Gewinde schneiden. Liegt in der Schlosserei auf der Werkbank."
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"2022-05-23T00:00:00",
"Kettbaum vor der Schlosserei neue Löcher bohren und Gewinde schneiden"
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"2022-01-30T00:00:00",
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"<div> <div id=\"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div> <script type=\"text/javascript\"> require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById(\"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\")) { Plotly.newPlot( \"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\", [{\"customdata\":[[\"2020-12-14T00:00:00\",\"2020-12-10T00:00:00\",\"Kettbaum schrauben abgerochen schrauben ausboren\"],[\"2021-12-17T00:00:00\",\"2021-12-16T00:00:00\",\"Neue L\\u00f6cher bohren und Gewinde schneiden. Kettbaum liegt vor der Schlosserei\"],[\"2022-06-22T00:00:00\",\"2022-06-21T00:00:00\",\"Kettbaum Gewinde nach schneiden (liegt vor Schlosserei)\"],[\"2022-12-13T00:00:00\",\"2022-12-12T00:00:00\",\"Kettbaum Gewinden kaputt, neue Gewinden machen bitte\"],[\"2022-02-03T00:00:00\",\"2022-02-03T00:00:00\",\"Gewinde schneiden. Kette liegt vor der Schlosserei\"],[\"2022-03-28T00:00:00\",\"2022-03-28T00:00:00\",\"Kettbaum Schraube defekt. Liegt vor Schlosserei\"],[\"2022-01-26T00:00:00\",\"2022-01-26T00:00:00\",\"2 Kettb\\u00e4ume Schrauben ausbohren. Kettb\\u00e4ume liegen vor der Schlosserei\"],[\"2021-12-16T00:00:00\",\"2021-12-15T00:00:00\",\"Kettbaum Schrauben gebrochen bitte rausbohren\"],[\"2023-01-23T00:00:00\",\"2023-01-21T00:00:00\",\"Schraube abgebrochen! Kettbaum liegt vor der Schlosserei\"],[\"2021-12-13T00:00:00\",\"2021-12-12T00:00:00\",\"Kettbaum bei seiten Gewinde neu schneiden\"],[\"2020-02-10T00:00:00\",\"2020-02-10T00:00:00\",\"Kettb\\u00e4ume vor Schlosserei\"],[\"2019-11-18T00:00:00\",\"2019-11-13T00:00:00\",\"3*Kettbaum Gewinde nachschneiden\"],[\"2019-11-11T00:00:00\",\"2019-11-11T00:00:00\",\"2X Kettbaum Gewinden nachschneiden\"],[\"2023-01-26T00:00:00\",\"2023-01-25T00:00:00\",\"Kettbaum Gewinde nach schneiden. Kette liegt vor der Schlosserei.\"],[\"2020-03-23T00:00:00\",\"2020-03-23T00:00:00\",\"Bei allen 3 Kettb\\u00e4umen Gewinde neu Schneiden. Ketten liegen vor der Schlosserei\"],[\"2020-03-23T00:00:00\",\"2020-03-22T00:00:00\",\"Neues Gewinde Schneiden. Kette liegt vor der Schlosserei\"],[\"2020-06-24T00:00:00\",\"2020-06-24T00:00:00\",\"Kettbaum Gewinde defekt\"],[\"2021-01-08T00:00:00\",\"2021-01-06T00:00:00\",\"Kettbaum gewinde nachschneiden (Kettbaum vor Schlosserei)\"],[\"2022-09-16T00:00:00\",\"2022-09-12T00:00:00\",\"Kettbaum Schrauben rund 2X liegen vor Schlosserei\"],[\"2020-01-10T00:00:00\",\"2020-01-09T00:00:00\",\"Gewinde vom Kettbaum nachschneiden (Kette liegt vor Schlosserei)\"],[\"2020-01-13T00:00:00\",\"2020-01-10T00:00:00\",\"Kettbaum Schrauben pr\\u00fcfen\"],[\"2021-07-08T00:00:00\",\"2021-07-06T00:00:00\",\"Neues Gewinde am Kettbaum Schneiden. Kette liegt vor der Schlosserei\"],[\"2020-02-20T00:00:00\",\"2020-02-20T00:00:00\",\"Gewinde defekt. Kette liegt vor der Schlosserei.\"],[\"2022-04-01T00:00:00\",\"2022-04-01T00:00:00\",\"Kettbaum Gewinde Schneiden, liegt vor der Schlosserei. ( Volle Kette) . 471 Wartet auf die Kette zum Andrehen.\"],[\"2021-05-05T00:00:00\",\"2021-05-05T00:00:00\",\"Adabter Scheibe Gewinde schneiden. Liegt in der Schlosserei auf der Werkbank.\"],[\"2022-05-24T00:00:00\",\"2022-05-23T00:00:00\",\"Kettbaum vor der Schlosserei neue L\\u00f6cher bohren und Gewinde schneiden\"],[\"2022-01-31T00:00:00\",\"2022-01-30T00:00:00\",\"Gewinde vom Kettbaum defekt. Neue L\\u00f6cher bohren + Gewinde schneiden\"],[\"2023-03-13T00:00:00\",\"2023-03-12T00:00:00\",\"Kettbaum-Schrauben abgebrochen !\"],[\"2022-03-24T00:00:00\",\"2022-03-24T00:00:00\",\"Kettbaum neue L\\u00f6cher bohren und Gewinde schneiden\"],[\"2022-04-26T00:00:00\",\"2022-04-25T00:00:00\",\"Kettbaum Gewinde defekt (liegt vor Schlosserei)\"],[\"2023-05-11T00:00:00\",\"2023-05-11T00:00:00\",\"Gebrochene Kettbaum Scheibe auswechseln. Steht vor der 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",
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EkN8eFCGH/L5wQeQTtD3xv1hk2CgCyO9GlTuozSK//SmXt/L7pZdPyMYtO1ukbrt1lTy8bZPMmD6tjd7FS5flod37os+l3398ZEyefvZQ9N7dd66R+wbXR39Ofl59/Mj2TbJuzS2tuOn302PiNQ8eHo/mJOcjv/053LqZIL91iTHeJQHkt0varFWUAPK7KDnm2SSA/LZJl9imCCC/TZG0F+fHP+6Ti5f7ZN61E/YW8SwybU88KwjpdCSA/OZg+EoA+e1PZbyV388felEWLpgrK5cvkVg2z587pyWwFcKkhE7LcSWw1SsW3jFyNWfv174pv7PhCzJ71kx59fXTMrh9jwwPDURrqVe3uck1V624uU2Yx/GR3/4cbt1MkN+6xBjvkgDy2yVt1ipKAPldlBzzbBJAftukS2xTBJDfmiSVf+7XnMNwbQLIb21kTKiAAPK7AugsmYsA8jsXJieDvJXf6d0rGT5+9JW2291KUt+wcH40NPmeujX+3IEjHW+Kp+PGAj0ps3vJb5XHa6fOTpHqyG8n59XqIshvq3gJXpIA8rskQKY7IYD8doKZRTQJIL81gTG8EgLIb03sfSIyqTmH4doEkN/ayJhQAQHkdwXQWTIXAeR3LkxOBgUjv9NCOvlxWoyrjx/c9V4rlPj1zJM7Wje7k59/68LbsnnHE7J1cH3bze+4XYoam2xrkm6Jct28a2Rk11ZZfP2CKCw3v52cWyuLIL+tYCWoIQLIb0MgCWOVAPLbKl6CFySA/C4IjmlOCSC/NXEjvzWBFRuO/C7GjVluCSC/3fJmtfwEkN/5WdkeGYT8Vje594yMyd6d90atStK3r9PyO74RHvfxVvOHhkfbJHUMttctbzUm2RblU0sWRf3F71i7uiXK1dpjB460ckN+2z6y9uIjv+2xJXJ5Asjv8gyJYJ8A8ts+Y1bQJ4D81mfGDPcEkN+azJHfmsCKDUd+F+PGLLcEkN9uebNafgLI7/ysbI/0Xn53EtedHkipQMV9v1VPb9UOJZbfnVqbqPEqztk3zme2R4ll+hc+v2qK/E7fHEd+2z6y9uIjv+2xJXJ5Asjv8gyJYJ8A8ts+Y1bQJ4D81mfGDPcEkN+azJHfmsCKDUd+F+PGLLcEkN9uebNafgLI7/ysbI/0Wn73urGdBNOp7UmyL3csv5M3tvOK71iSxzI9fatcye+hx0Zl2z0botYnyG/bR9ZefOS3PbZELk8A+V2eIRHsE0B+22fMCvoEkN/6zJjhngDyW5M58lsTWLHhyO9i3JjllgDy2y1vVstPAPmdn5Xtkd7K73Srk14g0vI72apk5fIlko7Vq9WJktmHDo/Ll9b9ZrSkivXA8Kg8OjQQye20kE+vjfy2fWTtxUd+22NL5PIEkN/lGRLBPgHkt33GrKBPAPmtz4wZ7gkgv/WYT4hIv94URhcggPwuAI0pzgkgv50jZ8GcBJDfOUE5GOat/O7U2iT9cMmYT1pAq88rSb1xy85oSHJe3Kbk2PGTbXjjlinqk6qv98HD46330w/LTD5Qc9nSRa1+32oC8tvBqbW0BPLbEljCGiGA/DaCkSCWCSC/LQMmfCECyO9C2JjkmADy2zFwlstFAPmdCxODKiaA/FYFUP8cRr0mK64GyycJIL/9OQ/eym9/EOllgvzW4+XTaOS3T9UglzQB5DdnIgQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPyuf43rsEPkt8irJ/tk5kyRuR9Bfvt0ppHf/lQD+W24Fshvw0AdhkN+O4TNUtoEkN/ayJhQAQHkdwXQWTKTAPI7ExEDPCCA/PagCKQwhQDym0MRAgHkt8jxE/0yZ/akzJuH/PbpzCK//akG8ttwLZDfhoE6DIf8dgibpbQJIL+1kTGhAgLI7wqgs2QmAeR3JiIGeEAA+e1BEUgB+c0ZCJIA8hv57evBRX77Uxnkt+FaIL8NA3UYDvntEDZLaRNAfmsjY0IFBJDfFUBnyUwCyO9MRAzwgADy24MikALymzMQJAHkN/Lb14OL/PanMshvw7VAfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS3B0UgBeQ3ZyBIAshv5LevBxf57U9lvJffr75+WnY/tV+G7x+Q2bNmTiF38dJleWj3vujzD2/bJDOmT4v+/PyhF+XBXe99ftnSRbJ3571t81Xcwe175My5N+W6edfIyK6tsvj6BdH4x0fG5OlnD7Wt9cj2TbJuzS2tz3XLC/ntz+HWzQT5rUuM8S4JIL9d0matogSQ30XJMc8mAeS3TbrENkUA+W2KJHFMEqDnt0maxLJFoPHye1Lk9b/rk6t+TuSaOfT8tnXOisRFfhehZmeOt/L7rQtvy+YdT8ix4yc7ymuFIxbfBw+Py223rmrJ75dePiF7RsZawlvJ7LNvnG+9r8T1A8Oj8ujQQEt4J/Gq8ep13+D6KdSz8kJ+2zmoLqIiv11QZo2iBJDfRckxzyUB5LdL2qyVlwDyOy8pxlVJAPldJX3W7kYA+c3ZCIFA4+V3VKQ+6ZNJQX37dWKR3/7Uw1v5HSPqdfNbSeobFs6Pho4ffSWS2+qlboKvWnFz66Z2UnYvmHdt9P4da1fLyuVLOlail/zOygv57c/h1s0E+a1LjPEuCSC/XdJmraIEkN9FyTHPJgHkt026xDZFwJn8nhT565MiV83sl/lzJ0ylT5yaEkB+17SwNdsW8rtmBa3RdpDf/hQzWPmdFNSqxUkv+R3f1t46uF5uuvGjrRvlcRmSt8bV59JtT9ItT9QY2p74c4hNZYL8NkWSODYIIL9tUCWmaQLIb9NEiWeCAPLbBEVi2CbgTH6LyIn/3i+zZolcNw/5bbuuocdHfodewWbkj/xuRp1D3CXy25+qBSm/lex+7dTZVluSpPxWPb/THyfl95zZH27rIR63Tpk/d07HNidxb/DhoYG2m+LIb38OsalMkN+mSBLHBgHktw2qxDRNAPltmijxTBBAfpugSAzbBJDftgkTvwgB5HcRasxxTQD57Zo46+UlgPzOS8r+uCDld6cHUipU8Q1u9WfV2kT1Ao9f8UMt1cfpB2ime4SnscftVXjgpf0DWeUKyO8q6bN2FgHkdxYh3veBAPLbhyqQQ5oA8pszEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+cg5XcaX/qmd/p9dUv7689/S7Zt3iCX3nlHhh4blW33bGg97FLJ7+cOHGk9EBP57c8BdZkJ8tslbdbSJYD81iXG+CoIIL+roM6aWQSQ31mEeN8HAshvH6pADmkCyG/ORAgEkN8hVKmZOSK//al77eV3suVJ/IBLdZP77BvnOz4gU40/dHhcvrTuN6MqJR+Wufj6Ba3K0fbEn0NsKhPktymSxLFBAPltgyoxTRNAfpsmSjwTBJDfJigSwzYBZ/J7UuTU3/fLh2ZMyDXX2N4V8UMngPwOvYLNyB/53Yw6h7hL5Lc/VfNWfsfS+tjxky1ad9+5pmNf7m49vtXcuN1JUlzHfb7jtijJuOn31OLPPLmj1e87K6/Tb170p7pkokUA+a2Fi8GOCSC/HQNnuUIEkN+FsDHJMgHkt2XAhDdCwJn8Vtn29f1zk8jJ6P94QaAXAeQ35yMEAsjvEKrUzByR3/7U3Vv57Q8ivUyQ33q8fBqN/PapGuSSJoD85kyEQAD5HUKVmpcj8rt5NQ9xx07ld4iAGpTzmbN9cuWVIh+5tvq/nUB+N+jgBbxV5HfAxat56shvfwqM/DZcC+S3YaAOwyG/HcJmKW0CyG9tZEyogADyuwLoLJlJAPmdiYgBHhBAfntQBE9SOPm3fXJlv8jHP4b89qQkpOE5AeS35wVqcHrIb3+Kj/w2XAvkt2GgDsMhvx3CZiltAshvbWRMqIAA8rsC6CyZSQD5nYmIAR4QQH57UARPUkB+e1II0giGAPI7mFI1LlHktz8lR34brgXy2zBQh+GQ3w5hs5Q2AeS3NjImVEAA+V0BdJbMJID8zkTEAA8IIL89KIInKSC/PSkEaQRDAPkdTKkalyjy25+SI78N1wL5bRiow3DIb4ewWUqbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFMGTFJDfnhSCNIIhgPwOplSNSxT57U/JvZffr75+WnY/tV+G7x+Q2bNmRuQuXrosD+3eJwcPj7dIPvPkDlm5fEn08VsX3pbNO56QY8dPRh8vW7pI9u68N5qffi8OkByj1hzcvkfOnHszevuR7Ztk3ZpbplTtpZdPyMYtOyW5NvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYoA8tsUyfDjnHlD5Iq+fpn7kYnKN8MDLysvAQnkIID8zgGJIZUQQH5Xgr3jot7K76SkTorpWG5/df8Lsvmu22XG9GmiJPTQ8KiM7Noqi69fEH186vQbLWH9/KEXZfzoK/Lwtk3R+PRLvf/aqbNy3+D6lhzfOrg+kulxHvHH8dxYfKuPkd/+HOgymSC/y9Bjrm0CyG/bhIlvggDy2wRFYpgmgPw2TZR4Ngggv21QDTRmX59I9KxLHngZaAVJ2zEB5Ldj4CyXmwDyOzcq6wO9ld/xzjvd/E5T6Saok6J6z8hY6/Z3cr6aO/TYqGy7Z0MkztV6DwyPyqNDA9HH8S3zVStubsn0OKft/+FOuX94VJJinJvf1s+stQWQ39bQEtgAAeS3AYiEsE4A+W0dsbMFJmRC+qXf2Xo2F0J+26RLbFMEkN+mSBLHJAFufpukSSxbBJDftsgStywB5HdZgubm10J+p4V1Gs/jI2Ny9o3zHW9+J299x/PU+EPf/rPoJrl6JduuJNeac/XMqL0K8tvcgawyEvK7SvqsnUUA+Z1FiPd9IID89qEK5XNQdw0nJyalX1mPGryQ3zUoYgO2gPxuQJED3CLyO8CiNTBl5HcDix7IlpHf/hQqePnd6WZ2jFeJ7Qd37Wvr+Z1En771Hb+nWprs+f1vyA/f+nHU9zvu+Z0e3+nGOTe//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOb/mIb/9qgfZNIMA8rsZdQ5tl8jv0CrWzHyR382sewi7Rn77U6Wg5XcsvufPnRP16+72imR2h7Yn6oa3eiXnptusxIJ7/drV8plP3tT2IMzkenHfb+S3P4dbNxPkty4xxrskgPx2SZu1ihJAfhcl59c85Ldf9SCbZhBAfjejzqHtEvkdWsWamS/yu5l1D2HXyG9/qhSs/M4rvhXqTje8u7VKUaL8uQNH2lqkdJLkcVzanvhzmMtmgvwuS5D5Ngkgv23SJbYpAshvUySrjYP8rpY/qzeTAPK7mXX3fdfIb98rRH6KAPKbc+ArAeS3P5UJUn73anWi0Kp2JwsXzJWVy5dEpNXHYweOtD3wspvQVlJ8cPseGR4aiOYnb36vW3NLW+Voe+LPQTaRCfLbBEVi2CKA/LZFlrgmCSC/TdKsNtbExIT09/PAy2qrwOpNIoD8blK1w9kr8jucWjU5U+R3k6vv996R3/7Ux1v5HYvlY8dPtmjdfeeaqEVJLKhVP+7kq9v7y5YuahPf3dqgxLHU+xu37GyFjnt+p8uG/PbnIJvIBPltgiIxbBFAftsiS1yTBJDfJmkSyxQBHnhpiiRxbBJAftukS+yiBJDfRckxzyUB5LdL2qylQwD5rUPL7lhv5bfdbduLTs9ve2xtR0Z+2yZM/DIEkN9l6DHXFQHktyvSrKNDAPmtQ4uxVRFAfpslPzEhUpN/vGIWjGY05LcmMIZXQgD5XQl2Fs1BAPmdA5KjIchvw6CR34aBOgyH/HYIm6W0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPltuAgTIlKPzk2GweiFQ37r8WJ0NQSQ39VwZ9VsAsjvbEauRiC/DZNGfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyITb1TIAAAgAElEQVS/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyK/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyK/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJwYrvx8fGZOnnz3UAvLMkztk5fIl0cdvXXhbNu94Qo4dP9l6/7p518jIrq2y+PoFU96/+841ct/g+ja4L718QjZu2Rl9btnSRbJ3570ye9ZMSX5evXfbravk4W2bZMb0adFY5LfxM+osIPLbGWoWKkAA+V0AGlOcE0B+O0fOgjkIIL9zQGJI5QSQ35WXgAQ6EEB+cyxCIID8DqFKzcwR+e1P3YOU30puf3X/C7L5rtsj6fzq66flgeFReXRooE1ubx1c3xLiMfKLly7LQ7v3yaoVN8u6NbdI+mM1TgnuPSNjLeGdLNfzh16UhQvmRnHjufPnzmnJc+S3P4dbN5NQ5ffEhEg/T7PXLXdw45HfwZWskQkjvxtZdu83jfz2vkQkKCLIb46BjwSQ3z5WhZzSBJDfnAlfCSC//alMkPI7jS++6R3L7vTHyfGd3lO3yNVL3f5W7w89Nirb7tkQifSsl5Lh40dfad