mirror of
https://github.com/fhswf/aki_prj23_transparenzregister.git
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Feature/visualize verflechtungen (#324)
This commit is contained in:
Tim Ronneburg
GitHub
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Jupyter/NetworkX/archive/Dev.ipynb
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Jupyter/NetworkX/archive/Dev.ipynb
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Jupyter/NetworkX/archive/networkX_with_sql.ipynb
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Jupyter/NetworkX/archive/networkX_with_sql.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os.path\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"# if not os.path.exists(\"src\"):\n",
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"# %cd \"../\"\n",
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"# os.path.abspath(\".\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"from aki_prj23_transparenzregister.utils.sql import entities\n",
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"from sqlalchemy.orm import aliased\n",
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"from sqlalchemy import func, text\n",
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"\n",
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"# Alias for Company table for the base company\n",
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"base_company = aliased(entities.Company, name=\"base_company\")\n",
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"\n",
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"# Alias for Company table for the head company\n",
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"head_company = aliased(entities.Company, name=\"head_company\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider\n",
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"from aki_prj23_transparenzregister.utils.sql.connector import get_session\n",
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"\n",
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"session = get_session(JsonFileConfigProvider(\"../secrets.json\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'SELECT base_company.name AS name_company_base, relation.relation AS relation_type, head_company.name AS name_company_head \\nFROM company AS base_company JOIN (relation JOIN company_relation ON relation.id = company_relation.id) ON relation.company_id = base_company.id JOIN company AS head_company ON company_relation.company2_id = head_company.id'"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Query to fetch relations between companies\n",
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"relations_query = (\n",
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" session.query(\n",
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" base_company.name.label(\"name_company_base\"),\n",
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" entities.CompanyRelation.relation.label(\"relation_type\"),\n",
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" head_company.name.label(\"name_company_head\"),\n",
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" )\n",
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" .join(\n",
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" entities.CompanyRelation,\n",
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" entities.CompanyRelation.company_id == base_company.id,\n",
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" )\n",
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" .join(\n",
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" head_company,\n",
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" entities.CompanyRelation.company2_id == head_company.id,\n",
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" )\n",
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")\n",
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"str(relations_query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"121 ms ± 9.27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%timeit pd.read_sql_query(str(relations_query), session.bind)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name_company_base</th>\n",
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" <th>relation_type</th>\n",
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" <th>name_company_head</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2. Schaper Objekt GmbH & Co. Kiel KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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" <td>Multi-Center Warenvertriebs GmbH</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Alb-Windkraft GmbH & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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" <td>EnBW Windkraftprojekte GmbH</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Anneliese Köster GmbH & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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" <td>INDUS Holding Aktiengesellschaft</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>AURELIUS Equity Opportunities SE & Co. KGaA</td>\n",
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" <td>HAFTENDER_GESELLSCHAFTER</td>\n",
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" <td>AURELIUS Management SE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Aurelius KG</td>\n",
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" <td>HAFTENDER_GESELLSCHAFTER</td>\n",
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" <td>Aurelius Verwaltungs GmbH</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>573</th>\n",
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" <td>Zalando BTD 011 SE & Co. KG</td>\n",
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" <td>HAFTENDER_GESELLSCHAFTER</td>\n",
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" <td>Zalando SE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>574</th>\n",
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" <td>Zalando BTD 011 SE & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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" <td>Zalando Operations GmbH</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>575</th>\n",
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" <td>zLabels Creation & Sales GmbH & Co. KG</td>\n",
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" <td>HAFTENDER_GESELLSCHAFTER</td>\n",
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" <td>zLabels GmbH</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>576</th>\n",
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" <td>Zalando Customer Care International SE & Co. KG</td>\n",
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" <td>HAFTENDER_GESELLSCHAFTER</td>\n",
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" <td>Zalando SE</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>577</th>\n",
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" <td>Zalando Customer Care International SE & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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" <td>Zalando Operations GmbH</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>578 rows × 3 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" name_company_base \\\n",
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"0 2. Schaper Objekt GmbH & Co. Kiel KG \n",
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"1 Alb-Windkraft GmbH & Co. KG \n",
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"2 Anneliese Köster GmbH & Co. KG \n",
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"3 AURELIUS Equity Opportunities SE & Co. KGaA \n",
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"4 Aurelius KG \n",
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".. ... \n",
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"573 Zalando BTD 011 SE & Co. KG \n",
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"574 Zalando BTD 011 SE & Co. KG \n",
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"575 zLabels Creation & Sales GmbH & Co. KG \n",
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"576 Zalando Customer Care International SE & Co. KG \n",
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"577 Zalando Customer Care International SE & Co. KG \n",
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"\n",
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" relation_type name_company_head \n",
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"0 KOMMANDITIST Multi-Center Warenvertriebs GmbH \n",
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"1 KOMMANDITIST EnBW Windkraftprojekte GmbH \n",
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"2 KOMMANDITIST INDUS Holding Aktiengesellschaft \n",
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"3 HAFTENDER_GESELLSCHAFTER AURELIUS Management SE \n",
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"4 HAFTENDER_GESELLSCHAFTER Aurelius Verwaltungs GmbH \n",
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".. ... ... \n",
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"573 HAFTENDER_GESELLSCHAFTER Zalando SE \n",
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"574 KOMMANDITIST Zalando Operations GmbH \n",
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"575 HAFTENDER_GESELLSCHAFTER zLabels GmbH \n",
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"576 HAFTENDER_GESELLSCHAFTER Zalando SE \n",
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"577 KOMMANDITIST Zalando Operations GmbH \n",
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"\n",
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"[578 rows x 3 columns]"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"company_relations = pd.read_sql_query(str(relations_query), session.bind)\n",
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"company_relations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"relations_query = (\n",
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" session.query(\n",
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" entities.Company.name.label(\"name_company\"),\n",
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" entities.PersonRelation.relation.label(\"relation_type\"),\n",
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" entities.Person.lastname.label(\"lastname\"),\n",
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" entities.Person.firstname.label(\"firstname\"),\n",
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" entities.Person.date_of_birth.label(\"date_of_birth\"),\n",
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" )\n",
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" .join(\n",
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" entities.PersonRelation,\n",
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" entities.PersonRelation.company_id == entities.Company.id,\n",
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" )\n",
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" .join(\n",
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" entities.Person,\n",
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" entities.PersonRelation.person_id == entities.Person.id,\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"373 ms ± 25.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"source": [
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"%timeit pd.read_sql_query(str(relations_query), session.bind)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name_company</th>\n",
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" <th>relation_type</th>\n",
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" <th>lastname</th>\n",
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" <th>firstname</th>\n",
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" <th>date_of_birth</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0 10 24 Telefondienste GmbH</td>\n",
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" <td>GESCHAEFTSFUEHRER</td>\n",
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||||||
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" <td>Tetau</td>\n",
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||||||