3+Rn5nEfP3/VDl93tE+0Rk0l+4ZFaaAPK7NEICOCCA/HYAmSW0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPntQRFIoSMB5Lc/B6MW8lvd1B4aHu3a1iTZ8kShV8L6wV37RLVKuenGj7bJbnWLfHD7Hjlz7s1WlTq1RYnfTIpz9Tnktz+HWzcT5LcuMca7JID8dkmbtYoSQH4XJcc8mwSQ3zbpEtsUAeS3KZLEMUkA+W2SJrFsEUB+2yJL3LIEkN9lCZqbH7T8TorqZM/vNB4lu8cOHGm1MYnbpKhxx078jSTlthLpzx040rrJHd8UX792ddQmJfnq1B4F+W3ucLqOhPx2TZz1dAggv3VoMbYqAsjvqsizbi8CyG/ORwgEkN8hVKl5OSK/m1fzEHeM/A6xas3IGfntT52Dlt8xxl5tTtSYZCuTOVfPbLvpne7bnZbfan66tYn6XPq2eZwL8tufw62bCfJblxjjXRJAfrukzVpFCSC/i5Jjnk0CyG+bdIltigDy2xRJ4pgkgPw2SZNYtgggv22RJW5ZAsjvsgTNza+F/FY4VPuRGxbOn3I7Oy2/1ce7n9ovw/cPyOxZMyOSSbl9+twPO77/2qmzrYdadhPfKhby29zhdB0J+e2aOOvpEEB+69BibFUEkN9VkWfdXgSQ35yPEAggv0OoUvNyRH43r+Yh7hj5HWLVmpEz8tufOgcpv1XbksPfPSpf+fLaiGTc/mR4aEBWLl8S3cpWL/XntNy+9M47snnHExK3MUnf/E5/nL5V3qnVSbKcyG9/DrduJmHLb93dMj40Asjv0CrWzHyR382su++7Rn77XiHyUwSQ35wDHwkgv32sCjmlCSC/ORO+EkB++1OZIOV3LKgPHh5vkUz2/E4/tHLZ0kWtft9JWR4/1DL9QMtYeB87fjKK/8j2Ta0b5eqG+dPPHmqrYPKBmshvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CXSS3xcv98ub/zApH/ygyEeunbS5PLEhkIsA8jsXJgY5JoD8dgyc5QoRQH4XwsYkBwSQ3w4g51wiSPmdc2+VDEN+V4LdyKLIbyMYCWKJAPLbEljCGiWA/DaKk2CGCHSS32+dF/nPY/3ya78q8ull70qf9BlajTAQKEYA+V2MG7PsEkB+2+VLdDMEkN9mOBLFPAHkt3mmRSMiv4uS6zIP+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhLoJr//8D/3y+duEfnMp5HfmRAZYJ0A8ts6YhYoQAD5XQAaU5wTQH47R86COQkgv3OCcjAM+W0YMvLbMFCH4ZDfDmGzlDYB5Lc2MiZUQAD5XQF0lswkgPzORMQADwggvz0oAilMIYD85lCEQAD5HUKVmpkj8tufuiO/DdcC+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhJAfmciYoAHBJDfHhSBFJDfnIEgCSC/gyxbI5JGfvtTZuS34Vogvw0DdRgO+e0QNktpE0B+ayNjQgUEkN8VQGfJTALd5Pc3/49+Wfmv++STN/+Mnt+ZFBlgmwDy2zZh4hchwM3vItSY45oA8ts1cdbLSwD5nZeU/XG55fdLL5+QjVt2tjK6bt41MrJrqyy+foH9LDus8PjImDz97KHWO888uUNWLl/S+jiZ77Kli2Tvzntl9qyZ0fvPH3pRHty1rzX2ke2bZN2aW6ascvHSZXlo93vjHt62SWZMnybpdeNJ8frI70qOg5FFkd9GMBLEEgHktyWwhDVKAPltFCfBDBHoJL8v/7RPLlzokw98YEKunmVoIcJAoAQB5HcJeEy1RgD5bQ0tgQ0SQH4bhEkoowSQ30ZxlgqWS34r4fv9l0+0CeS3Lrwtm3c8Ib+4fIncN7i+VBK6k9XaX93/gmy+6/ZISL/6+ml5YHhUHh0aiGR8+mMlu8ePvhIJbPXa+7Vvyu9s+EIkw+N9bB1c3ybPY/F98PC43Hbrqpb8Tueq1tr91H4Zvn8giof81q2mP+PzyO+f/ETkJ//YJ5MiMvPnRK66Sv2JFwTsE0B+22fMCuUJIL/LMySCeQKd5Lf5VYgIgXIEkN/l+DHbDgHktx2uRDVLAPltlifRzBFAfptjWTZSpvxWN6j3jIy1ie940W7iuGxSuvPTeSjZ/dqpsy0pn5bhyfix5F614ua2299K+N+wcH40NBbnSrSnX/G4+OY48lu3ev6MzyO/T58R+cZzV0hfX5/c8cV35aMLkN/+VLDemSC/613fuuwO+V2XStZrH8jvetWzrrtBfte1smHvC/kddv2akj3yuymVDm+fyG9/apYpv9NyN516WjRXsTUl6IeGR1ttWFTO6hXfSO8l6ZUYH9y+R4aHBlo3v5Pzk7fG0/I7fetbrYn8ruIEmFkzj/z+u9Miz/zhFdLf1yf//svvysc+ivw2Q58oWQSQ31mEeN8HAshvH6pADmkCyG/ORAgEkN8hVKl5OSK/m1fzEHeM/A6xas3IGfntT517yu/4VvQda1e3tQRJpq/E83MHjnRtC2Jzq7G4PnPuTUn2/E4L+07yO/7cseMnJdnzOy3ze8nvTn8xgPy2WXG7sZHfdvkSvRwB5Hc5fsx2QwD57YYzq+gRQH7r8WJ0NQSQ39VwZ9XeBJDfnJAQCCC/Q6hSM3NEfvtT957yWwniocdGZds9G7o+2LLT7WfX20vLbZ2b3+m2J90eaJnu+92tHQzy23X1za2H/DbHkkjmCSC/zTMlonkCyG/zTIlYngDyuzxDItgngPy2z5gV9Akgv/WZMcM9AeS3e+asmI8A8jsfJxejaiG/FajkLWydnt9qbq/WLZ1ufnfrE65iIb9dHFs7a+SR36rn97NjV4r6QXC96vlN2xM7xSDqFALIbw5FCASQ3yFUqXk5Ir+bV/MQd4z8DrFq9c8Z+V3/Gtdhh8jvOlSxnntAfvtT1yDlt7ptfvi7R+UrX14bkUz37U4/4DIpsC+98458df8Lsvmu20X18I5vja9fu7rtgZdxiTrJ714PAUV++3O4dTPJI7/f/keRt9/uj0J/+KpJueoqen7rcmZ8MQLI72LcmOWWAPLbLW9Wy0cA+Z2PE6OqJYD8rpY/q3cmgPzmZIRAAPkdQpWamSPy25+6Z8rvzTueENUXu9dr2dJFsnfnvTJ71kwnO4tvXh88PN5aL9nzW31SCeqNW3ZG76fzS7c2Sfb8Tm8gLb97PTxTzUV+OzkCVhbJkt8XL07KT3/WJx92c8yt7JGg4RJAfodbuyZljvxuUrXD2SvyO5xaNTlT5HeTq+/v3pHf/taGzN4ngPzmNPhKAPntT2V6ym9/0gwnE+R3OLVKZ5olv8/9Q59cuNAnP3/Tu9InfeFulMyDJID8DrJsjUsa+d24kgexYeR3EGVqfJLI78YfAS8BIL+9LAtJpQggvzkSvhJAfvtTGeS34Vogvw0DdRguS36fPdcnb/1IZMknJpDfDuvCUu8RQH5zEkIggPwOoUrNyxH53byah7hj5HeIVat/zsjv+te4DjtEftehivXcA/Lbn7oivw3XAvltGKjDcMhvh7BZSpsA8lsbGRMqIID8rgA6S2YSQH5nImKABwSQ3x4UgRSmEEB+cyhCIID8DqFKzcwR+e1P3TN7fg//3tdl6He/1LWft+qBnTXGn+3azwT5bZ+xrRWQ37bIEtcEAeS3CYrEsE0A+W2bMPGLEEB+F6HGHNcEkN+uibNeHgLI7zyUGFM1AeR31RVg/W4EkN/+nI1M+e3jAy/9wTc1E+S3z9XpnVse+f2jCyKf+B9oexJulcPNHPkdbu2alDnyu0nVDmevyO9watXkTJHfTa6+v3tHfvtbGzJ7nwDym9PgKwHktz+VyZTfWbe6bd38funlE7Jxy84WqdtuXSUPb9skM6ZPk1dfPy2D2/fImXNvtpGMx1x65x1JSvtlSxfJ3p33tm6vPz4yJk8/e6g195Htm2TdmlvaYiXXT85Pr53MSwVAfvtzuHUzyZLfP/6xyMV3+mXutRPSx/MudfEyviQB5HdJgEx3QgD57QQzi2gSQH5rAmN4JQSQ35VgZ9EMAshvjkgIBJDfIVSpmTkiv/2pu7c9v58/9KIsXDBXVi5fIhcvXZaHdu+T+XPnyH2D6zvSU0L7hoXzI4mtxPWp02+0hLaKNX70lUieq9fer31TfmfDFyIZHsvs4aGBaC31UvP3jIy1CfN40WRe6nNqXfWK80J++3O4dTPJkt+68RgPAZMEkN8maRLLFgHkty2yxC1DAPldhh5zXRFAfrsizTo6BJDf7bQu/2xSfvpOf+uT0z44KR+4UocoY20QQH7boEpMEwSQ3yYomonhrfxOby8psNXt7+RLCezdT+2X4fsHOvYm7yWzY7G+asXNkSxXN9mHHhuVbfdskMXXL8iknM4L+Z2JzNsByG9vS0Ni//wXbchvjkEIBJDfIVSpeTkiv5tX8xB3jPwOsWr1zxn53V7jM2f75NB/7ZNLl/pk+gyRNf9mQq6bN1n/g+D5DpHfnheowekhv/0pfm75rW44n33jfOv2tLqJffDwuFw37xoZ2bU1lygus+30DetkrOSt705rJHNPi3Mlu1WLlK2D66Ob351aqtx955qON8473UhHfpepcrVzkd/V8mf13gSQ35yQEAggv0OoUvNyRH43r+Yh7hj5HWLV6p8z8ru9xn9/WuQPv36FXLzYJx/6kMi//9K7suA65HfV/yUgv6uuAOt3I4D89uds5JLfaUGsblI/d+BIJMJ/cOJk689psWxqm71ubve69a1uZT+4a5+ke36nxbn6OG5bktyb2k+89/VrV7f1BY/7htPz21SVq4+D/K6+BmTQnQDym9MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfiO/QzjlyO8QqtTMHJHf/tQ9t/xOtgJJ3sLOajlSdqtKRg8Nj3a8XZ5uWdJtrW7yvNON8LT8VjF7tVyh7UnZCvszH/ntTy3IZCoB5DenIgQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPxGfodwypHfIVSpmTkiv/2pey75HUvmO9aulptu/Ghbm5Bet7LLbrOX+Fax867dqY93t1YonWS+EtyvnTrbsfVJejxtT8pWvbr5yO/q2LNyNgHkdzYjRlRPAPldfQ3IYCoB5DenIgQCyO8QqtS8HJHfyO8QTj3yO4QqNTNH5Lc/dc8lv1W6yV7YcQ/suCXILy5f0lEMl9lmltjudetbyeqFC+ZGPbzVS308duCI7N15b/RAzF79w9N9vNMtX/7gPx2QWz+7otXjPC3Rkd9lql7tXOR3tfxZvTcB5DcnJAQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPxur/GPLvTLP/xQZHJSpK9P5CPXTsrVs+j5XfV/CcjvqivA+t0IIL/9ORu55bfrlOOe2sl1kw/X7NWKJP3QymTP71hmHzt+sm1Lyd7d6TGPbN/U6vetpPzGLTtbc+n57fpk2FsP+W2PLZHLE0B+l2dIBPsEuslv9Wthn/3lWQECHQkgvzkYIRBAfodQpebliPzuUPOk6+aHGy/+o0B+e1EGkuhAAPntz7HwVn77g0gvE25+6/HyaTTy26dqkEuaAPKbMxECgbT8Vr8fHv72FdJ/hcivf+5dBHgIRaxhjsjvGha1hltCftewqDXYEvK7BkVswBaQ3w0ocqBbRH77U7hc8rvbben0NpI3pP3ZottMkN9ueZtcDfltkiaxTBNAfpsmSjwbBDrJ77HnrpArPiDyxduR3zaYEzObAPI7mxEjqieA/K6+BmQwlQDym1MRAgHkdwhVamaOyG9/6p5Lfqt0VRuSGxbOb7X/UJ9LPgjzU0sWyUO798mqFTe3jfFnq24yQX674WxjFeS3DarENEUA+W2KJHFsEkB+26RL7KIEkN9FyTHPJQHkt0varJWXAPI7LynGVUkA+V0lfdbuRQD57c/5yCW/1c3vocdGZds9G1oPeoy3oHpvv3bqbPTAS9UP+7kDR+ThbZtkxvRp/uzSYSbIb4ewDS+F/DYMlHBGCSC/jeIkmCUCyG9LYAlbigDyuxQ+JjsigPx2BJpltAggv7VwMbgiAsjvisCzbCYB5HcmImcDSsvvpPA+fe6Hsvup/TJ8/4DMnjXT2SZ8Wgj57VM19HJBfuvxYrRbAshvt7xZrRgB5HcxbsyySwD5bZcv0c0QQH6b4UgUswSQ32Z5Es0OAeS3Ha5ELU8A+V2eoakIueR3sr3JyuVL2tZOyu8fnDgpe0bGZO/Oe53J7zg3lVT6xrlq1fL0s4eifO++c010Oz1+vfr6aRncvkfOnHtTrpt3jYzs2tq61Z7ucf7Mkzsk3rfa78YtO1txbrt1Vdu6yG9TR9N9HOS3e+asmJ8A8js/K0ZWRyAtv1Umf3msT/r7RT5586T09VWXGys3lwDyu7m1D2nnyO+QqtWcXJHfzal1yDtFfodcvXrnjvz2p7655LdKV0nfoeHRjpJ46+D6SA6rFijjR19x1vYkFt8HD49LWkIr8a1eSeGdFN8PDI/Ko0MDU9q4xDHj3uVKkifHqj0uXDA32m88dv7cOa11kN/+HG7dTJDfusQY75IA8tslbdYqSqCT/O6TPpmUyaIhmQeB0gSQ36UREsABAeS3A8gsoU0A+a2NjAkVEEB+VwCdJXMRQH7nwuRkUG75rbJJ3paOs0veinaScWKR+CGc6lNJ6d6r93ivW+zxHpOtW9IyPL3HtPBHfrs+BebWQ36bY0mk4gSUIux0ORb5XZwpM90R6CS/3a3OShDoTAD5zckIgQDyO4QqNS9H5Hfzah7ijpHfIVatGTkjv/2ps5b89idtkeTN7rSAVh8/uGtfW7qxpE+3NFGDkrfGlThPt27pdYs8/R7y26dTopcL8luPF6PdEkB+u+XNasUIIL+LcWOWXQLIb7t8iW6GAPLbDEeimCWA/DbLk2h2CCC/7XAlankCyO/yDE1FCFJ+K7n92qmzrVYjafkd3whft+aWiFOyZYv6uNPN7rh1Sadb493kdydRjvw2dTTdx0F+u2fOivkJIL/zs2JkdQSQ39WxZ+XuBJDfnI4QCCC/Q6hS83JEfjev5iHuGPkdYtWakTPy258655bfnW5Mx9tYtnSR04dcJh9kmUQZ3+De+7Vvyg0L50ssv5OtSz7zyZva5Hcsx+Pb3n/9N3+f6+Z3px7oKhby25/DrZsJ8luXGONdEkB+u6TNWkUJIL+LkmOeTQLIb5t0iW2KAPLbFEnimCSA/DZJk1i2CCC/bZElblkCyO+yBM3Nzy2/e7X+MJdOsUid2p4kb4Yn+3zfdONHZeixUdl2z4bWwy6Tt71Pn/thx5vh8QMwY1mefvhnnDnyu1gNfZiF/PahCuTQjQDym7MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfte/xnXYIfK7DlWs5x6Q3/7UNZf8Vre+08LYny2IpOV3/GDO4aEBWbl8SdT2JNnHW4n8s2+cl4e3bYq28dDufRLL7fQDLlWsB4ZH5dGhgUiWd2p1kmSB/PbpZOjlgvzW48VoSwTU0y7VUy9TL+S3Jd6ENUoA+W0UJ8EMEUB+GwJJGKsEkN9W8RK8IAHkd0FwTHNKAPntFDeLaRBAfmvAsjy0lvJbMVOSeuOWnRG+6+ZdIyO7trZueseC++Dh8ej9u+9c0+ofrj5Ot3iJH5ap3uvUciUZH/lt+cRaDI/8tgiX0KUJIL9LIySAAwLIbweQWUKbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFIEUOhJAfvtzMHLJ71j6Jvto+7MFvzJBfvtVD51skN/5aV3+qcjfvt4vNy2eEFE3lXlZJ4D8to6YBQwQQH4bgEgI4wSQ38aREtACAeS3BaiELE0A+V0aIQEcEEB+O4DMEoUIIL8LYbMyKbf8Vu0/vv78t2Tb5g0yY/o0K8nUISjyO9wqIr/z1+6dyyJ//ud98iu/PCF9fdjv/OSKj0R+F2fHTHcEkN/uWLNSfgLI7/ysGFkdAeR3dexZuTsB5DenIwQCyO8QqtTMHJHf/tQ9l/xOtwFJp79s6SLZu/NemT1rpj87qygT5HdF4A0si/zODxH5nZ+VqZHIb1MkiWOTAPLbJl1iFyWA/C5KjnkuCSC/XdJmrbwEkN95STGuSgLI7yrps3YvAshvf85HLvntT7r+Z4L89r9G3TJEfuevHfI7PytTI5HfpkgSxyYB5LdNusQuSgD5XZQc81wSQH67pM1aeQkgv/OSYlyVBJDfVdJnbeR3GGcA+W24Tshvw0AdhkN+54eN/M7PytRI5LcpksSxSQD5bZMusYsSQH4XJcc8lwSQ3y5ps1ZeAsjvvKQYVyUB5HeV9Fkb+R3GGcgtv1XP78Hte+TMuTen7KyKtiePj4zJ088eauXyzJM7ZOXyJa2Pnz/0ojy4a1/08XXzrpGRXVtl8fULoo+T78UT7r5zjdw3uL41/6WXT8jGLTujj5P7S68bT4jXR36HcfA7ZYn8zl+76IGXf9svNy16V4Se3/nBlRiJ/C4Bj6nOCCC/naFmIQ0CyG8NWAytjADyuzL0LNyDAPKb4xECAeR3CFVqZo60PfGn7rnk98VLl+Wh3ftk1Yqb5TOfvKntwZdKBn/2lz7dJp5tb0/1IP/q/hdk8123Rw/fVGL+geFReXRoIBLcSlzvGRlr9SFPf6zk9/jRV+ThbZs6PrwzPb7XftTau5/aL8P3D0Q9z5HftqtvLz7y2x5bIpcngPwuz5AI9gkgv+0zZgV9AshvfWbMcE8A+e2eOStmE0B+ZzNiRPUEkN/V14AMOhNAfvtzMnLJbyWbhx4blW33bIgyT8peJYqfO3Ckq0h2sdX4gZxbB9dHEj4tt9NyvJf8Tu41vineaw9K/t+wcL6sW3NLNAz57aLidtZAfutxnZgUUT8Q83JDAPnthjOrlCOA/C7Hj9l2CCC/7XAlqlkCyG+zPIlmhgDy2wxHotglgPy2y5foxQkgv4uzMz1TW37PuXqmDP/e12Xod78U3XRO33w2nWCeeErADw2PtlqbxDL84wvmRlL+hW+Py2unzrbamqTbniRbnnRq75JuiRLn1GnvyO88FfNzDPJbvy6TExPS19+vP5EZ2gSQ39rImFABAeR3BdBZMpMA8jsTEQM8IID89qAIpDCFAPKbQxECAeR3CFVqZo7Ib3/qnkt+J9ueqBvOydvOWS1EbG41KarTPb9Vjn/16in53vePTen5ncwpFuXr166Obm+nb7Kn30/OTd/6Vu8hv21W3G5s5Lc+X+S3PrOiM5DfRckxzyUB5LdL2qyVlwDyOy8pxlVJAPldJX3W7kYA+c3ZCIEA8juEKjUzR+S3P3XPJb/T6cZC+Njxkz3Fsqttdmp7krzpnb4Zns5LCfx4fKc2Lp0Ef7e+4MhvV1U3vw7yW58p8lufWdEZyO+i5JjnkgDy2yVt1spLAPmdlxTjqiSA/K6SPmsjvzkDIRNAfodcvXrnjvz2p76F5Lc/6b+fSfIWdvpGdlqO95LfnVqZJOW4mpu+CZ+Mh/z28XTkywn5nY9TchTyW59Z0RnI76LkmOeSAPLbJW3WyksA+Z2XFOOqJID8rpI+ayO/OQMhE0B+h1y9eueO/PanvkHKbyWoD3/3qHzly2sjknH7k+GhgdYDL8cOHJG9O++N+pInb34vmHet/NHB78gXb/uczJg+TdJiPBbb8+fOiXqEdxLn3W59q1yQ3/4cbt1MkN+6xEQmZEL6hZ7f+uT0ZyC/9Zkxwz0B5Ld75qyYTQD5nc2IEdUTQH5XXwMymEqAtiecihAIIL9DqFIzc0R++1P3XPI72eakV+qPbN8U9c22/YoF9cHD462lOvX8fvrZQ9H71827pvUwTPWxuhkev6c+Tued3m/y/axb5Mhv29W3Fx/5bY8tkcsTQH6XZ0gE+wSQ3/YZs4I+AeS3PjNmuCeA/HbPnBWzCSC/sxkxonoCNuV3n/TJpExWv0kyCJIA8tufsuWS37EwvmHh/Da5HUvoO9aulk8tWSQP7d4nq1bc7ESA+4OwPRPkt6+Vyc4L+Z3NiBHVEUB+V8eelfMTQH7nZ8VIdwSQ3+5Ys1JxAsjv4uyYaY8A8tseWyKbI2BTfv/3vxb5+cUifX3m8iVScwggv/2pdS75rW47Dz02Ktvu2SCLr1/Qln3Ww4ORQsgAACAASURBVCL92aqbTJDfbjjbWAX5bYMqMU0RQH6bIkkcmwSQ3zbpErsoAeR3UXLMc0kA+e2SNmvlJYD8zkuKcVUSsCm//+TFfll9y4TgvquscLhrI7/9qV1p+a36Xz934Ig8vG2TnD73Q9n91H4Zvn8g6rXdxBfyO9yqI7/DrV0TMkd+N6HK4e8R+R1+Deu4A+R3Hatavz0hv+tX0zrsCPldhyrWfw/I7/rXONQdIr/9qVwu+Z1sb7Jy+ZK27JPy+wcnTsqekbHWgyb92aa7TJDf7libXgn5bZoo8UwSQH6bpEksWwSQ37bIErcMAeR3GXrMdUUA+e2KNOvoEEB+69BibFUEkN9VkWfdLALI7yxC7t7PJb9VOkpyDw2Ptj04Mv3wR9UCZfzoK9Et8BnTp7nbhUcrIb89KoZmKshvTWAMd0oA+e0UN4sVJID8LgiOaVYJIL+t4iW4IQLIb0MgCWOUAPLbKE6CWSKA/LYElrClCSC/SyM0FiC3/FYrvvr6aRncvkfOnHuzlcAzT+6Q9G1wY9l1CRTfRD94eLxrHkrWb9yyM3p/2dJFrdvosbA/dvxkW/TkmPQ+u+0