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" <td>Nicolas</td>\n",
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||||||
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" <td>1971-01-02</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||||
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" <th>1</th>\n",
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" <td>0 10 24 Telefondienste GmbH</td>\n",
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" <td>PROKURIST</td>\n",
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" <td>Dammast</td>\n",
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" <td>Lutz</td>\n",
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" <td>1966-12-06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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||||||
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" <td>1. Staiger Grundstücksverwaltung GmbH & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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||||||
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" <td>Tutsch</td>\n",
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||||||
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" <td>Rosemarie</td>\n",
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||||||
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" <td>1941-10-09</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||||
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" <th>3</th>\n",
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||||||
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" <td>1. Staiger Grundstücksverwaltung GmbH & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
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||||||
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" <td>Staiger</td>\n",
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" <td>Marc</td>\n",
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||||||
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" <td>1969-10-22</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||||
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" <th>4</th>\n",
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||||||
|
" <td>1. Staiger Grundstücksverwaltung GmbH & Co. KG</td>\n",
|
||||||
|
" <td>KOMMANDITIST</td>\n",
|
||||||
|
" <td>Staiger</td>\n",
|
||||||
|
" <td>Michaela</td>\n",
|
||||||
|
" <td>1971-03-03</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>...</th>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>14891</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Weirich</td>\n",
|
||||||
|
" <td>Torsten</td>\n",
|
||||||
|
" <td>1975-07-21</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>14892</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Brusinski</td>\n",
|
||||||
|
" <td>Bastian</td>\n",
|
||||||
|
" <td>1980-10-29</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>14893</th>\n",
|
||||||
|
" <td>Zalando Customer Care International SE & Co. KG</td>\n",
|
||||||
|
" <td>PROKURIST</td>\n",
|
||||||
|
" <td>Pape</td>\n",
|
||||||
|
" <td>Ute</td>\n",
|
||||||
|
" <td>1978-12-13</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>14894</th>\n",
|
||||||
|
" <td>zebotec GmbH</td>\n",
|
||||||
|
" <td>GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Neff</td>\n",
|
||||||
|
" <td>Werner</td>\n",
|
||||||
|
" <td>1981-11-24</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>14895</th>\n",
|
||||||
|
" <td>zebotec GmbH</td>\n",
|
||||||
|
" <td>GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Morris</td>\n",
|
||||||
|
" <td>Richard</td>\n",
|
||||||
|
" <td>1971-01-02</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"<p>14896 rows × 5 columns</p>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" name_company relation_type \\\n",
|
||||||
|
"0 0 10 24 Telefondienste GmbH GESCHAEFTSFUEHRER \n",
|
||||||
|
"1 0 10 24 Telefondienste GmbH PROKURIST \n",
|
||||||
|
"2 1. Staiger Grundstücksverwaltung GmbH & Co. KG KOMMANDITIST \n",
|
||||||
|
"3 1. Staiger Grundstücksverwaltung GmbH & Co. KG KOMMANDITIST \n",
|
||||||
|
"4 1. Staiger Grundstücksverwaltung GmbH & Co. KG KOMMANDITIST \n",
|
||||||
|
"... ... ... \n",
|
||||||
|
"14891 Wohnungsbaugesellschaft mit beschränkter Haftu... GESCHAEFTSFUEHRER \n",
|
||||||
|
"14892 Wohnungsbaugesellschaft mit beschränkter Haftu... GESCHAEFTSFUEHRER \n",
|
||||||
|
"14893 Zalando Customer Care International SE & Co. KG PROKURIST \n",
|
||||||
|
"14894 zebotec GmbH GESCHAEFTSFUEHRER \n",
|
||||||
|
"14895 zebotec GmbH GESCHAEFTSFUEHRER \n",
|
||||||
|
"\n",
|
||||||
|
" lastname firstname date_of_birth \n",
|
||||||
|
"0 Tetau Nicolas 1971-01-02 \n",
|
||||||
|
"1 Dammast Lutz 1966-12-06 \n",
|
||||||
|
"2 Tutsch Rosemarie 1941-10-09 \n",
|
||||||
|
"3 Staiger Marc 1969-10-22 \n",
|
||||||
|
"4 Staiger Michaela 1971-03-03 \n",
|
||||||
|
"... ... ... ... \n",
|
||||||
|
"14891 Weirich Torsten 1975-07-21 \n",
|
||||||
|
"14892 Brusinski Bastian 1980-10-29 \n",
|
||||||
|
"14893 Pape Ute 1978-12-13 \n",
|
||||||
|
"14894 Neff Werner 1981-11-24 \n",
|
||||||
|
"14895 Morris Richard 1971-01-02 \n",
|
||||||
|
"\n",
|
||||||
|
"[14896 rows x 5 columns]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 26,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"df = pd.read_sql_query(str(relations_query), session.bind)\n",
|
||||||
|
"df"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 27,
|
||||||
|
"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>person_id</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>2520</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>4993</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>3202</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>4611</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>4095</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>...</th>\n",
|
||||||
|
" <td>...</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1804</th>\n",
|
||||||
|
" <td>3565</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1805</th>\n",
|
||||||
|
" <td>3510</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1806</th>\n",
|
||||||
|
" <td>530</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1807</th>\n",
|
||||||
|
" <td>536</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1808</th>\n",
|
||||||
|
" <td>4617</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"<p>1809 rows × 1 columns</p>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" person_id\n",
|
||||||
|
"0 2520\n",
|
||||||
|
"1 4993\n",
|
||||||
|
"2 3202\n",
|
||||||
|
"3 4611\n",
|
||||||
|
"4 4095\n",
|
||||||
|
"... ...\n",
|
||||||
|
"1804 3565\n",
|
||||||
|
"1805 3510\n",
|
||||||
|
"1806 530\n",
|
||||||
|
"1807 536\n",
|
||||||
|
"1808 4617\n",
|
||||||
|
"\n",
|
||||||
|
"[1809 rows x 1 columns]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 27,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from sqlalchemy import func, text\n",
|
||||||
|
"\n",
|
||||||
|
"# Subquery to group and count the relations without joins\n",
|
||||||
|
"grouped_relations_subquery = (\n",
|
||||||
|
" session.query(\n",
|
||||||
|
" entities.PersonRelation.person_id,\n",
|
||||||
|
" )\n",
|
||||||
|
" .group_by(entities.PersonRelation.person_id)\n",
|
||||||
|
" .having(func.count() > 1)\n",
|
||||||
|
")\n",
|
||||||
|
"pd.DataFrame(grouped_relations_subquery.all())"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 28,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"relations_query = (\n",
|
||||||
|
" session.query(\n",
|
||||||
|
" entities.Company.name.label(\"name_company\"),\n",
|
||||||
|
" entities.PersonRelation.relation.label(\"relation_type\"),\n",
|
||||||
|
" entities.Person.lastname.label(\"lastname\"),\n",
|
||||||
|
" entities.Person.firstname.label(\"firstname\"),\n",
|
||||||
|
" entities.Person.date_of_birth.label(\"date_of_birth\"),\n",
|
||||||
|
" )\n",
|
||||||
|
" .join(\n",
|
||||||
|
" entities.PersonRelation,\n",
|
||||||
|
" entities.PersonRelation.company_id == entities.Company.id,\n",
|
||||||
|
" )\n",
|
||||||
|
" .join(\n",
|
||||||
|
" entities.Person,\n",
|
||||||
|
" entities.PersonRelation.person_id == entities.Person.id,\n",
|
||||||
|
" )\n",
|
||||||
|
" .filter(entities.PersonRelation.person_id.in_(grouped_relations_subquery))\n",
|
||||||
|
")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 30,
|
||||||
|
"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>name_company</th>\n",
|
||||||
|
" <th>relation_type</th>\n",
|
||||||
|
" <th>lastname</th>\n",
|
||||||
|
" <th>firstname</th>\n",
|
||||||
|
" <th>date_of_birth</th>\n",
|
||||||
|
" <th>person_name</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>0 10 24 Telefondienste GmbH</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Tetau</td>\n",
|
||||||
|
" <td>Nicolas</td>\n",
|
||||||
|
" <td>1971-01-02</td>\n",
|
||||||
|
" <td>TetauNicolas</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>0 10 24 Telefondienste GmbH</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.PROKURIST</td>\n",
|
||||||
|
" <td>Dammast</td>\n",
|
||||||
|
" <td>Lutz</td>\n",
|
||||||
|
" <td>1966-12-06</td>\n",
|
||||||
|
" <td>DammastLutz</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>01050.com GmbH</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Tetau</td>\n",
|
||||||
|
" <td>Nicolas</td>\n",
|
||||||
|
" <td>1971-01-02</td>\n",
|
||||||
|
" <td>TetauNicolas</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>01050.com GmbH</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.PROKURIST</td>\n",
|
||||||
|
" <td>Dammast</td>\n",
|
||||||
|
" <td>Lutz</td>\n",
|
||||||
|
" <td>1966-12-06</td>\n",
|
||||||
|
" <td>DammastLutz</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>AASP Filmproduktionsgesellschaft mbH & Co. Leo...</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.KOMMANDITIST</td>\n",
|
||||||
|
" <td>Dellhofen</td>\n",
|
||||||
|
" <td>Jens</td>\n",
|
||||||
|
" <td>1977-04-19</td>\n",
|
||||||
|
" <td>DellhofenJens</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",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7071</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Karounos</td>\n",
|
||||||
|
" <td>Marita</td>\n",
|
||||||
|
" <td>1971-03-30</td>\n",
|
||||||
|
" <td>KarounosMarita</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7072</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.PROKURIST</td>\n",
|
||||||
|
" <td>Groll</td>\n",
|
||||||
|
" <td>Michael</td>\n",
|
||||||
|
" <td>1967-12-24</td>\n",
|
||||||
|
" <td>GrollMichael</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7073</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Weirich</td>\n",
|
||||||
|
" <td>Torsten</td>\n",
|
||||||
|
" <td>1975-07-21</td>\n",
|
||||||
|
" <td>WeirichTorsten</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7074</th>\n",
|
||||||
|
" <td>Wohnungsbaugesellschaft mit beschränkter Haftu...</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.GESCHAEFTSFUEHRER</td>\n",
|
||||||
|
" <td>Brusinski</td>\n",
|
||||||
|
" <td>Bastian</td>\n",
|
||||||
|
" <td>1980-10-29</td>\n",
|
||||||
|
" <td>BrusinskiBastian</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>7075</th>\n",
|
||||||
|
" <td>Zalando Customer Care International SE & Co. KG</td>\n",
|
||||||
|
" <td>RelationshipRoleEnum.PROKURIST</td>\n",
|
||||||
|
" <td>Pape</td>\n",
|
||||||
|
" <td>Ute</td>\n",
|
||||||
|
" <td>1978-12-13</td>\n",
|
||||||
|
" <td>PapeUte</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"<p>7076 rows × 6 columns</p>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" name_company \\\n",
|
||||||
|
"0 0 10 24 Telefondienste GmbH \n",
|
||||||
|
"1 0 10 24 Telefondienste GmbH \n",
|
||||||
|
"2 01050.com GmbH \n",
|
||||||
|
"3 01050.com GmbH \n",
|
||||||
|
"4 AASP Filmproduktionsgesellschaft mbH & Co. Leo... \n",
|
||||||
|
"... ... \n",
|
||||||
|
"7071 Wohnungsbaugesellschaft mit beschränkter Haftu... \n",
|
||||||
|
"7072 Wohnungsbaugesellschaft mit beschränkter Haftu... \n",
|
||||||
|
"7073 Wohnungsbaugesellschaft mit beschränkter Haftu... \n",
|
||||||
|
"7074 Wohnungsbaugesellschaft mit beschränkter Haftu... \n",
|
||||||
|