xXiP5PvLbdvXtxUd+22NL5PIEkN/lGRLBPgHkt33GrKBPAPmtz4wZ7gkgv90zZ8VsAsjvbEaMqJ6ATfl99OV++VfL6fldfZXDzAD57U/dtOS3L2krgf3V/S/I5rtuj26Yp2+lK3n9wPCoPDo0ED2gM+tGevKhnenb7J1uvCsOSbmO/PblZJTLA/ldjh+z7RJAftvlS3QzBJDfZjgSxSwB5LdZnkSzQwD5bYcrUcsRQH6X48dsNwRsyu/oSZeTbvbBKvUjgPz2p6a55LcSwkOPjcq2ezZEMtm3V6f2K6+dOiv3Da6PUk3L8GT+6b2lRXl8y3zViptl3ZpbWvHUgz23/4c75f7hUdk6uL51+52b376djvz5IL/zs2KkewLIb/fMWVGfAPJbnxkz7BNAfttnzArlCSC/yzMkgnkCyG/zTIlonoBV+W0+XSI2iADy259i10J+p+X24yNjEeFYfqfleBJ/8ta3+nynW+LJeMm15lw9UzbveAL5nT7PfX0ik+H99Sjy258vTGQylQDym1MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfte/xnXYIfK7DlWs5x6Q3/7UNZf8VukqAfzZX/q08/7eWag63cxWud6wcH7rpnY3+d3pRnunW+Kx/P6dDV9ouwHfKS43v0UE+Z11bHkfAtoEkN/ayJhQAQHkdwXQWTKTAPI7ExEDPCCA/PagCKQwhQDym0MRAgHkdwhVamaOyG9/6p5bfisp/PXnvyXbNm+I+mz78IrF9/y5c1q3vGNRr/436+Z3+oZ4vCd1+/vBXfvatvjI9k3ymU/eNOWBn/GguO838hv57cN/G+RQPwLI7/rVtI47Qn7Xsarh7wn5HX4Nm7AD5HcTqhzeHpHf4dWsiRkjv5tY9TD2jPz2p0655Hd8w/nY8ZMdM1+2dJHs3XmvzJ4109nOuolvlUC6lUmn29y9+oAnN6HW2b13v3xp3W9M6XfOze8u5ebmt7P/DlioOQSQ382pdcg7RX6HXL365o78rm9t67Qz5HedqlmfvSC/61PLOu8E+V3n6oa9N+S3P/XLJb/9Sfe9TDq1OknmmBbbWX28e+2v2+1wNQf5jfz27b8N8qkvAeR3fWtbp50hv+tUzfrsBfldn1rWeSfI7zpXN9y9Ib/DrV2TMkd+N6naYe0V+e1PvbTk90svn5CNW3a2ZR+3+3C5JSW3B7fvkTPn3mxb9u4717RanSRzTd9MV+/tGRnreltdCe+nnz0UxVbtTtatuaXj9pDfLqtufy0eeGmfMSsUJ4D8Ls6Ome4IIL/dsWal/ASQ3/lZMbI6Asjv6tizcncCyG9ORwgEkN8hVKmZOSK//al7bvndSRjHEvqeu367qyD2Z6tuMqHn9/ucz57rl3cuv//xtA+IzJ//rvRJn5tiaK6C/NYExnCnBJDfTnGzWEECyO+C4JhmlQDy2ypeghsigPw2BJIwRgkgv43iJJglAshvS2AJW5oA8rs0QmMBcsnvuM3IHWtXy8rlS9oWV1L8uQNH5OFtm7x5EKYxOgUCIb/fh/atb18pf/p/v//xr/3qhNy6+l0R1RPcwxfy28OikFKLAPKbwxACAeR3CFVqXo7I7+bVPMQdI79DrFr9c0Z+17/Gddgh8rsOVaznHpDf/tQ1l/xW7T2GHhuVbfdsmPLQR3X7e/dT+2X4/gGnD7z0B2F7Jsjv93n8n4evlO/96fsf3/JrE/Ibv4789vXskpffBJDffteH7N4jgPzmJPhIAPntY1XIKU0A+c2Z8JEA8tvHqpBTmgDymzPhKwHktz+VySW/ufmdv2DIb+R3/tPCSAjkJ4D8zs+KkdURQH5Xx56VuxNAfnM6QiCA/A6hSs3LEfndvJqHuGPkd4hVa0bOyG9/6pxLfqt0nz/0oowdONL2kEh6fk8tJPIb+e3Pf95kUicCyO86VbO+e0F+17e2Ie8M+R1y9ZqTO/K7ObUOaafI75Cq1dxckd/Nrb3vO0d++1Oh3PJbpaz6e2/csrMt+2ee3DGlD7g/23OfCfL7feYv/umV8t/+4v2PV/zChHz2V2h74v5UsmIdCCC/61DF+u8B+V3/Goe4Q+R3iFVrXs7I7+bVPIQdI79DqBI5Ir85A74SQH77U5me8lv1+t6844ko27077/Wqp/fjI2Py9LOHWiSTEj7O+9jxk633r5t3jYzs2trWs1zdZn/t1Fm5b3B9a1x8m/3MuTfbqnTbrauih3ru/do329aNB8XrI7/fx/bWhT6ZmHj/Y/VAyQ/PnPD1eZfCAy/9+cJEJlMJIL85FSEQQH6HUKXm5Yj8bl7NQ9wx8jvEqtU/Z+R3/Wtchx0iv+tQxXruAfntT10zb36nRbIPN71VTl/d/4Jsvut2mTF9mihh/cDwqDw6NBDJ7TjnrYPrO95KT95gv/vONW3yu1NplGi/YeF8Wbfmlilvpx/4ifz253DrZoL81iXGeJcEkN8uabNWUQLI76LkmGeTAPLbJl1imyKA/DZFkjgmCSC/TdIkli0CyG9bZIlblgDyuyxBc/Mz5XdyqeRt6zzS2FyavSOlZXeW/I6jdbr5nV4pLbfT76fFOPLbVdXNr+Ov/O4TkUnzGyZiUASQ30GVq7HJIr8bW3qvN4789ro8JPcvBJDfHAUfCSC/fawKOaUJIL85E74SQH77Uxkt+R2nnbw5vWzpospboqh8hoZHW21N0rfVO7U8UXvJI791bn2rmMhvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CSC/bdIltikCyG9TJIljkgDy2yRNYtkigPy2RZa4ZQkgv8sSNDe/kPyOl7946bI8tHuf/O3pNyoR4Mn+3L3asSjJPXbgyJQcs+S37q1v5Le5g1lFJOR3FdRZMy8B5HdeUoyrkgDyu0r6rN2NAPKbsxECAeR3CFVqXo7I7+bVPMQdI79DrFozckZ++1PnUvLblwdiZrU5Ue8PPTYq2+7ZkPnAy7TYX7Xi5o69vtVt8z0jY1OEOje//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOebZJID8tkmX2KYIIL9NkSSOSQLIb5M0iWWLAPLbFlniliWA/C5L0Nz8QvLbt7YnCkev9iRF5Hc3ua3Wim+8dxLjyG9zh9N1JOS3a+Ksp0MA+a1Di7FVEUB+V0WedXsRQH5zPkIggPwOoUrNyxH53byah7hj5HeIVWtGzshvf+qcW37Hwvfg4fEo+yofeKnakRz+7lH5ypfXRrnE7U+GhwZk5fIlosS1eqk/q5dqbzJ+9BV5eNsmmTF9Wot+t7YnveS2mtxLjCO//Tncupn4Kr8nRKRfdzOMrx0B5HftSlrLDSG/a1nW4DeF/A6+hI3YAPK7EWUObpPI7+BK1siEkd+NLHsQm0Z++1OmTPmd7Kut0u7VW9vVttIiPp1XOuf0QzmTN9fjnJP76ibL1disFivIb1enwPw6vspv8zslYogEkN8hVq15OSO/m1fzEHaM/A6hSmHmODkp8uMfi3zo5/rkA1dOltoE8rsUPiZbIoD8tgSWsEYJIL+N4iSYQQLIb4MwS4bqKb996eldco9OpyO/neI2uhjy2yhOghkmgPw2DJRwVgggv61gJWhJAsjvkgCZ3pPAXx67Qj7+8Qm5ehbym6NSPwLI7/rVtI47Qn7Xsar12BPy2586Zt789ifVMDJBfodRp05ZIr/DrV0TMkd+N6HK4e8R+R1+Deu4A+R3Havqz56O/rd+Wbx4EvntT0nIxCAB5LdBmISyRgD5bQ0tgUsSQH6XBGhwOvLbIEwVCvltGKjDcMhvh7BZSpsA8lsbGRMqIID8rgA6S2YSQH5nImJACQLI7xLwmOo9AeS39yUiQRFBfnMMfCWA/PanMshvw7VAfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGFCCAPK7BDymek8A+e19iUgQ+c0Z8JgA8tuf4iC/DdcC+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhJAfmciYkAJAn/xcr/ceCNtT0ogZKrHBJDfHheH1FoEuPnNYfCVAPLbn8p4K79fff20DG7fI2fOvdmitWzpItm7816ZPWumxA/jPHb8ZPT+M0/ukJXLl7TGPn/oRXlw177o4+vmXSMju7bK4usXtN5Pxk+/n147Hfvipcvy0O59cvDweBTvke2bZN2aW6I/I7/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SbfhsSdFzpztk6uvFpkxgwdeNvw01HL7yO9alrV2m0J+166ktdkQ8tufUnotvx8YHpVHhwbapLVCF8vnVStujqSzktXJsS+9fEL2jIy1RHn64/T4ZDliqb51cH0k09XcoeHRljxPr50uJfLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYrA3Kuny/m335GfvVtOopvKhzgQUASQ35yDEAiYlN//dLFPfvbTPpn54XelT/pC2D45ekwA+e1PcYKU30pe735qvwzfPxDdAk8LaXXre/zoK/Lwtk0yY/q0Njm+YN610a3tO9aubrspHpckPbdT7NdOnZX7Btd3rCLy25/DrZsJ8luXGONdEkB+u6TNWkUJIL+LkmOeTQLIb5t0iW2KAPLbFEnimCSA/DZJk1i2CJiU3395rF+O/X99snbNhHz4w/xlpK2aNSUu8tufSnstv5NtT5ItT9I3uRXOx0fGIqpKSse3tz++YG4kwF/49rjEwjrdLkXNue3WVS1Rnpbf6dhqnaefPdSqYLplCvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYoA8tsUSeKYJID8NkmTWLYImJTff360X8a/3y//7n95V2bNQn7bqllT4iK//am0t/I7jUhJ57NvnI8k9Q9OnJTnDhxpCeu0oI4//qtXT8n3vn+sred3t1vj8+fOicR5p5YosVjffNftU26NK1k+duBIq8UK8tufw62bCfJblxjjXRJAfrukzVpFCSC/i5Jjnk0CyG+bdIltigDy2xRJ4pgkgPw2SZNYtgggv22RJW5ZAsjvsgTNzQ9Gfiel9V//zd+39fROy28lpJOtSZJ9u9XYZMsU9XH6JnnyYZkxavVQyy98ftUU+Z3uEY78Nnc4XUdCfrsmzno6BJDfOrQYWxUB5HdV5Fm3FwHkN+cjBALI7xCq1Lwckd/Nq3mIO0Z+h1i1ZuSM/PanzkHK7/M/ertnz291U/uGhfOjh2GqV1JQ33TjR2XosVHZds+G1oM0lfxO3ySPS6R6fu/eu1++tO43ovGdYifjIb/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpgggv02RJI5JAshvkzSJZYsA8tsWWeKWJYD8LkvQ3Hxv5fcfH/m+3HTjx1qCOtnTO/0QynSrknQrkuTN71hgxy1UFEr1AMxVK25uyfIk3uS66vPpWOke4chvc4fTdSTkt2virKdDAPmtQ4uxVRFAfldFnnV7EUB+cz5CIID8DqFKzcsR+d28moe4Y2Pye1Lk3Bv9cvGdSZk7V+RD0+n5HeJ58Cln5Lc/1fBWfivJvHHLzhap5EMp1SfTD6585skdsnL5ktb45IMp0w+ljOX5wcPj0fi771wT9fuOX8m5qt1JfIM8fj/ZFiX5IE71PvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJ3U6g75+v1RSTJchvzpKPBJDfPlaFnNIEjMlv0ELAMAHkt2GgJcJ5K79L7KnSqcjvSvGXWhz5XQofky0TQH5bBkx4IwSQ30YwEsQwAeS3YaCE60rgz4/2y4oVE6IUuO4L+a1LjPEuCCC/XVBmjbIEkN9lCTLfFgHkty2y+nGR3/rMes5AfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGGCIwJ98p19Wfw75bQgnYTwggPz2oAikkEkA+Z2JiAEVEUB+VwS+w7LIb8O1QH4bBuowHPLbIWyW0iaA/NZGxoQKCCC/K4DOkpkEkN+ZiBhgiADy2xBIwnhDAPntTSlIpAcB5DfHw1cCyG9/KoP8NlwL5LdhoA7DIb8dwmYpbQLIb21kTKiAAPK7AugsmUkA+Z2JiAGGCCC/DYEkjDcEkN/elIJEkN+cgQAJIL/9KRry23AtkN+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgYYIoD8NgSSMN4QQH57UwoSQX5zBgIkgPz2p2jey+9XXz8tu5/aL8P3D8jsWTOnkLt46bI8tHtf9PmHt22SGdOnRX9+/tCL8uCu9z6/bOki2bvz3tb85HtxwLvvXCP3Da6PPlRrDm7fI2fOvRl9/Mj2TbJuzS3Rn196+YRs3LKzlcdtt65qWxf57c/h1s0E+a1LjPEuCSC/XdJmraIEkN9FyTHPJgHkt026xI4JTE6KnDnbJ9fNn5S+Ak+85IGXnCUfCSC/fawKOaUJ0PaEM+ErAeS3P5XxVn6/deFt2bzjCTl2/OQUeR3ji8X3wcPjkpTQSlDvGRlrCe/HR8bk7BvnW5Jaye/xo6+0Ses4Zrzu1sH1snL5Ekl/rOYuXDA3ei9ef/7cOS1xjvz253DrZoL81iXGeJcEkN8uabNWUQLI76LkmGeTAPLbJl1imyKA/DZFkjgmCSC/TdIkli0CyG9bZIlblgDyuyxBc/O9ld/xFnvd/FZS+4aF86OhscxWf1Y3wVetuLl1W1vFeGB4VB4dGpDF1y+IboV3k9/psbHgTsZL4k/HQn6bO5yuIyG/XRNnPR0CyG8dWoytigDyuyryrNuLAPKb8xECAevyW11NL3IlPQR45GiNAPLbGloCGySA/DYIk1BGCSC/jeIsFSxY+a3Et3qpViVJAd1Jfne6vR23RFHjky1P1Mcq9qFv/5mM7NoardGr7UoyDzUW+V3qPFY6GfldKX4WzyCA/OaIhEAA+R1ClZqXI/K7eTUPccc25ffkP18OUv/fHyIYcq6UAPK7UvwsnpMA8jsnKIY5J4D8do6864JBym8lu187dbbVaiR9+zr9cVp+J2nE761fu7qtr/ee3/+G/PCtH0d9v5M9v5Nz0+1VkN/+HOwimSC/i1BjjisCyG9XpFmnDAHkdxl6zLVFAPltiyxxTRJAfpukSSxTBJDfpkgSxyYB5LdNusQuQwD5XYae2blBym912/rpZw9NIRH3/VZvqNYnqhd4/Lpu3jXRTW7V9iT9Ssr0dJuVTnJczVfie2h4dEpMbn6bPaAuoyG/XdJmLV0CyG9dYoyvggDyuwrqrJlFAPmdRYj3fSCA/PahCuSQJoD85kyEQAD5HUKVmpkj8tufugcpvzvJ6249vNVYJbS//vy3ZNvmDTJj+rSe8ltJ7ecOHGl7GGa6tUk38a0CI7/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpgggv02RJI5JAshvkzSJZYsA8tsWWeKWJYD8LkvQ3Pzay+90yxP1AMs/Ovgd+eJtn4tEePp9JcoHt++R4aEBWbl8Sev9uC1Kp1YnyXIgv80dTteRkN+uibOeDgHktw4txlZFAPldFXnW7UUA+c35CIGATfmt9j8hE9JP1+8QjoJXOSK/vSoHyXQhgPzmaPhKAPntT2W8ld+xlD52/GSLVvrBlPEb3Xp8q7md2p2k26ake3orwb1xy87Wusn3O7VcSa6B/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbImBbfpvKkzjNIoD8bla9Q90t8jvUytU/b+S3PzX2Vn77g0gvE+S3Hi+fRiO/faoGuaQJIL85EyEQQH6HUKXm5Yj8bl7NQ9wx8jvEqtU/Z+R3/Wtchx0iv+tQxXruAfntT12R34Zrgfw2DNRhOOS3Q9gspU0A+a2NjAkVEEB+VwCdJTMJIL8zETHAAwLI73JFmJiclP6+vnJBmD2FAPKbQxECAeR3CFVqZo7Ib3/qjvw2XAvkt2GgDsMhvx3CZiltAshvbWRMqIAA8rsC6CyZSQD5nYmIAR4QQH6XKwLyuxy/brOR33a4EtUsAeS3WZ5EM0cA+W2OZdlIyO+yBFPzkd+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgZ4QAD5Xa4IyO9y/JDfdvgR1Q0B5LcbzqyiTwD5rc/M1gzkt2GyyG/DQB2GQ347hM1S2gSQ39rImFABAeR3BdBZMpMA8jsTEQM8IID8LlcE5Hc5fshvO/yI6oYA8tsNZ1bRJ4D81mdma4b38vvV10/L7qf2y/D9AzJ71swpHC5euiwP7d4Xff7hbZtkxvRp0Z+fP/SiPLjrvc8vW7pI9u68N5r/1oW3ZfOOJ+TY8ZNtsZJj1JqD2/fImXNvRmMe2b5J1q25pTU+XvPg4fEp7yO/bR1V+3GR3/YZs0JxAsjv4uyY6Y4A8tsda1bKTwD5nZ8VI6sjgPyujj0rdydA2xNORwgEkN8hVKmZOSK//am7t/I7KamTYjqJLimhb7t1VUt+v/TyCdkzMtYS3o+PjMnZN863yfFkHCXKXzt1Vu4bXN+S41sH18vK5UumfByvuWrFzW1CPI6H/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbIoD8NkWSOCYJIL9N0iSWLQLIb1tkiVuWAPK7LEFz872V3/EWe938VlL7hoXzo6HjR1+J5LZ6qZvgSTmtYjwwPCqPDg3I4usXtNFTkn3osVHZds+G6L302LTsToryTmVAfps7nK4jIb9dE2c9HQLIbx1ajK2KAPK7KvKs24sA8pvzEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+dg5bcS3+qlbmsrId1Lfse3yOPb3En8nWS2in3o238mI7u2RkOTbVfUe08/e6gV4rp510TjYqmO/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbIoD8NkWSOCYJIL9N0iSWLQLIb1tkiVuWAPK7LEFz84OU32lhnZTfqud3+uNu8jt96zvGGrVN+f1vyA/f+nHU9zvu+R3fAr9j7eqoJYp6qbXGDhxptVhBfps7nK4jIb9dE2c9HQLIbx1ajK2KAPK7KvKs24sA8pvzEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+cg5Xf69nWMM+77rT5WrU/iB1Kqj9M3tNXnkrfH4xjpNiuxOF+/drV84fOrorhJ+Z0W68hvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CSC/bdIltikCyG9TJIljkgDy2yRNYtkigPy2RZa4ZQkgv8sSNDc/SPmd3n76pnf6fSW0v/78t2Tb5g2iboarV7c+4OrW93MHjrQ9HDMpyeM+4+vW3BLFSd8eR36bO5yuIyG/XRNnPR0CyG8dWoytigDyuyryrNuLAPKb8xECAeR3CFVqXo7I7+bVPMQdI79DrFozckZ++1Pn2svvbi1POt36jqX44PY9Mjw0ELU2Sd78VsJbyfGh4dFWn++0eEd++3O4dTNBfusSY7xLAshvl7RZqygB5HdRcsyzSQD5bZMusU0RqEp+T/7LBvpMbYQ4tSKA/K5VOWu7GeR3bUsb/MaQ3/6U0Fv5HUvnY8dPtmjdfeea6AGX6Ve3Ht9qbqd2J1FP75GxVp/udDz1/sYtO1ufjnt+x59Q6z24a1/04bKli9riIL/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpghUJb9N5U+cehJAftezrnXbFfK7bhWtz36Q3/7U0lv57Q8ivUyQ33q8fBqN/PapGuSSJoD85kyEQAD5HUKVmpcj8rt5NQ9xx8jvEKtW/5yR3/WvcZU7/PvTfXLllSLz5sb/BqVYNsjvYtyYZZ8A8ts+47wrIL/zkso5DvmdE5SHw5DfHhaFlFoEkN8chhAIIL9DqFLzckR+N6/mIe4Y+R1i1eqfM/K7/jWucocn/6ZPrvyAyMc/hvyusg6sbY8A8tseW93IyG9dYhnjkd+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgZ4QAD57UERSGEKAeQ3h8ImAeS3TbrE9oEA8tuHKryXA/LbcC2Q34aBOgyH/HYIm6W0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPntQRFIAfnNGXBKAPntFDeLVUAA+V0B9C5LIr8N1wL5bRiow3DIb4ewWUqbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFIEUkN+cAacEkN9OcbNYBQSQ3xVAD01+v/TyCdm4ZWcr7dtuXSUP9/vG2QAAIABJREFUb9skM6ZPa30uOWbZ0kWyd+e9MnvWzOj9Xu+p9199/bQMbt8jZ869KdfNu0ZGdm2VxdcviOY+PjImTz97qLXOM0/ukJXLl7QhVPN3P7Vfhu8faK2pBiC//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOebZJID8tkmX2KYIIL9NkSSOSQK0PTFJk1hpAqf+rk/6r+yTj86fKAWHB16WwsdkiwSQ3xbhaob29ub384delIUL5kbS+eKly/LQ7n0yf+4cuW9