"7075 Zalando Customer Care International SE & Co. KG \n",
|
||||||
|
"\n",
|
||||||
|
" relation_type lastname firstname \\\n",
|
||||||
|
"0 RelationshipRoleEnum.GESCHAEFTSFUEHRER Tetau Nicolas \n",
|
||||||
|
"1 RelationshipRoleEnum.PROKURIST Dammast Lutz \n",
|
||||||
|
"2 RelationshipRoleEnum.GESCHAEFTSFUEHRER Tetau Nicolas \n",
|
||||||
|
"3 RelationshipRoleEnum.PROKURIST Dammast Lutz \n",
|
||||||
|
"4 RelationshipRoleEnum.KOMMANDITIST Dellhofen Jens \n",
|
||||||
|
"... ... ... ... \n",
|
||||||
|
"7071 RelationshipRoleEnum.GESCHAEFTSFUEHRER Karounos Marita \n",
|
||||||
|
"7072 RelationshipRoleEnum.PROKURIST Groll Michael \n",
|
||||||
|
"7073 RelationshipRoleEnum.GESCHAEFTSFUEHRER Weirich Torsten \n",
|
||||||
|
"7074 RelationshipRoleEnum.GESCHAEFTSFUEHRER Brusinski Bastian \n",
|
||||||
|
"7075 RelationshipRoleEnum.PROKURIST Pape Ute \n",
|
||||||
|
"\n",
|
||||||
|
" date_of_birth person_name \n",
|
||||||
|
"0 1971-01-02 TetauNicolas \n",
|
||||||
|
"1 1966-12-06 DammastLutz \n",
|
||||||
|
"2 1971-01-02 TetauNicolas \n",
|
||||||
|
"3 1966-12-06 DammastLutz \n",
|
||||||
|
"4 1977-04-19 DellhofenJens \n",
|
||||||
|
"... ... ... \n",
|
||||||
|
"7071 1971-03-30 KarounosMarita \n",
|
||||||
|
"7072 1967-12-24 GrollMichael \n",
|
||||||
|
"7073 1975-07-21 WeirichTorsten \n",
|
||||||
|
"7074 1980-10-29 BrusinskiBastian \n",
|
||||||
|
"7075 1978-12-13 PapeUte \n",
|
||||||
|
"\n",
|
||||||
|
"[7076 rows x 6 columns]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 30,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"relations_df = pd.DataFrame(relations_query.all())\n",
|
||||||
|
"relations_df[\"person_name\"] = relations_df[\"lastname\"] + relations_df[\"firstname\"]\n",
|
||||||
|
"relations_df.rename(\n",
|
||||||
|
" columns={\"oldName1\": \"newName1\", \"oldName2\": \"newName2\"}, inplace=True\n",
|
||||||
|
")\n",
|
||||||
|
"relations_df"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"node_template = {\n",
|
||||||
|
" \"id\": \"(company|person)_\\d\",\n",
|
||||||
|
" \"label\": \"Name from entries\",\n",
|
||||||
|
" \"type\": \"Company|Person\",\n",
|
||||||
|
" \"shape\": \"dot\",\n",
|
||||||
|
" \"color\": \"#729b79ff\",\n",
|
||||||
|
" # TODO add title for hover effect in graph \"title\": \"\"\n",
|
||||||
|
"}\n",
|
||||||
|
"nodes = relations_df\n",
|
||||||
|
"for index in relations_df.index:\n",
|
||||||
|
" nodes[\"index\"] = {\n",
|
||||||
|
" \"label\": company_2.name,\n",
|
||||||
|
" \"type\": \"Company\",\n",
|
||||||
|
" \"shape\": \"dot\",\n",
|
||||||
|
" \"color\": \"#729b79ff\",\n",
|
||||||
|
" }"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import networkx as nx\n",
|
||||||
|
"import matplotlib.pyplot as plt\n",
|
||||||
|
"# relations_df[\"person_name\"] = relations_df[\"lastname\"] + relations_df[\"firstname\"]\n",
|
||||||
|
"\n",
|
||||||
|
"nodes = \n",
|
||||||
|
"# create edges from dataframe\n",
|
||||||
|
"graph = nx.from_pandas_edgelist(relations_df, source=\"name_company\", target=\"person_name\", edge_attr=\"relation_type\")\n",
|
||||||
|
"\n",
|
||||||
|
"# update node attributes from dataframe\n",
|
||||||
|
"nodes_attr = nodes.set_index(\"index\").to_dict(orient=\"index\")\n",
|
||||||
|
"nx.set_node_attributes(graph, nodes_attr)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from pyvis.network import Network\n",
|
||||||
|
"\n",
|
||||||
|
"net = Network(\n",
|
||||||
|
" directed=False, neighborhood_highlight=True, bgcolor=\"white\", font_color=\"black\"\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"# pass networkx graph to pyvis\n",
|
||||||
|
"net.from_nx(graph)\n",
|
||||||
|
"\n",
|
||||||
|
"net.inherit_edge_colors(False)\n",
|
||||||
|
"net.set_edge_smooth(\"dynamic\")\n",
|
||||||
|
"adj_list = net.get_adj_list()\n",
|
||||||
|
"\n",
|
||||||
|
"measure_type = \"degree\"\n",
|
||||||
|
"measure_vector = {}\n",
|
||||||
|
"\n",
|
||||||
|
"if measure_type == \"eigenvector\":\n",
|
||||||
|
" measure_vector = nx.eigenvector_centrality(graph)\n",
|
||||||
|
" df[\"eigenvector\"] = measure_vector.values()\n",
|
||||||
|
"if measure_type == \"degree\":\n",
|
||||||
|
" measure_vector = nx.degree_centrality(graph)\n",
|
||||||
|
" df[\"degree\"] = measure_vector.values()\n",
|
||||||
|
"if measure_type == \"betweeness\":\n",
|
||||||
|
" measure_vector = nx.betweenness_centrality(graph)\n",
|
||||||
|
" df[\"betweeness\"] = measure_vector.values()\n",
|
||||||
|
"if measure_type == \"closeness\":\n",
|
||||||
|
" measure_vector = nx.closeness_centrality(graph)\n",
|
||||||
|
" df[\"closeness\"] = measure_vector.values()\n",
|
||||||
|
"if measure_type == \"pagerank\":\n",
|
||||||
|
" measure_vector = nx.pagerank(graph)\n",
|
||||||
|
" df[\"pagerank\"] = measure_vector.values()\n",
|
||||||
|
"if measure_type == \"average_degree\":\n",
|
||||||
|
" measure_vector = nx.average_degree_connectivity(graph)\n",
|
||||||
|
" # df[\"average_degree\"] = measure_vector.values()\n",
|
||||||
|
" print(measure_vector.values())\n",
|
||||||
|
"\n",
|
||||||
|
"# calculate and update size of the nodes depending on their number of edges\n",
|
||||||
|
"for node_id, neighbors in adj_list.items():\n",
|
||||||
|
" # df[\"edges\"] = measure_vector.values()\n",
|
||||||
|
"\n",
|
||||||
|
" if measure_type == \"edges\":\n",
|
||||||
|
" size = 10 # len(neighbors)*5\n",
|
||||||
|
" else:\n",
|
||||||
|
" size = measure_vector[node_id] * 50\n",
|
||||||
|
" next(\n",
|
||||||
|
" (node.update({\"size\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
|
||||||
|
" None,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"net.repulsion()\n",
|
||||||
|
"net.show_buttons(filter_=[\"physics\"])\n",
|
||||||
|
"\n",
|
||||||
|
"# net.show_buttons()\n",
|
||||||
|
"\n",
|
||||||
|
"# save graph as HTML\n",
|
||||||
|
"net.save_graph(\"./tmp.html\")"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "aki-prj23-transparenzregister-IY2hcXvW-py3.11",
|
||||||
|
"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": "3.11.4"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
272
Jupyter/NetworkX/archive/network_graph.html
Normal file
272
Jupyter/NetworkX/archive/network_graph.html
Normal file
File diff suppressed because one or more lines are too long
129790
Jupyter/NetworkX/archive/sql_alchemy_to_networkx.ipynb
Normal file
129790
Jupyter/NetworkX/archive/sql_alchemy_to_networkx.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
21843
Jupyter/NetworkX/archive/sql_alchemy_to_networkx_v2.ipynb
Normal file
21843
Jupyter/NetworkX/archive/sql_alchemy_to_networkx_v2.ipynb
Normal file
File diff suppressed because one or more lines are too long
275
Jupyter/NetworkX/archive/tmp.html
Normal file
275
Jupyter/NetworkX/archive/tmp.html
Normal file
File diff suppressed because one or more lines are too long
272
Jupyter/NetworkX/archive_prod/network_graph.html
Normal file
272
Jupyter/NetworkX/archive_prod/network_graph.html
Normal file
File diff suppressed because one or more lines are too long
151
Jupyter/NetworkX/archive_prod/networkx_dash.py
Normal file
151
Jupyter/NetworkX/archive_prod/networkx_dash.py
Normal file
@ -0,0 +1,151 @@
|
|||||||
|
"""Old NetworkX Graph which needs to be discarded in the next commits."""
|
||||||
|
import networkx as nx
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
from dash import dcc, html
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
|
||||||
|
from aki_prj23_transparenzregister.utils.sql import connector, entities
|
||||||
|
|
||||||
|
test_company = 13 # 2213 # 13
|
||||||
|
|
||||||
|
|
||||||
|
def find_company_relations(company_id: int) -> pd.DataFrame:
|
||||||
|
"""_summary_.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
company_id (int): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: _description_
|
||||||
|
"""
|
||||||
|
session = connector.get_session(JsonFileConfigProvider("./secrets.json"))
|
||||||
|
query_companies = session.query(entities.Company)
|
||||||
|
query_relations = session.query(entities.CompanyRelation)
|
||||||
|
|
||||||
|
companies_df: pd.DataFrame = pd.read_sql(str(query_companies), session.bind) # type: ignore
|
||||||
|
companies_relations_df: pd.DataFrame = pd.read_sql(str(query_relations), session.bind) # type: ignore
|
||||||
|
|
||||||
|
companies_relations_df = companies_relations_df.loc[
|
||||||
|
companies_relations_df["relation_id"] == company_id, :
|
||||||
|
][["relation_id", "company_relation_company2_id"]]
|
||||||
|
|
||||||
|
company_name = []
|
||||||
|
connected_company_name = []
|
||||||
|
|
||||||
|
for _, row in companies_relations_df.iterrows():
|
||||||
|
company_name.append(
|
||||||
|
companies_df.loc[companies_df["company_id"] == row["relation_id"]][
|
||||||
|
"company_name"
|
||||||
|
].iloc[0]
|
||||||
|
)
|
||||||
|
connected_company_name.append(
|
||||||
|
companies_df.loc[
|
||||||
|
companies_df["company_id"] == row["company_relation_company2_id"]
|
||||||
|
]["company_name"].iloc[0]
|
||||||
|
)
|
||||||
|
|
||||||
|
# print(company_name)
|
||||||
|
companies_relations_df["company_name"] = company_name
|
||||||
|
companies_relations_df["connected_company_name"] = connected_company_name
|
||||||
|
# print(companies_relations_df)
|
||||||
|
return companies_relations_df
|
||||||
|
|
||||||
|
|
||||||
|
# Plotly figure
|
||||||
|
def network_graph(company_id: int) -> go.Figure:
|
||||||
|
"""_summary_.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
company_id (int): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
go.Figure: _description_
|
||||||
|
"""
|
||||||
|
edges = []
|
||||||
|
for _, row in find_company_relations(company_id).iterrows():
|
||||||
|
edges.append([row["company_name"], row["connected_company_name"]])
|
||||||
|
|
||||||
|
network_graph = nx.Graph()
|
||||||
|
network_graph.add_edges_from(edges)
|
||||||
|
pos = nx.spring_layout(network_graph)
|
||||||
|
|
||||||
|
# edges trace
|
||||||
|
edge_x = []
|
||||||
|
edge_y = []
|
||||||
|
for edge in network_graph.edges():
|
||||||
|
x0, y0 = pos[edge[0]]
|
||||||
|
x1, y1 = pos[edge[1]]
|
||||||
|
edge_x.append(x0)
|
||||||
|
edge_x.append(x1)
|
||||||
|
edge_x.append(None)
|
||||||
|
edge_y.append(y0)
|
||||||
|
edge_y.append(y1)
|
||||||
|
edge_y.append(None)
|
||||||
|
|
||||||
|
edge_trace = go.Scatter(
|
||||||
|
x=edge_x,
|
||||||
|
y=edge_y,
|
||||||
|
line={"color": "black", "width": 1},
|
||||||
|
hoverinfo="none",
|
||||||
|
showlegend=False,
|
||||||
|
mode="lines",
|
||||||
|
)
|
||||||
|
|
||||||
|
# nodes trace
|
||||||
|
node_x = []
|
||||||
|
node_y = []
|
||||||
|
text = []
|
||||||
|
for node in network_graph.nodes():
|
||||||
|
x, y = pos[node]
|
||||||
|
node_x.append(x)
|
||||||
|
node_y.append(y)
|
||||||
|
text.append(node)
|
||||||
|
|
||||||
|
node_trace = go.Scatter(
|
||||||
|
x=node_x,
|
||||||
|
y=node_y,
|
||||||
|
text=text,
|
||||||
|
mode="markers+text",
|
||||||
|
showlegend=False,
|
||||||
|
hoverinfo="none",
|
||||||
|
marker={"color": "pink", "size": 50, "line": {"color": "black", "width": 1}},
|
||||||
|
)
|
||||||
|
|
||||||
|
# layout
|
||||||
|
layout = {
|
||||||
|
"plot_bgcolor": "white",
|
||||||
|
"paper_bgcolor": "white",
|
||||||
|
"margin": {"t": 10, "b": 10, "l": 10, "r": 10, "pad": 0},
|
||||||
|
"xaxis": {
|
||||||
|
"linecolor": "black",
|
||||||
|
"showgrid": False,
|
||||||
|
"showticklabels": False,
|
||||||
|
"mirror": True,
|
||||||
|
},
|
||||||
|
"yaxis": {
|
||||||
|
"linecolor": "black",
|
||||||
|
"showgrid": False,
|
||||||
|
"showticklabels": False,
|
||||||
|
"mirror": True,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# figure
|
||||||
|
return go.Figure(data=[edge_trace, node_trace], layout=layout)
|
||||||
|
|
||||||
|
|
||||||
|
def networkx_component(company_id: int) -> html.Div:
|
||||||
|
"""Retruns the Layout with a Graph.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
company_id (int): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
any: _description_
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
[
|
||||||
|
dcc.Graph(id="my-graph", figure=network_graph(company_id)),
|
||||||
|
]
|
||||||
|
)
|
||||||
186
Jupyter/NetworkX/archive_prod/networkx_dash_overall.py
Normal file
186
Jupyter/NetworkX/archive_prod/networkx_dash_overall.py
Normal file
@ -0,0 +1,186 @@
|
|||||||
|
"""Old Module for NetworkX Graphs."""