wfbRFJbf3jIy1Ce9470pMPzA8Ko8ODURCW8UaP/pKS56n308ye+vC2/LV/S/I5rtuj0R7eqx6f/OOJ+TY8ZOSFu7Ib83T59lw5LdnBSGdNgLIbw5ECASQ3yFUqXk5Ir+bV/MQd4z8DrFq9c8Z+V3/Gtdhh8jvOlSxnntAfvtTV2/ldxpRUmBfeucdGXpsVLbds6F1Wzs5Xo197dTZlihPCuwF866NRPoda1dPuc3dqSyx7N46uL5tPDe//TnEpjJBfpsiSRwbBJDfNqgS0zQB5LdposQzQQD5bYIiMWwTQH7bJkz8NIHJyUk59w/9MmfWhEz7YF9HQMhvzk0IBJDfIVSpmTkiv/2pezDyW7UiUS918zvZsiRGefeda1qyOzlWvZ8U2Dfd+NHWze14bqeWKvF76ob50PBoW1sU9R7y259DbCoT5LcpksSxQQD5bYMqMU0TQH6bJko8EwSQ3yYoEsM2AeS3bcLE70Rg/KUr5FOfnJCrPjSJ/OaIBEsA+R1s6WqfOPLbnxIHIb/TLU7Ux88dONJqYxLL7fVrV8u6NbdEPbtvWDg/+nNafs+Z/eG2Xt2dWqrEcjvuCU7Pb38OrM1MkN826RK7LAHkd1mCzHdBAPntgjJr6BJAfusSY3wVBJDfVVBnTeQ3Z6AOBJDfdahiPfeA/Panrt7L7043r9PyW+FMtkXZ+7VvRoTj/uDJm99p+a3G9eofTtsTfw6r7UyQ37YJE78MAeR3GXrMdUUA+e2KNOvoEEB+69BibFUEkN9VkW/2usjvZte/LrtHftelkvXbB/Lbn5p6Lb91Wo4k+3z36vk95+qZU/qFd5LpyRKlb5Kr92h74s8hNpUJ8tsUSeLYIID8tkGVmKYJIL9NEyWeCQLIbxMUiWGbAPLbNmHidyKA/OZc1IEA8rsOVaznHpDf/tTVW/nd6zZ2ulVJ+nZ28gGXi69f0HYrfMb0aVFblLNvnI/apqiXegDmqhU3R21S1NzD3z0qX/ny2ui9uL/48NAAD7z059xayQT5bQUrQQ0RQH4bAkkYqwSQ31bxErwgAeR3QXBMc0oA+e0UN4v9C4H/9wdXyE2LJ+TnZtDzm0MRLgHkd7i1q3vmyG9/Kuyt/FaC+ulnD7WRum7eNa0HT8bC+9jxk9GYR7ZvavX4Vh8reb5xy87ovWVLF8nenffK7Fkzo49jeX7w8Hj0cfJhmen31PvJnt/pddPzT7950Z/qkokWAeS3Fi4GOyaA/HYMnOUKEUB+F8LGJMsEkN+WARPeCAHktxGMBClAYHJSpK+v88T+PpG5s2fI2fP8jlsALVMcEUB+OwLNMtoEkN/ayKxN8FZ+W9ux5cDIb8uALYZHfluES+jSBJDfpRESwAEB5LcDyCyhTQD5rY2MCQUJTEyKnD0rsmB+D5vYJTbyuyB0plklgPy2ipfghgggvw2BJIxxAshv40gLB0R+F0bXeSLy2zBQh+GQ3w5hs5Q2AeS3NjImVEAA+V0BdJbMJID8zkTEAEMEfvozkfHv98mv/fKE9HW7Sov8NkSbMC4IIL9dUGaNsgSQ32UJMt8WAeS3LbL6cZHf+sx6zkB+GwbqMBzy2yFsltImgPzWRsaECgggvyuAzpKZBJDfmYgYYIgA8tsQSMJ4QwD57U0pSKQHAeQ3x8NXAshvfyqD/DZcC+S3YaAOwyG/HcJmKW0CyG9tZEyogADyuwLoLJlJAPmdiYgBhgggvw2BJIw3BJDf3pSCRJDfnIEACSC//Ska8ttwLZDfhoE6DIf8dgibpbQJIL+1kTGhAgLI7wqgs2QmAeR3JiIGGCKA/DYEkjDeEEB+e1MKEkF+cwYCJID89qdoyG/DtUB+GwbqMBzy2yFsltImgPzWRsaECgggvyuAzpKZBJDfmYgYYIiAkt8v/2Wf/OtfoOe3IaSEqZgA8rviArB8LgK0PcmFiUEVEEB+VwC9y5JByu9XXz8tg9v3yJlzb7Zt67ZbV8nD2zbJjOnT5KWXT8jGLTuj95ctXSR7d94rs2fNbBv//KEX5bVTZ+W+wfVtn398ZEyefvZQ63PPPLlDVi5fEn2cXju5pnof+e3P4dbNBPmtS4zxLgkgv13SZq2iBJDfRckxzyYB5LdNusROEpicmJTzF/rlmtmT2mDmXj1dzr/9jvzsXf252osxAQI5CSC/c4JiWKUEkN+V4mfxHgSQ3/4cjyDldyd8SljfsHC+rFtzSySoHxgelUeHBmTx9QtESe7xo690FON337mmTX6/deFt+er+F2TzXbdHEr1TrIUL5rZkuFpXvWKBjvz253DrZoL81iXGeJcEkN8uaTdjrUmZlDf+4Qq5fPm9/X7gykmZN29S+kpsH/ldAh5TrRFAfltDS2CDBJDfBmESyhgB5LcxlASySAD5bREuoUsRQH6Xwmd0ci3ktxLUu5/aL8P3D0S3u9M3utMCOybY7eZ3krCS4Zt3PCFbB9e3hHfy/bRYR34bPZ9OgyG/neJmMU0CyG9NYAzPReDb37lC/p/x94Z+Ztmk/NYX3hXpK66/kd+5sDPIMQHkt2PgLFeIAPK7EDYmWSaA/LYMmPBGCCC/jWAkiAUCyG8LUAuGrIX8Tt76VhzSt7G7Cew88lu1TxkaHpWRXVujW+TJ18VLl+Wh3ftk/tw53PwueAB9mob89qka5JImgPzmTNgg8H99+wr57vfek93/avmE3L4W+W2DMzGrJYD8rpY/q+cjgPzOx4lRbgkgv93yZrViBJDfxbgxyz4B5Ld9xnlXCF5+p299x/I7boGiPi4iv5O9vZM9v2OwcV9wen7nPWr+j0N++1+jJmeI/G5y9e3tHfltjy2R/SGA/PanFmTSnQDym9PhIwHkt49VIac0AeQ3Z8JXAshvfyoTtPyOb16vWnFz1Os7KabVn+M+3EXkdxyLtif+HFbbmSC/bRMmfhkCyO8y9JjbjQDym7PRBALI7yZUOfw9Ir/Dr2Edd4D8rmNV67cn5Hf9alqXHSG//alk0PJbtSTZMzIme3feG/X6jl8me36rmOm2KsnypW+e0/Pbn8OtmwnyW5cY410SQH67pN2ctb7z3SvkpT9/r+3JJ2+ekC/8j7Q9aU71m7NT5Hdzah3yTpHfIVevvrkjv+tb2zrtDPldp2rWay/Ib3/qGaz87nbrW6FNP+Ay/VDKbpI8nnv4u0flK19eGw2L258MDw1ED7z8g/90QG797IpW/28lxs++cV4e3rZJZkyfJshvfw63bibIb11ijHdJAPntknZD1poUOf+jfvnZz97bb3//pFx7zWSpzfPAy1L4mGyJAPLbEljCGiWA/DaKk2CGCCC/DYEkjFUCyG+reAleggDyuwQ8w1ODld/dhHbMR90K37hlZ/ThsqWL2m6HJ9+Lx8d9vWOpfvDweAt1sud3em6657fh+hAOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEChAIVn4X2CtTIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoCEEkN8NKTTbhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQJALI7yZVm71CAALBE+j1vIPgN8cGIAABCFgmoJ7Vol73Da63vBLhIQABCNSLgGr/uWdkrK2daL12yG4gAAEI2CGQfi6hnVWI2osA8pvzERyBZF/26+ZdIyO7trYeQBrcZki48QSSzxG4+841mUIG+d34I1MaAF9DSyMkgCcE4oeSnzn3puR9Bgvy25PiBZyGOkNPP3so2kHyuUABb4nUG0igyM8CyO8GHhTDW9b9vcfw8oSDgDECuj8LIL+NoS8cCPldGB0Tqybw1oW3ZeixUdl2zwbkd9XFYP3SBNRDfF87dRb5XZokAfIS4GtoXlKM852A+mX6uQNH5OFtm2TG9Gk900V++17NMPKLxeEda1fLyuVLwkiaLCHQgYDOzwLIb46QKQJ5f+8xtR5xIGCDgM7PAshvGxXQi4n81uPFaI8IpH9YS94AU2kmb9GqX3Z/8k+X5Cc/+Sc5eHhckjfGVZzNO56QrYPrW7/AqPE3LJwv69bcIvH7x46fjHafvF0WfxH7rd/8FRn+j1+XZUsXyaeXLpLpH5zWkpjc1PXo0HicSvKHwPQ3x+Q31k8tWSQP7d4nq1bcLL/+q78w5ezyjdXjInuWms7XUHU+x4++Ildd9SH5xn/5drST+MZj1tdQNTZ5O6LT19/f+o1flmfG/msU93/+rc/JyddOt4lMhKVnh8ezdJLy+9I77/T8np48S+lzxfdrzwrrcTrpX3h1flbs9DOq+lzciif9lznq6++Du/a1aKS/9ia/fj6w5d/JyNf+izw6NNC6GIKw9PggeZBa+meB9NfF5Hn8wYmTrbYnf/KnfxH9XJD8S0e+V3tQ0EBSSMvvvD8npv+lV/zzaXwO1e9Bu5/aL8P3D8jsWTOl29fP+GfTpB/Y8b/dKQcP/1mbE+D3qkAOVEVppn8W6PX18/S5H8oDw6PR92f1iv+8+PoF0cd8r7ZfROS3fcasYIlA+oe1Pz7yfbnpxo9FP+zHInx4aCAS2uoL0aFv/1mrRUryG2XWL8rqC5F6qTjxLzfr166OxHi8zprP/1Lrl5b0N12+kFk6ADULW0R+qzPY6YfH5C/RNcPEdgwS0PkaGv/yEEuX5Nc1lVLWXyAeOjwuX1r3m1H26uvx2TfOR78wx19/P75gbusX6E5SPv0DokEMhKoBgaLym+/XNSh+RVtI/8Kr87Ni+i8Me/2yrLb3Rwe/I1+87XPRv2pQX4vHDhyJei7HX3uTXz/jr7HxBQ7+QqeiAxLQskXlt9pi8l/gIgkDKroHqSZ/f1FnMO/PiSr1+BJQ/HtQ8i9hkt/Xp3/wg12/fioxnvYDKja/V3lwOAJKoaj8Vr4qedmS79Vuio78dsOZVSwQ6PXP9NJfQHr9YpElv9OpJ2N1+0Ev+cUs+WcLGAhZEwJF5Xfyv4M5V8+kFVBNzoOLbeh8De11syZLfqf3kiXO07988E9jXZyGsNcoKr/TopDv12GfA5fZZ/1T514/K+r8spxu45P8uVN9z0//xaNikJQ/53/0dtstSJeMWCsMAkXld3yrNm7Zx/fqMOrtS5a9zkvWz4nJ79VZN7+T+03/3t7pXyrwe5UvJySMPHS+nydvfseXNeN/pcD3ajf1Rn674cwqFgikf1hLPrglXu6R7ZuiG9pl5Xfyn2Kp2HFLlW7yO/5F/N6v3CEPP/41+pJbqH/dQhaV30l5s3DB3Nx9b+vGj/3oE9D5GlpWficfcKQyVS2ikjcXk22nkvLmofvukif+4Dmhr65+fZs0o4z85vt1k06Kub12kt95f1bU+WVZye90W7+4dVQ3+Z2Mf+r0G7meJ2KODJFCI1BGfsd/0cL36tCqXn2+afmt83Oijvzu9vUzvnmrSMQtp2IqcXx+r6