|
||||||
|
import networkx as nx
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
from dash import Dash, Input, Output, dcc, html
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
|
||||||
|
from aki_prj23_transparenzregister.utils.sql import connector, entities
|
||||||
|
|
||||||
|
test_company = 13 # 2213 # 13
|
||||||
|
|
||||||
|
|
||||||
|
def find_all_company_relations() -> pd.DataFrame:
|
||||||
|
"""Searches for all companies and their relation in the DB.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: _description_
|
||||||
|
"""
|
||||||
|
session = connector.get_session(JsonFileConfigProvider("./secrets.json"))
|
||||||
|
query_companies = session.query(entities.Company) # .all()
|
||||||
|
query_relations = session.query(entities.CompanyRelation) # .all()
|
||||||
|
|
||||||
|
companies_df: pd.DataFrame = pd.read_sql(str(query_companies), session.bind) # type: ignore
|
||||||
|
companies_relations_df: pd.DataFrame = pd.read_sql(str(query_relations), session.bind) # type: ignore
|
||||||
|
# print(companies_relations_df)
|
||||||
|
companies_relations_df = companies_relations_df[
|
||||||
|
["relation_id", "company_relation_company2_id"]
|
||||||
|
]
|
||||||
|
# print(companies_relations_df)
|
||||||
|
company_name = []
|
||||||
|
connected_company_name = []
|
||||||
|
|
||||||
|
companies_relations_df = companies_relations_df.head()
|
||||||
|
# print(companies_relations_df)
|
||||||
|
|
||||||
|
for _, row in companies_relations_df.iterrows():
|
||||||
|
company_name.append(
|
||||||
|
companies_df.loc[companies_df["company_id"] == row["relation_id"]][
|
||||||
|
"company_name"
|
||||||
|
].iloc[0]
|
||||||
|
)
|
||||||
|
|
||||||
|
connected_company_name.append(
|
||||||
|
companies_df.loc[
|
||||||
|
companies_df["company_id"] == row["company_relation_company2_id"]
|
||||||
|
]["company_name"].iloc[0]
|
||||||
|
)
|
||||||
|
# print(connected_company_name)
|
||||||
|
|
||||||
|
# print(company_name)
|
||||||
|
companies_relations_df["company_name"] = company_name
|
||||||
|
companies_relations_df["connected_company_name"] = connected_company_name
|
||||||
|
# print("Test")
|
||||||
|
# print(companies_relations_df)
|
||||||
|
return companies_relations_df
|
||||||
|
|
||||||
|
|
||||||
|
# Plotly figure
|
||||||
|
def create_network_graph() -> go.Figure:
|
||||||
|
"""Create a NetworkX Graph.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
go.Figure: _description_
|
||||||
|
"""
|
||||||
|
edges = []
|
||||||
|
for _, row in find_all_company_relations().iterrows():
|
||||||
|
edges.append([row["company_name"], row["connected_company_name"]])
|
||||||
|
|
||||||
|
network_graph = nx.Graph()
|
||||||
|
network_graph.add_edges_from(edges)
|
||||||
|
pos = nx.spring_layout(network_graph)
|
||||||
|
|
||||||
|
# edges trace
|
||||||
|
edge_x = []
|
||||||
|
edge_y = []
|
||||||
|
for edge in network_graph.edges():
|
||||||
|
x0, y0 = pos[edge[0]]
|
||||||
|
x1, y1 = pos[edge[1]]
|
||||||
|
edge_x.append(x0)
|
||||||
|
edge_x.append(x1)
|
||||||
|
edge_x.append(None)
|
||||||
|
edge_y.append(y0)
|
||||||
|
edge_y.append(y1)
|
||||||
|
edge_y.append(None)
|
||||||
|
|
||||||
|
edge_trace = go.Scatter(
|
||||||
|
x=edge_x,
|
||||||
|
y=edge_y,
|
||||||
|
line={"color": "black", "width": 1},
|
||||||
|
hoverinfo="none",
|
||||||
|
showlegend=False,
|
||||||
|
mode="lines",
|
||||||
|
)
|
||||||
|
|
||||||
|
# nodes trace
|
||||||
|
node_x = []
|
||||||
|
node_y = []
|
||||||
|
text = []
|
||||||
|
for node in network_graph.nodes():
|
||||||
|
x, y = pos[node]
|
||||||
|
node_x.append(x)
|
||||||
|
node_y.append(y)
|
||||||
|
text.append(node)
|
||||||
|
|
||||||
|
node_trace = go.Scatter(
|
||||||
|
x=node_x,
|
||||||
|
y=node_y,
|
||||||
|
text=text,
|
||||||
|
mode="markers+text",
|
||||||
|
showlegend=False,
|
||||||
|
hoverinfo="none",
|
||||||
|
marker={"color": "pink", "size": 50, "line": {"color": "black", "width": 1}},
|
||||||
|
)
|
||||||
|
|
||||||
|
# layout
|
||||||
|
layout = {
|
||||||
|
"plot_bgcolor": "white",
|
||||||
|
"paper_bgcolor": "white",
|
||||||
|
"margin": {"t": 10, "b": 10, "l": 10, "r": 10, "pad": 0},
|
||||||
|
"xaxis": {
|
||||||
|
"linecolor": "black",
|
||||||
|
"showgrid": False,
|
||||||
|
"showticklabels": False,
|
||||||
|
"mirror": True,
|
||||||
|
},
|
||||||
|
"yaxis": {
|
||||||
|
"linecolor": "black",
|
||||||
|
"showgrid": False,
|
||||||
|
"showticklabels": False,
|
||||||
|
"mirror": True,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
measure_vector = {}
|
||||||
|
network_metrics_df = pd.DataFrame()
|
||||||
|
|
||||||
|
measure_vector = nx.eigenvector_centrality(network_graph)
|
||||||
|
network_metrics_df["eigenvector"] = measure_vector.values()
|
||||||
|
|
||||||
|
measure_vector = nx.degree_centrality(network_graph)
|
||||||
|
network_metrics_df["degree"] = measure_vector.values()
|
||||||
|
|
||||||
|
measure_vector = nx.betweenness_centrality(network_graph)
|
||||||
|
network_metrics_df["betweeness"] = measure_vector.values()
|
||||||
|
|
||||||
|
measure_vector = nx.closeness_centrality(network_graph)
|
||||||
|
network_metrics_df["closeness"] = measure_vector.values()
|
||||||
|
|
||||||
|
# figure
|
||||||
|
return go.Figure(data=[edge_trace, node_trace], layout=layout)
|
||||||
|
|
||||||
|
|
||||||
|
# Dash App
|
||||||
|
app = Dash(__name__)
|
||||||
|
|
||||||
|
app.title = "Dash Networkx"
|
||||||
|
# className="networkx_style"
|
||||||
|
app.layout = html.Div(
|
||||||
|
style={"width": "49%"},
|
||||||
|
children=[
|
||||||
|
html.I("Write your EDGE_VAR"),
|
||||||
|
html.Br(),
|
||||||
|
# dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='metric-dropdown'),
|
||||||
|
dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
|
||||||
|
dcc.Graph(id="my-graph", style={"width": "49%"}),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@app.callback(
|
||||||
|
Output("my-graph", "figure"),
|
||||||
|
# Input('metric-dropdown', 'value'),
|
||||||
|
[Input("EGDE_VAR", "value")],
|
||||||
|
)
|
||||||
|
def update_output() -> go.Figure:
|
||||||
|
"""Just Returns the go Figure of Plotly.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
go.Figure: Returns a HTML Figure for Plotly.
|
||||||
|
"""
|
||||||
|
return create_network_graph()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
"""Main Method to test this page."""
|
||||||
|
app.run(debug=True)
|
||||||
366
Jupyter/NetworkX/archive_prod/ui_elements.py
Normal file
366
Jupyter/NetworkX/archive_prod/ui_elements.py
Normal file
@ -0,0 +1,366 @@
|
|||||||
|
"""Dash elements."""
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objs as go
|
||||||
|
from cachetools import TTLCache, cached
|
||||||
|
from dash import dash_table, dcc, html
|
||||||
|
from sqlalchemy.engine import Engine
|
||||||
|
from sqlalchemy.orm import Session
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.ui.archive.networkx_dash import networkx_component
|
||||||
|
from aki_prj23_transparenzregister.utils.sql import entities
|
||||||
|
|
||||||
|
COLORS = {
|
||||||
|
"light": "#edefef",
|
||||||
|
"lavender-blush": "#f3e8ee",
|
||||||
|
"ash-gray": "#bacdb0",
|
||||||
|
"cambridge-blue": "#729b79",
|
||||||
|
"paynes-gray": "#475b63",
|
||||||
|
"raisin-black": "#2e2c2f",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def get_company_data(session: Session) -> pd.DataFrame:
|
||||||
|
"""Creates a session to the database and get's all available company data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
session: A session connecting to the database.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A dataframe containing all available company data including the corresponding district court.
|
||||||
|
"""
|
||||||
|
query_company = session.query(entities.Company, entities.DistrictCourt.name).join(
|
||||||
|
entities.DistrictCourt
|
||||||
|
)
|
||||||
|
engine = session.bind
|
||||||
|
if not isinstance(engine, Engine):
|
||||||
|
raise TypeError
|
||||||
|
|
||||||
|
return pd.read_sql(str(query_company), engine, index_col="company_id")
|
||||||
|
|
||||||
|
|
||||||
|
def get_finance_data(session: Session) -> pd.DataFrame:
|
||||||
|
"""Collects all available company data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
session: A session connecting to the database.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A dataframe containing all financial data of all companies.
|
||||||
|
"""
|
||||||
|
query_finance = session.query(
|
||||||
|
entities.AnnualFinanceStatement, entities.Company.name, entities.Company.id
|
||||||
|
).join(entities.Company)
|
||||||
|
|
||||||
|
engine = session.bind
|
||||||
|
if not isinstance(engine, Engine):
|
||||||
|
raise TypeError
|
||||||
|
|
||||||
|
return pd.read_sql(str(query_finance), engine)
|
||||||
|
|
||||||
|
|
||||||
|
@cached( # type: ignore
|
||||||
|
cache=TTLCache(maxsize=1, ttl=300),
|
||||||
|
key=lambda session: 0 if session is None else str(session.bind),
|
||||||
|
)
|
||||||
|
def get_options(session: Session | None) -> dict[int, str]:
|
||||||
|
"""Collects the search options for the companies.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
session: A session connecting to the database.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A dict containing the company id as key and its name.
|
||||||
|
"""
|
||||||
|
if not session:
|
||||||
|
return {}
|
||||||
|
return get_company_data(session)["company_name"].to_dict()
|
||||||
|
|
||||||
|
|
||||||
|
def create_header(options: dict) -> html:
|
||||||
|
"""Creates header for dashboard.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
options: A dictionary with company names and ids for the dropdown.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the page's header including the name of the page and the search for companies.
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
className="header-wrapper",
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="header-title",
|
||||||
|
children=[
|
||||||
|
html.I(
|
||||||
|
id="home-button",
|
||||||
|
n_clicks=0,
|
||||||
|
className="bi-house-door-fill",
|
||||||
|
),
|
||||||
|
html.H1(
|
||||||
|
className="header-title-text",
|
||||||
|
children="Transparenzregister für Kapitalgesellschaften",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="header-search",
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="header-search-dropdown",
|
||||||
|
children=[
|
||||||
|
dcc.Dropdown(
|
||||||
|
id="select_company",
|
||||||
|
options=[
|
||||||
|
{"label": o, "value": key}
|
||||||
|
for key, o in options.items()
|
||||||
|
],
|
||||||
|
placeholder="Suche nach Unternehmen oder Person",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def create_company_header(selected_company_name: str) -> html:
|
||||||
|
"""Create company header based on selected company.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_name: The company name that has been chosen in the dropdown.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the company header.
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
className="company-header",
|
||||||
|
children=[
|
||||||
|
html.H1(
|
||||||
|
className="company-header-title",
|
||||||
|
id="id-company-header-title",
|
||||||
|
children=selected_company_name,
|
||||||
|
),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def create_company_stats(selected_company_data: pd.Series) -> html:
|
||||||
|
"""Create company stats.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_data: A series containing all company information of the selected company.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the company stats table and the three small widgets.
|
||||||
|
"""
|
||||||
|
company_data = {
|
||||||
|
"col1": ["Unternehmen", "Straße", "Stadt"],
|
||||||
|
"col2": [
|
||||||
|
selected_company_data["company_name"],
|
||||||
|
selected_company_data["company_street"],
|
||||||
|
str(
|
||||||
|
selected_company_data["company_zip_code"]
|
||||||
|
+ " "
|
||||||
|
+ selected_company_data["company_city"]
|
||||||
|
),
|
||||||
|
],
|
||||||
|
"col3": ["Branche", "Amtsgericht", "Gründungsjahr"],
|
||||||
|
"col4": [
|
||||||
|
selected_company_data["company_sector"],
|
||||||
|
selected_company_data["district_court_name"],
|
||||||
|
"xxx",
|
||||||
|
],
|
||||||
|
}
|
||||||
|
df_company_data = pd.DataFrame(data=company_data)
|
||||||
|
return html.Div(
|
||||||
|
className="stats-wrapper",
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="widget-large",
|
||||||
|
children=[
|
||||||
|
html.H3(
|
||||||
|
className="widget-title",
|
||||||
|
children="Stammdaten",
|
||||||
|
),
|
||||||
|
dash_table.DataTable(
|
||||||
|
df_company_data.to_dict("records"),
|
||||||
|
[{"name": i, "id": i} for i in df_company_data.columns],
|
||||||
|
style_table={
|
||||||
|
"width": "90%",
|
||||||
|
"marginLeft": "auto",
|
||||||
|
"marginRight": "auto",
|
||||||
|
"paddingBottom": "20px",
|
||||||
|
"color": COLORS["raisin-black"],
|
||||||
|
},
|
||||||
|
# hide header of table
|
||||||
|
css=[
|
||||||
|
{
|
||||||
|
"selector": "tr:first-child",
|
||||||
|
"rule": "display: none",
|
||||||
|
},
|
||||||
|
],
|
||||||
|
style_cell={"textAlign": "center"},
|
||||||
|
style_cell_conditional=[
|
||||||
|
{"if": {"column_id": c}, "fontWeight": "bold"}
|
||||||
|
for c in ["col1", "col3"]
|
||||||
|
],
|
||||||
|
style_data={
|
||||||
|
"whiteSpace": "normal",
|
||||||
|
"height": "auto",
|
||||||
|
},
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="widget-small",
|
||||||
|
children=[
|
||||||
|
html.H3(
|
||||||
|
className="widget-title",
|
||||||
|
children="Stimmung",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="widget-small",
|
||||||
|
children=[
|
||||||
|
html.H3(
|
||||||
|
className="widget-title",
|
||||||
|
children="Aktienkurs",
|
||||||
|
),
|
||||||
|
html.H1(
|
||||||
|
className="widget-content",
|
||||||
|
children="123",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="widget-small",
|
||||||
|
children=[
|
||||||
|
html.H3(
|
||||||
|
className="widget-title",
|
||||||
|
children="Umsatz",
|
||||||
|
),
|
||||||
|
html.H1(
|
||||||
|
className="widget-content",
|
||||||
|
children="1234",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def create_tabs(selected_company_id: int, selected_finance_df: pd.DataFrame) -> html:
|
||||||
|
"""Create tabs for more company information.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_id: Id of the chosen company in the dropdown.
|
||||||
|
selected_finance_df: A dataframe containing all available finance information of the companies.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the tabs of the company page.
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
className="tabs",
|
||||||
|
children=[
|
||||||
|
dcc.Tabs(
|
||||||
|
id="tabs",
|
||||||
|
value="tab-1",
|
||||||
|
children=[
|
||||||
|
dcc.Tab(
|
||||||
|
label="Kennzahlen",
|
||||||
|
value="tab-1",
|
||||||
|
className="tab-style",
|
||||||
|
selected_className="selected-tab-style",
|
||||||
|
children=[kennzahlen_layout(selected_finance_df)],
|
||||||
|
),
|
||||||
|
dcc.Tab(
|
||||||
|
label="Beteiligte Personen",
|
||||||
|
value="tab-2",
|
||||||
|
className="tab-style",
|
||||||
|
selected_className="selected-tab-style",
|
||||||
|
),
|
||||||
|
dcc.Tab(
|
||||||
|
label="Stimmung",
|
||||||
|
value="tab-3",
|
||||||
|
className="tab-style",
|
||||||
|
selected_className="selected-tab-style",
|
||||||
|
),
|
||||||
|
dcc.Tab(
|
||||||
|
label="Verflechtungen",
|
||||||
|
value="tab-4",
|
||||||
|
className="tab-style",
|
||||||
|
selected_className="selected-tab-style",
|
||||||
|
children=[network_layout(selected_company_id)],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(id="tabs-example-content-1"),
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def kennzahlen_layout(selected_finance_df: pd.DataFrame) -> html:
|
||||||
|
"""Create metrics tab.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_id: Id of the chosen company in the dropdown.
|
||||||
|
selected_finance_df: A dataframe containing all available finance information of the companies.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the metrics tab of the company page.
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
[
|
||||||
|
dcc.Graph(
|
||||||
|
figure=financials_figure(
|
||||||
|
selected_finance_df, "annual_finance_statement_ebit"
|
||||||
|
)
|
||||||
|
)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def financials_figure(selected_finance_df: pd.DataFrame, metric: str) -> go.Figure:
|
||||||
|
"""Creates plotly line chart for a specific company and a metric.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_finance_df: A dataframe containing all finance information of the selected company.
|
||||||
|
metric: The metric that should be visualized.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
A plotly figure showing the available metric data of the company.
|
||||||
|
"""
|
||||||
|
# create figure
|
||||||
|
fig_line = go.Figure()
|
||||||
|
# add trace for company 1
|
||||||
|
fig_line.add_trace(
|
||||||
|
go.Scatter(
|
||||||
|
x=selected_finance_df["annual_finance_statement_date"],
|
||||||
|
y=selected_finance_df[metric],
|
||||||
|
line_color=COLORS["raisin-black"],
|
||||||
|
marker_color=COLORS["raisin-black"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
# set title and labels
|
||||||
|
fig_line.update_layout(
|
||||||
|
title=metric,
|
||||||
|
xaxis_title="Jahr",
|
||||||
|
yaxis_title="in Mio.€",
|
||||||
|
plot_bgcolor=COLORS["light"],
|
||||||
|
)
|
||||||
|
return fig_line
|
||||||
|
|
||||||
|
|
||||||
|
def network_layout(selected_company_id: int) -> html:
|
||||||
|
"""Create network tab.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_id: Id of the chosen company in the dropdown.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the network tab of the company page.
|
||||||
|
"""
|
||||||
|
return networkx_component(selected_company_id)
|
||||||
22
documentations/meeting-notes/Meeting_2023-10-12.md
Normal file
22
documentations/meeting-notes/Meeting_2023-10-12.md
Normal file
@ -0,0 +1,22 @@
|
|||||||
|
# Weekly *12*: 12.10.2023
|
||||||
|
|
||||||
|
## Teilnehmer
|
||||||
|
- Prof. Arinir
|
||||||
|
- Tristan Nolde
|
||||||
|
- Tim Ronneburg
|
||||||
|
- Philipp Horstenkamp
|
||||||
|
- Kim Mesewinkel-Risse
|
||||||
|
- Sascha Zhu
|
||||||
|
- Sebastian Zeleny
|
||||||
|
|
||||||
|
## Themen
|
||||||
|
|
||||||
|
- ABC:
|
||||||
|
- ...
|
||||||
|
|
||||||
|
## Abgeleitete Action Items
|
||||||
|
|
||||||
|
| Action Item | Verantwortlicher | Deadline |
|
||||||
|
|-------------|------------------|-----------------|
|
||||||
|
| Finanzdaten optimieren | Kim, Tristan | nächstes Weekly |
|
||||||
|
| Rebasen vom Branch für Sascha | Sascha | nächstes Weekly |
|
||||||
BIN
documentations/plots/first_complete_force_graph.png
Normal file
BIN
documentations/plots/first_complete_force_graph.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 406 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 1.2 KiB After Width: | Height: | Size: 2.3 KiB |
@ -0,0 +1,180 @@
|
|||||||
|
<html>
|
||||||
|
<head>
|
||||||
|
<meta charset="utf-8">
|
||||||
|
|
||||||
|
<script src="lib/bindings/utils.js"></script>
|
||||||
|
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css" integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA==" crossorigin="anonymous" referrerpolicy="no-referrer" />
|
||||||
|
<script src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js" integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
|
||||||
|
|
||||||
|
|
||||||
|
<center>
|
||||||
|
<h1></h1>
|
||||||
|
</center>
|
||||||
|
|
||||||
|
<!-- <link rel="stylesheet" href="../node_modules/vis/dist/vis.min.css" type="text/css" />
|
||||||
|
<script type="text/javascript" src="../node_modules/vis/dist/vis.js"> </script>-->
|
||||||
|
<link
|
||||||
|
href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css"
|
||||||
|
rel="stylesheet"
|
||||||
|
integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6"
|
||||||
|
crossorigin="anonymous"
|
||||||
|
/>
|
||||||
|
<script
|
||||||
|
src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/js/bootstrap.bundle.min.js"
|
||||||
|
integrity="sha384-JEW9xMcG8R+pH31jmWH6WWP0WintQrMb4s7ZOdauHnUtxwoG2vI5DkLtS3qm9Ekf"
|
||||||
|
crossorigin="anonymous"
|
||||||
|
></script>
|
||||||
|
|
||||||
|
|
||||||
|
<center>
|
||||||
|
<h1></h1>
|
||||||
|
</center>
|
||||||
|
<style type="text/css">
|
||||||
|
|
||||||
|
#mynetwork {
|
||||||
|
width: 100%;
|
||||||
|
height: 600px;
|
||||||
|
background-color: white;
|
||||||
|
border: 1px solid lightgray;
|
||||||
|
position: relative;
|
||||||
|
float: left;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#config {
|
||||||
|
float: left;
|
||||||
|
width: 400px;
|
||||||
|
height: 600px;
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
</style>
|
||||||
|
</head>
|
||||||
|
|
||||||
|
|
||||||
|
<body>
|
||||||
|
<div class="card" style="width: 100%">
|
||||||
|
|
||||||
|
|
||||||
|
<div id="mynetwork" class="card-body"></div>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
<div id="config"></div>
|
||||||
|
|
||||||
|
|
||||||
|
<script type="text/javascript">
|
||||||
|
|
||||||
|
// initialize global variables.