r+nPiegc7387T85nu1++oiv90zZ0VDBJI/rC2Yd230T6Dmz50TfQMzefM7+U/01S8heW5+x+tfePsf5ROLF2Y+xNAQEsIETKCM/I7/EkZtf+v/+m95+FbA58Bl6jpfQ8vIb/ULzdDwaKvtVNaNnuQvH3/16imZNfPncj3I0CU71vKLQBn5zfdrv2oZSjadfuGN2zll/ayo+8vy4PY9Erfxy3PzWzGMvs7+/jcinMn+36HwJU93BMrIb5Wl+r2I79Xu6lWXlZK/9+j+nJhXfqvbtN2+fvaS3/xeVZdTZn8fut/PO/b55nu1/UL9ywrIb2eoWcg0gaRAUT29kv2/0r25s/opJufGf0N8z12/PeXWePwFLpbsvfrbpXvkmt4/8epDIP2XNel+oPFtCNVvOfnAS/WvGtQrnv+3p9+IbtOqf4rKCwJZBHS+hvaS3+mvv+mvoennHnTqWZu++R3Lm41bdkr8L3iy9sP7zSWQ/B6f/nra63t6TIzv1809O0V33uufz2f9rJj+ZTn59VXlo34mVS/1XIT0TbGkJOp281vNjX+OSPcDL7pf5tWXQKfv0XEP5fR5TD7wMv5ZM/4Zle/V9T0jpneW/j6t+3NiUn6n56r3vv/yiej3ISW/k7IxLdm7PaCV36tMV7y+8dI/C+h8P+d7tftzgfx2z5wVSxCIf5g/dvxkFCV++FpSlKg/q38Seu3sD8v6/+nXM9uepP9J6W23rpKrrvqQfOoTN7Q91FI9XTqO+4u/sDS6zd1LfidvoqV7NpZAwNQaEUg+gVydu/hJ5enz/G9/+/Pyk5/8U9T6oZP8VuPpVVujg2FxK0W/hmb1VEz+s9L019B0S6pf+8Vlov5VTK+2JwpB+mGEFrEQOkACyX8iHbfRiWVMr/PY6Zddvl8HeAAqSDn9tSwp+5JnLutnxbT8TsZVczeu/zfyl6+82vqZINlOZdmSG6Odq9vcveR3Vk/yCvCxpEcEev0s0Os8dpLffK/2qLCep9Lt9x7dnxPTv/Mkv0YO/e9fku99/5gM3z8QXQbq9vWz181vfq/y/CBVnF6vnwV6ff1M/2W22gbfq90WE/ntljerNYiA+mb72V/6NC0oGlTzqraqfokZ/r2vy9Dvfolb31UVgXWNE1C/JKlX/C8cjC9AQAj8CwG+X3MU/v/27idUqiqOA/hZRS1E+oPaAzFyU0EQROAqAlcpQQSJrjJFHrbyDz60iAip91AkV4mIZotQDCQQbZMgrYQIAiHbJIXw9Im2TVvFGZjhvsvMvTO8O7+5Pj6uer7x/M75nPjde79z585yE8iB5HcXf0oHdm9NbsBYbrvbrvU4VrdrP8xm6QKuq5ZuaIThBByrh3Nq6lXC76YkjUOgIOAuCP87RApUfWN65DzUItCUQPkZpE2NaxwCZQHHa/9PLEcBnwZbjrvavjU5VrdvT8x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{\"responsive\": true} ).then(function(){\n",
" \n",
"var gd = document.getElementById('cd137535-45bb-4ca1-98a5-50e849527307');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" 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",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
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"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#fig = px.scatter(filtered, x='ErstellungsDatum', y='delta')\n",
"fig = px.timeline(filtered, x_start='ErstellungsDatum', x_end='ErledigungsDatum', y='VorgangsID')\n",
"fig.update_xaxes(tickformat='%B\\n%Y')\n",
"fig.update_yaxes(type='category')\n",
"#fig.update_yaxes(tickformat=',d', dtick=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1bd744d-50a6-49c1-8559-3f71b416e18c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "81a6d2d2-51c1-4c49-8451-5f38dd39c90a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c07e03d5-b2c7-4c7e-a6ff-2257c586e7ba",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7dbe83c6-a8d6-48d4-a2a4-19f9ddd9f8d3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c22298d2-1260-4959-a1a9-83d8f0973254",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "80fba6825e3e41d0a15729ae8bd16f57",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/6 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tk_graph_cands, _ = tokens.build_token_graph(\n",
" data=filtered,\n",
" model=SPCY_MODEL,\n",
" target_feature='VorgangsBeschreibung',\n",
" build_map=False,\n",
" logging_graph=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "447ef344-92c1-42e4-8b32-e970c61c1d8c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"()"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tuple(tk_graph_cands.edges)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f8781584-a992-4117-832d-c5296af83740",
"metadata": {},
"outputs": [],
"source": [
"for edge in tk_graph_cands.edges:\n",
" print(tk_graph_cands.edges[edge])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3a27a6f3-f196-4f8e-bb1c-09da845c463d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = [data['weight'] for data in tk_graph_cands.edges.values()]\n",
"weights"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "cf6c2c0f-271c-4eb0-b364-f30dbe62b119",
"metadata": {},
"outputs": [],
"source": [
"weights = [20 for _ in range(10)]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "142c1f09-776e-4117-841f-d8b5d4c72d44",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d995c03d-d9d7-4ad0-81a2-18dac6c9900a",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "caba22f6-a641-40b0-b5c4-3092142a67db",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2.99573227, 2.99573227, 2.99573227, 2.99573227, 2.99573227,\n",
" 2.99573227, 2.99573227, 2.99573227, 2.99573227, 2.99573227])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(weights)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cd4642b-0715-4b82-a7da-b75e970f870c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5fd7759f-f1eb-4f0c-9b05-f7230f194515",
"metadata": {},
"outputs": [],
"source": [
"rescaled_dir, rescaled_undir = graphs.pipe_rescale_graph_edge_weights(tk_graph_cands)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b6ec4ffc-ab27-43de-84e7-74b14cdb05c2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TokenGraph(name: TokenGraph, number of nodes: 12, number of edges: 17)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1b44a944-9566-4fbd-82a6-152735a240ae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"------------------------------------\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n",
"{'weight': 0.0952}\n"
]
}
],
"source": [
"for edge in rescaled_dir.edges:\n",
" print(rescaled_dir.edges[edge])\n",
"\n",
"print('------------------------------------')\n",
"\n",
"for edge in rescaled_undir.edges:\n",
" print(rescaled_undir.edges[edge])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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