|
||||||
|
var edges;
|
||||||
|
var nodes;
|
||||||
|
var allNodes;
|
||||||
|
var allEdges;
|
||||||
|
var nodeColors;
|
||||||
|
var originalNodes;
|
||||||
|
var network;
|
||||||
|
var container;
|
||||||
|
var options, data;
|
||||||
|
var filter = {
|
||||||
|
item : '',
|
||||||
|
property : '',
|
||||||
|
value : []
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
// This method is responsible for drawing the graph, returns the drawn network
|
||||||
|
function drawGraph() {
|
||||||
|
var container = document.getElementById('mynetwork');
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
// parsing and collecting nodes and edges from the python
|
||||||
|
nodes = new vis.DataSet([{"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 2, "label": "Volkswagen AG", "shape": "dot", "size": 10, "title": "Volkswagen AG\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 1, "label": "Porsche Automobil Holding", "shape": "dot", "size": 10, "title": "Porsche Automobil Holding\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 3, "label": "Volkswagen", "shape": "dot", "size": 10, "title": "Volkswagen\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 4, "label": "Audi", "shape": "dot", "size": 10, "title": "Audi\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 5, "label": "Seat", "shape": "dot", "size": 10, "title": "Seat\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 6, "label": "Skoda Auto", "shape": "dot", "size": 10, "title": "Skoda Auto\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 7, "label": "Porsche AG", "shape": "dot", "size": 10, "title": "Porsche AG\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 8, "label": "Lamborghini", "shape": "dot", "size": 10, "title": "Lamborghini\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilhersteller", "color": " #729b79ff", "font": {"color": "black"}, "id": 9, "label": "Bentley", "shape": "dot", "size": 10, "title": "Bentley\nAutomobilhersteller", "type": "Company"}, {"branche": "Automobilzulieferer", "color": "#475b63ff", "font": {"color": "black"}, "id": 10, "label": "Forvia", "shape": "dot", "size": 10, "title": "Forvia\nAutomobilzulieferer", "type": "Company"}, {"branche": "Automobilzulieferer", "color": "#475b63ff", "font": {"color": "black"}, "id": 11, "label": "Hella", "shape": "dot", "size": 10, "title": "Hella\nAutomobilzulieferer", "type": "Company"}]);
|
||||||
|
edges = new vis.DataSet([{"from": 2, "label": "part_of", "to": 1, "width": 1}, {"from": 1, "label": "part_of", "to": 3, "width": 1}, {"from": 1, "label": "part_of", "to": 4, "width": 1}, {"from": 1, "label": "part_of", "to": 5, "width": 1}, {"from": 1, "label": "part_of", "to": 6, "width": 1}, {"from": 1, "label": "part_of", "to": 7, "width": 1}, {"from": 4, "label": "part_of", "to": 8, "width": 1}, {"from": 4, "label": "part_of", "to": 9, "width": 1}, {"from": 4, "label": "supplierer", "to": 10, "width": 1}, {"from": 11, "label": "part_of", "to": 10, "width": 1}]);
|
||||||
|
|
||||||
|
nodeColors = {};
|
||||||
|
allNodes = nodes.get({ returnType: "Object" });
|
||||||
|
for (nodeId in allNodes) {
|
||||||
|
nodeColors[nodeId] = allNodes[nodeId].color;
|
||||||
|
}
|
||||||
|
allEdges = edges.get({ returnType: "Object" });
|
||||||
|
// adding nodes and edges to the graph
|
||||||
|
data = {nodes: nodes, edges: edges};
|
||||||
|
|
||||||
|
var options = {
|
||||||
|
"configure": {
|
||||||
|
"enabled": true,
|
||||||
|
"filter": [
|
||||||
|
"physics"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"edges": {
|
||||||
|
"color": {
|
||||||
|
"inherit": false
|
||||||
|
},
|
||||||
|
"smooth": {
|
||||||
|
"enabled": true,
|
||||||
|
"type": "dynamic"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"interaction": {
|
||||||
|
"dragNodes": true,
|
||||||
|
"hideEdgesOnDrag": false,
|
||||||
|
"hideNodesOnDrag": false
|
||||||
|
},
|
||||||
|
"physics": {
|
||||||
|
"enabled": true,
|
||||||
|
"repulsion": {
|
||||||
|
"centralGravity": 0.2,
|
||||||
|
"damping": 0.09,
|
||||||
|
"nodeDistance": 150,
|
||||||
|
"springConstant": 0.05,
|
||||||
|
"springLength": 50
|
||||||
|
},
|
||||||
|
"solver": "repulsion",
|
||||||
|
"stabilization": {
|
||||||
|
"enabled": true,
|
||||||
|
"fit": true,
|
||||||
|
"iterations": 1000,
|
||||||
|
"onlyDynamicEdges": false,
|
||||||
|
"updateInterval": 50
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
// if this network requires displaying the configure window,
|
||||||
|
// put it in its div
|
||||||
|
options.configure["container"] = document.getElementById("config");
|
||||||
|
|
||||||
|
|
||||||
|
network = new vis.Network(container, data, options);
|
||||||
|
|
||||||
|
|
||||||
|
network.on("click", neighbourhoodHighlight);
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
return network;
|
||||||
|
|
||||||
|
}
|
||||||
|
drawGraph();
|
||||||
|
</script>
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
File diff suppressed because one or more lines are too long
823
poetry.lock
generated
823
poetry.lock
generated
File diff suppressed because it is too large
Load Diff
@ -55,6 +55,9 @@ cachetools = "^5.3.1"
|
|||||||
dash = "^2.14.1"
|
dash = "^2.14.1"
|
||||||
dash-auth = "^2.0.0"
|
dash-auth = "^2.0.0"
|
||||||
dash-bootstrap-components = "^1.5.0"
|
dash-bootstrap-components = "^1.5.0"
|
||||||
|
dash-daq = "^0.5.0"
|
||||||
|
dash_cytoscape = "^0.2.0"
|
||||||
|
dashvis = "^0.1.3"
|
||||||
datetime = "^5.2"
|
datetime = "^5.2"
|
||||||
deutschland = {git = "https://github.com/TrisNol/deutschland.git", branch = "hotfix/python-3.11-support"}
|
deutschland = {git = "https://github.com/TrisNol/deutschland.git", branch = "hotfix/python-3.11-support"}
|
||||||
frozendict = "^2.3.8"
|
frozendict = "^2.3.8"
|
||||||
@ -63,9 +66,10 @@ matplotlib = "^3.8.1"
|
|||||||
pgeocode = "^0.4.1"
|
pgeocode = "^0.4.1"
|
||||||
psycopg2-binary = "^2.9.7"
|
psycopg2-binary = "^2.9.7"
|
||||||
pymongo = "^4.6.0"
|
pymongo = "^4.6.0"
|
||||||
python = "^3.11"
|
python = ">=3.11,<3.13"
|
||||||
python-dotenv = "^1.0.0"
|
python-dotenv = "^1.0.0"
|
||||||
rapidfuzz = "^3.5.2"
|
rapidfuzz = "^3.5.2"
|
||||||
|
scipy = "^1.11.3"
|
||||||
seaborn = "^0.13.0"
|
seaborn = "^0.13.0"
|
||||||
selenium = "^4.15.2"
|
selenium = "^4.15.2"
|
||||||
spacy = "^3.6.1"
|
spacy = "^3.6.1"
|
||||||
@ -109,6 +113,7 @@ SQLAlchemy = {version = "*", extras = ["mypy"]}
|
|||||||
black = "*"
|
black = "*"
|
||||||
loguru-mypy = "*"
|
loguru-mypy = "*"
|
||||||
mypy = "*"
|
mypy = "*"
|
||||||
|
networkx-stubs = "*"
|
||||||
pandas-stubs = "*"
|
pandas-stubs = "*"
|
||||||
pip-audit = "*"
|
pip-audit = "*"
|
||||||
pip-licenses = "*"
|
pip-licenses = "*"
|
||||||
@ -121,6 +126,7 @@ types-setuptools = "*"
|
|||||||
types-six = "*"
|
types-six = "*"
|
||||||
types-tabulate = "*"
|
types-tabulate = "*"
|
||||||
types-tqdm = "*"
|
types-tqdm = "*"
|
||||||
|
types-urllib3 = "*"
|
||||||
|
|
||||||
[tool.poetry.group.test.dependencies]
|
[tool.poetry.group.test.dependencies]
|
||||||
pytest = "^7.4.2"
|
pytest = "^7.4.2"
|
||||||
|
|||||||
112
src/aki_prj23_transparenzregister/ui/assets/networkx_style.css
Normal file
112
src/aki_prj23_transparenzregister/ui/assets/networkx_style.css
Normal file
@ -0,0 +1,112 @@
|
|||||||
|
.networkx_style {
|
||||||
|
float: right;
|
||||||
|
margin-top: 20px;
|
||||||
|
margin-left: 10px;
|
||||||
|
margin-right: 20px;
|
||||||
|
border: 1px solid;
|
||||||
|
border-color: var(--raisin-black);
|
||||||
|
border-radius: 20px;
|
||||||
|
width: 57%;
|
||||||
|
height: 100%;
|
||||||
|
}
|
||||||
|
|
||||||
|
.top_companytable_style {
|
||||||
|
float: left;
|
||||||
|
margin-top: 20px;
|
||||||
|
margin-left: 20px;
|
||||||
|
margin-right: 10px;
|
||||||
|
border: 1px solid;
|
||||||
|
border-color: var(--raisin-black);
|
||||||
|
border-radius: 20px;
|
||||||
|
width: 37%;
|
||||||
|
height: 100%;
|
||||||
|
}
|
||||||
|
.networkx_style .filter-wrapper {
|
||||||
|
float: left;
|
||||||
|
width: 100%;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .filter-wrapper .filter-wrapper-item {
|
||||||
|
display: inline-block;
|
||||||
|
padding: 10px;
|
||||||
|
width: 31%;
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .filter-wrapper .filter-wrapper-item .dropdown_style {
|
||||||
|
padding-bottom: 10px;
|
||||||
|
margin-top: 1px;
|
||||||
|
padding-left: 10px;
|
||||||
|
width: 100%;
|
||||||
|
font-size: '30%'
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .header {
|
||||||
|
text-align: center;
|
||||||
|
align-items: center;
|
||||||
|
align-content: center;
|
||||||
|
vertical-align: middle;
|
||||||
|
padding: 20px;
|
||||||
|
margin: 0px;
|
||||||
|
/* margin: 5px; */
|
||||||
|
color: var(--raisin-black);
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .filter-wrapper .filter-wrapper-item .filter-description {
|
||||||
|
padding: 5px;
|
||||||
|
padding-left: 10px;
|
||||||
|
margin: 0px;
|
||||||
|
/* margin: 5px; */
|
||||||
|
color: var(--raisin-black);
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .switch-style {
|
||||||
|
align-self: left;
|
||||||
|
float: none;
|
||||||
|
padding-bottom: 10px;
|
||||||
|
margin-top: 1px;
|
||||||
|
padding-left: 10px;
|
||||||
|
width: 80%;
|
||||||
|
display: inline-block;
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .switch-style .jAjsxw {
|
||||||
|
align-self: left;
|
||||||
|
float: none;
|
||||||
|
display: inline-block;
|
||||||
|
padding: 0px;
|
||||||
|
margin: 0px;
|
||||||
|
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .graph-style {
|
||||||
|
|
||||||
|
margin-top: 10px;
|
||||||
|
padding-bottom: 0px;
|
||||||
|
display: inline-block;
|
||||||
|
margin: 0px;
|
||||||
|
width: 100%;
|
||||||
|
height: 100%;
|
||||||
|
/* float: inline-end, */
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
.networkx_style .graph-style .canvas {
|
||||||
|
padding: 0px;
|
||||||
|
margin: 0px;
|
||||||
|
width: 100%
|
||||||
|
/* background-color: var(--raisin-black); */
|
||||||
|
|
||||||
|
}
|
||||||
@ -10,6 +10,12 @@ from dash import dash_table, dcc, html
|
|||||||
from sqlalchemy.orm import Session
|
from sqlalchemy.orm import Session
|
||||||
|
|
||||||
from aki_prj23_transparenzregister.ui import data_elements, finance_elements
|
from aki_prj23_transparenzregister.ui import data_elements, finance_elements
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_2d import create_2d_graph
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
|
||||||
|
create_edge_and_node_list_for_company,
|
||||||
|
find_company_relations,
|
||||||
|
)
|
||||||
|
|
||||||
COLORS = {
|
COLORS = {
|
||||||
"light": "#edefef",
|
"light": "#edefef",
|
||||||
@ -344,7 +350,27 @@ def person_relations_layout(selected_company_id: int, session: Session) -> html:
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
def network_layout(selected_company_id: int) -> html:
|
def kennzahlen_layout(selected_finance_df: pd.DataFrame) -> html:
|
||||||
|
"""Create metrics tab.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_finance_df: A dataframe containing all available finance information of the companies.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
The html div to create the metrics tab of the company page.
|
||||||
|
"""
|
||||||
|
return html.Div(
|
||||||
|
[
|
||||||
|
dcc.Graph(
|
||||||
|
figure=finance_elements.financials_figure(
|
||||||
|
selected_finance_df, "annual_finance_statement_ebit"
|
||||||
|
)
|
||||||
|
)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def network_layout(selected_company_id: int) -> html.Div:
|
||||||
"""Create network tab.
|
"""Create network tab.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
@ -353,4 +379,34 @@ def network_layout(selected_company_id: int) -> html:
|
|||||||
Returns:
|
Returns:
|
||||||
The html div to create the network tab of the company page.
|
The html div to create the network tab of the company page.
|
||||||
"""
|
"""
|
||||||
return html.Div([f"Netzwerk von Unternehmen mit ID: {selected_company_id}"])
|
person_relations, company_relations = find_company_relations(selected_company_id)
|
||||||
|
|
||||||
|
# Create Edge and Node List from data
|
||||||
|
nodes, edges = create_edge_and_node_list_for_company(company_relations)
|
||||||
|
# Initialize the Network and receive the Graph and a DataFrame with Metrics
|
||||||
|
if nodes != {}:
|
||||||
|
graph, metrics = initialize_network(nodes=nodes, edges=edges)
|
||||||
|
metric = "None"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph,
|
||||||
|
nodes,
|
||||||
|
edges,
|
||||||
|
metrics,
|
||||||
|
metric,
|
||||||
|
layout="Spring",
|
||||||
|
edge_annotation=True,
|
||||||
|
edge_thickness=1,
|
||||||
|
)
|
||||||
|
return html.Div(
|
||||||
|
children=[
|
||||||
|
dcc.Graph(figure=figure, id="company-graph", className="graph-style")
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
return html.Div(
|
||||||
|
[
|
||||||
|
html.H3(
|
||||||
|
f"Leider gibt es keine Verbindungen vom Unternehmen mit ID: {selected_company_id}"
|
||||||
|
)
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|||||||
@ -1,22 +1,409 @@
|
|||||||
"""Content of home page."""
|
"""Content of home page."""
|
||||||
|
from functools import lru_cache
|
||||||
|
|
||||||
import dash
|
import dash
|
||||||
from dash import html
|
import dash_daq as daq
|
||||||
|
import networkx as nx
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
from cachetools import TTLCache, cached
|
||||||
|
from dash import Input, Output, callback, dash_table, dcc, html
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_2d import (
|
||||||
|
create_2d_graph,
|
||||||
|
)
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_3d import (
|
||||||
|
# initialize_network,
|
||||||
|
create_3d_graph,
|
||||||
|
)
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import (
|
||||||
|
initialize_network,
|
||||||
|
)
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
|
||||||
|
create_edge_and_node_list,
|
||||||
|
filter_relation_type,
|
||||||
|
get_all_company_relations,
|
||||||
|
get_all_person_relations,
|
||||||
|
)
|
||||||
|
|
||||||
dash.register_page(
|
dash.register_page(
|
||||||
__name__,
|
__name__,
|
||||||
path="/",
|
path="/",
|
||||||
title="Transparenzregister: Home",
|
title="Transparenzregister: Home",
|
||||||
redirect_from=[
|
# TODO Fix redirect in unit tests
|
||||||
"/unternehmensdetails",
|
# redirect_from=[
|
||||||
"/personendetails",
|
# "/unternehmensdetails",
|
||||||
"/unternehmensdetails/",
|
# "/personendetails",
|
||||||
"/personendetails/",
|
# "/unternehmensdetails/<value>",
|
||||||
],
|
# "/personendetails/<value>",
|
||||||
|
# ],
|
||||||
)
|
)
|
||||||
|
|
||||||
layout = html.Div(
|
metric = "None"
|
||||||
|
switch_edge_annotaion_value = False
|
||||||
|
egde_thickness = 1
|
||||||
|
network = None
|
||||||
|
|
||||||
|
|
||||||
|
def person_relation_type_filter() -> np.ndarray:
|
||||||
|
"""Returns an Numpy Array of String with Person telation types."""
|
||||||
|
return get_all_person_relations()["relation_type"].unique()
|
||||||
|
|
||||||
|
|
||||||
|
def company_relation_type_filter() -> np.ndarray:
|
||||||
|
"""Returns an Numpy Array of String with Company relation types."""
|
||||||
|
return get_all_company_relations()["relation_type"].unique()
|
||||||
|
|
||||||
|
|
||||||
|
def update_table(
|
||||||
|
metric_dropdown_value: str, metrics: pd.DataFrame
|
||||||
|
) -> tuple[dict, list]:
|
||||||
|
"""_summary_.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
metric_dropdown_value (str): _description_
|
||||||
|
metrics (pd.DataFrame): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple[dict, list]: _description_
|
||||||
|
"""
|
||||||
|
table_df = metrics.sort_values(metric_dropdown_value, ascending=False).head(10)
|
||||||
|
table_df = table_df[["designation", "category", metric_dropdown_value]]
|
||||||
|
columns = [{"name": i, "id": i} for i in table_df.columns]
|
||||||
|
return table_df.to_dict("records"), columns # type: ignore
|
||||||
|
|
||||||
|
|
||||||
|
def layout() -> list[html.Div]:
|
||||||
|
"""Generates the Layout of the Homepage."""
|
||||||
|
person_relation_types = person_relation_type_filter()
|
||||||
|
company_relation_types = company_relation_type_filter()
|
||||||
|
top_companies_dict, top_companies_columns, figure = update_figure(
|
||||||
|
"None",
|
||||||
|
False,
|
||||||
|
False,
|
||||||
|
company_relation_types[0],
|
||||||
|
person_relation_types[0],
|
||||||
|
"Spring",
|
||||||
|
1,
|
||||||
|
"degree",
|
||||||
|
)
|
||||||
|
return html.Div(
|
||||||
|
children=html.Div(
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="top_companytable_style",
|
||||||
|
children=[
|
||||||
|
html.H1(
|
||||||
|
title="Top Ten Nodes in Graph by Metric",
|
||||||
|
style={"align": "mid"},
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Filter Metric:"],
|
||||||
|
),
|
||||||
|
dcc.Dropdown(
|
||||||
[
|
[
|
||||||
html.H1("This is our Home page", style={"margin": 0}),
|
"eigenvector",
|
||||||
html.Div("This is our Home page content."),
|
"degree",
|
||||||
|
"betweenness",
|
||||||
|
"closeness",
|
||||||
|
],
|
||||||
|
"closeness",
|
||||||
|
id="dropdown_table_metric",
|
||||||
|
className="dropdown_style",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
dash_table.DataTable(
|
||||||
|
top_companies_dict,
|
||||||
|
top_companies_columns,
|
||||||
|
id="metric_table",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="networkx_style",
|
||||||
|
children=[
|
||||||
|
html.H1(className="header", children=["Social Graph"]),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper",
|
||||||
|
id="company_dropdown",
|
||||||
|
# style="visibility: hidden;",
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Company Relation Type Filter:"],
|
||||||
|
),
|
||||||
|
dcc.Dropdown(
|
||||||
|
company_relation_types,
|
||||||
|
company_relation_types[0],
|
||||||
|
id="dropdown_company_relation_filter",
|
||||||
|
className="dropdown_style",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
# style="visibility: visible;",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Person Relation Type Filter:"],
|
||||||
|
),
|
||||||
|
dcc.Dropdown(
|
||||||
|
person_relation_types,
|
||||||
|
person_relation_types[0],
|
||||||
|
id="dropdown_person_relation_filter",
|
||||||
|
className="dropdown_style",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Choose Graph Metric:"],
|
||||||
|
),
|
||||||
|
dcc.Dropdown(
|
||||||
|
[
|
||||||
|
"None",
|
||||||
|
"eigenvector",
|
||||||
|
"degree",
|
||||||
|
"betweenness",
|
||||||
|
"closeness",
|
||||||
|
],
|
||||||
|
"None",
|
||||||
|
id="dropdown",
|
||||||
|
className="dropdown_style",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Choose Layout:"],
|
||||||
|
),
|
||||||
|
dcc.Dropdown(
|
||||||
|
[
|
||||||
|
"Spring",
|
||||||
|
# "Bipartite",
|
||||||
|
"Circular",
|
||||||
|
"Kamada Kawai",
|
||||||
|
# "Planar",
|
||||||
|
"Random",
|
||||||
|
"Shell (only 2D)",
|
||||||
|
# "Spectral",
|
||||||
|
"Spiral (only 2D)",
|
||||||
|
# "Multipartite"
|
||||||
|
],
|
||||||
|
"Spring",
|
||||||
|
id="dropdown_layout",
|
||||||
|
className="dropdown_style",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Adjust Edge Thickness"],
|
||||||
|
),
|
||||||
|
dcc.Slider(
|
||||||
|
1,
|
||||||
|
4,
|
||||||
|
1,
|
||||||
|
value=1,
|
||||||
|
id="slider",
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper",
|
||||||
|
children=[
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Switch to 2D Diagramm"],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="switch-style",
|
||||||
|
children=[
|
||||||
|
daq.BooleanSwitch(id="switch", on=False)
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
# html.Div(
|
||||||
|
# className="filter-wrapper-item",
|
||||||
|
# children=[
|
||||||
|
# html.H5(
|
||||||
|
# className="filter-description",
|
||||||
|
# children=["Enable Node Annotation"],
|
||||||
|
# ),
|
||||||
|
# html.Div(
|
||||||
|
# className="switch-style",
|
||||||
|
# children=[daq.BooleanSwitch(id="switch_node_annotation", on=False)],
|
||||||
|
# ),
|
||||||
|
# ],
|
||||||
|
# ),
|
||||||
|
html.Div(
|
||||||
|
className="filter-wrapper-item",
|
||||||
|
children=[
|
||||||
|
html.H5(
|
||||||
|
className="filter-description",
|
||||||
|
children=["Enable Edge Annotation"],
|
||||||
|
),
|
||||||
|
html.Div(
|
||||||
|
className="switch-style",
|
||||||
|
children=[
|
||||||
|
daq.BooleanSwitch(
|
||||||
|
id="switch_edge_annotation",
|
||||||
|
on=False,
|
||||||
|
)
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
dcc.Graph(
|
||||||
|
figure=figure, id="my-graph", className="graph-style"
|
||||||
|
),
|
||||||
|
],
|
||||||
|
),
|
||||||
]
|
]
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@lru_cache(200)
|
||||||
|
def update_graph_data(
|
||||||
|
person_relation_type: str = "HAFTENDER_GESELLSCHAFTER",
|
||||||
|
company_relation_type: str = "GESCHAEFTSFUEHRER",
|
||||||
|
) -> tuple[nx.Graph, pd.DataFrame, dict, list]:
|
||||||
|
"""_summary_.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
person_relation_type (str, optional): _description_. Defaults to "HAFTENDER_GESELLSCHAFTER".
|
||||||
|
company_relation_type (str, optional): _description_. Defaults to "GESCHAEFTSFUEHRER".
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple[nx.Graph, pd.DataFrame, dict, list]: _description_
|
||||||
|
"""
|
||||||
|
# Get Data
|
||||||
|
person_df = get_all_person_relations()
|
||||||
|
company_df = get_all_company_relations()
|
||||||
|
|
||||||
|
person_relation = filter_relation_type(person_df, person_relation_type)
|
||||||
|
company_relation = filter_relation_type(company_df, company_relation_type)
|
||||||
|
|
||||||
|
# Create Edge and Node List from data
|
||||||
|
nodes_tmp, edges_tmp = create_edge_and_node_list(person_relation, company_relation)
|
||||||
|
|
||||||
|
graph, metrics = initialize_network(nodes=nodes_tmp, edges=edges_tmp)
|
||||||
|
return graph, metrics, nodes_tmp, edges_tmp
|
||||||
|
|
||||||
|
|
||||||
|
@callback(
|
||||||
|
[
|
||||||
|
Output("metric_table", "data"),
|
||||||
|
Output("metric_table", "columns"),
|
||||||
|
Output("my-graph", "figure"),
|
||||||
|
],
|
||||||
|
[
|
||||||
|
Input("dropdown", "value"),
|
||||||
|
Input("switch", "on"),
|
||||||
|
# Input("switch_node_annotation", "on"),
|
||||||
|
Input("switch_edge_annotation", "on"),
|
||||||
|
Input("dropdown_company_relation_filter", "value"),
|
||||||
|
Input("dropdown_person_relation_filter", "value"),
|
||||||
|
Input("dropdown_layout", "value"),
|
||||||
|
Input("slider", "value"),
|
||||||
|
Input("dropdown_table_metric", "value"),
|
||||||
|
],
|
||||||
|
prevent_initial_call=True,
|
||||||
|
allow_duplicate=True,
|
||||||
)
|
)
|
||||||
|
# @lru_cache(20)
|
||||||
|
@cached(cache=TTLCache(maxsize=100, ttl=500))
|
||||||
|
def update_figure( # noqa: PLR0913
|
||||||
|
selected_metric: str,
|
||||||
|
switch_value: bool,
|
||||||
|
# switch_node_annotaion_value: bool,
|
||||||
|
switch_edge_annotaion_value: bool,
|
||||||
|
c_relation_filter_value: str,
|
||||||
|
p_relation_filter_value: str,
|
||||||
|
layout: str,
|
||||||
|
slider_value: float,
|
||||||
|
metric_dropdown_value: str,
|
||||||
|
) -> go.Figure:
|
||||||
|
"""In this Callback the Value of the Dropdown is used to filter the Data. In Addition it takes the filter for the Graph metrics and creates a new graph, or switches between 3D and 2D.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_metric (_type_): Selected Value
|
||||||
|
switch_value (bool): True if 2D, False if 3D
|
||||||
|
switch_edge_annotaion_value: True if Edge should have a description, Flase = No Descritpion
|
||||||
|
c_relation_filter_value (_type_): Variable with String value of Relation Type for Companies
|
||||||
|
p_relation_filter_value (_type_): Variable with String value of Relation Type for Persons
|
||||||
|
layout: String of the Layout Dropdown
|
||||||
|
metric_dropdown_value: String of the Metric Dropdown
|
||||||
|
slider_value: Sets the size of the Edge Connections
|
||||||
|
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Network Graph(Plotly Figure): Plotly Figure in 3 or 2D
|
||||||
|
"""
|
||||||
|
_ = c_relation_filter_value, p_relation_filter_value
|
||||||
|
|
||||||
|
graph, metrics, nodes, edges = update_graph_data(
|
||||||
|
person_relation_type=p_relation_filter_value,
|
||||||
|
company_relation_type=c_relation_filter_value,
|
||||||
|
)
|
||||||
|
|
||||||
|
table_dict, table_columns = update_table(metric_dropdown_value, metrics)
|
||||||
|
|
||||||
|
if switch_value:
|
||||||
|
return (
|
||||||
|
table_dict,
|
||||||
|
table_columns,
|
||||||
|
create_2d_graph(
|
||||||
|
graph,
|
||||||
|
nodes,
|
||||||
|
edges,
|
||||||
|
metrics,
|
||||||
|
selected_metric,
|
||||||
|
layout,
|
||||||
|
switch_edge_annotaion_value,
|
||||||
|
slider_value, # type: ignore
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
return (
|
||||||
|
table_dict,
|
||||||
|
table_columns,
|
||||||
|
create_3d_graph(
|
||||||
|
graph,
|
||||||
|
nodes,
|
||||||
|
edges,
|
||||||
|
metrics,
|
||||||
|
selected_metric,
|
||||||
|
layout,
|
||||||
|
switch_edge_annotaion_value,
|
||||||
|
slider_value, # type: ignore
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|||||||
@ -0,0 +1 @@
|
|||||||
|
"""Everything regarding data extraction for NetworkX Graphs."""
|
||||||
184
src/aki_prj23_transparenzregister/utils/networkx/network_2d.py
Normal file
184
src/aki_prj23_transparenzregister/utils/networkx/network_2d.py
Normal file
@ -0,0 +1,184 @@
|
|||||||
|
"""This Module contains the Method to create a 2D Network Graph with NetworkX."""
|
||||||
|
import networkx as nx
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
|
||||||
|
|
||||||
|
def create_2d_graph( # noqa PLR0913
|
||||||
|
graph: nx.Graph,
|
||||||
|
nodes: dict,
|
||||||
|
edges: list,
|
||||||
|
metrics: pd.DataFrame,
|
||||||
|
metric: str | None,
|
||||||
|
layout: str,
|
||||||
|
edge_annotation: bool,
|
||||||
|
edge_thickness: int,
|
||||||
|
) -> go.Figure:
|
||||||
|
"""This Method creates a 2d Network in Plotly with a Scatter Graph and retuns it.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
graph (_type_): NetworkX Graph.
|
||||||
|
nodes (_type_): List of Nodes
|
||||||
|
edges (_type_): List of Edges
|
||||||
|
metrics (_type_): DataFrame with the MEtrics
|
||||||
|
metric (_type_): Selected Metric
|
||||||
|
edge_annotation: Enables the Description of Edges
|
||||||
|
edge_thickness: Int Value of the Edge thickness
|
||||||
|
layout: String which defines the Graph Layout
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
_type_: Plotly Figure
|
||||||
|
"""
|
||||||
|
# Set 2D Layout
|
||||||
|
pos = nx.spring_layout(graph)
|
||||||
|
match layout:
|
||||||
|
case "Spring":
|
||||||
|
pos = nx.spring_layout(graph)
|
||||||
|
# case "Bipartite":
|
||||||
|
# pos = nx.bipartite_layout(graph)
|
||||||
|
case "Circular":
|
||||||
|
pos = nx.circular_layout(graph)
|
||||||
|
case "Kamada Kawai":
|
||||||
|
pos = nx.kamada_kawai_layout(graph)
|
||||||
|
# case "Planar":
|
||||||
|
# pos = nx.planar_layout(graph)
|
||||||
|
case "Random":
|
||||||
|
pos = nx.random_layout(graph)
|
||||||
|
case "(Shell only 2D)":
|
||||||
|
pos = nx.shell_layout(graph)
|
||||||
|
# case "Spectral":
|
||||||
|
# pos = nx.spectral_layout(graph)
|
||||||
|
case "(Spiral only 2D)":
|
||||||
|
pos = nx.spiral_layout(graph)
|
||||||
|
# case "Multipartite":
|
||||||
|
# pos = nx.multipartite_layout(graph)
|
||||||
|
|
||||||
|
# Initialize Variables to set the Position of the Edges.
|
||||||
|
edge_x = []
|
||||||
|
edge_y = []
|
||||||
|
# Initialize Node Position Variables.
|
||||||
|
node_x = []
|
||||||
|
node_y = []
|
||||||
|
# Initialize Position Variables for the Description Text of the edges.
|
||||||
|
edge_weight_x = []
|
||||||
|
edge_weight_y = []
|
||||||
|
|
||||||
|
# Getting the Positions from NetworkX and assign them to the variables.
|
||||||
|
for edge in graph.edges():
|
||||||
|
x0, y0 = pos[edge[0]]
|
||||||
|
x1, y1 = pos[edge[1]]
|
||||||
|
edge_x.append(x0)
|
||||||
|
edge_x.append(x1)
|
||||||
|
# edge_x.append(None)
|
||||||
|
|
||||||
|
edge_y.append(y0)
|
||||||
|
edge_y.append(y1)
|
||||||
|
# edge_y.append(None)
|
||||||
|
|
||||||
|
edge_weight_x.append((x1 + x0) / 2)
|
||||||
|
edge_weight_y.append((y1 + y0) / 2)
|
||||||
|
# edge_weight_y.append(None)
|
||||||
|
# Add the Edges to the scatter plot according to their Positions.
|
||||||
|
edge_trace = go.Scatter(
|
||||||
|
x=edge_x,
|
||||||
|
y=edge_y,
|
||||||
|
line={"width": 0.5, "color": "#888"},
|
||||||
|
hoverinfo="none",
|
||||||
|
mode="lines",
|
||||||
|
)
|
||||||
|
# Add the Edgedescriptiontext to the scatter plot according to its Position.
|
||||||
|
edge_weights_trace = go.Scatter(
|
||||||
|
x=edge_weight_x,
|
||||||
|
y=edge_weight_y,
|
||||||
|
mode="text",
|
||||||
|
marker_size=0.5,
|
||||||
|
text=[0.45, 0.7, 0.34],
|
||||||
|
textposition="top center",
|
||||||
|
hovertemplate="Relation: %{text}<extra></extra>",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Getting the Positions from NetworkX and assign it to the variables.
|
||||||
|
for node in graph.nodes():
|
||||||
|
x, y = pos[node]
|
||||||
|
node_x.append(x)
|
||||||
|
node_y.append(y)
|
||||||
|
# Add the Nodes to the scatter plot according to their Positions.
|
||||||
|
node_trace = go.Scatter(
|
||||||
|
x=node_x,
|
||||||
|
y=node_y,
|
||||||
|
mode="markers",
|
||||||
|
hoverinfo="text",
|
||||||
|
marker={
|
||||||
|
"showscale": True,
|
||||||
|
"colorscale": "YlGnBu",
|
||||||
|
"reversescale": True,
|
||||||
|
"color": [],
|
||||||
|
"size": 10,
|
||||||
|
"colorbar": {
|
||||||
|
"thickness": 15,
|
||||||
|
"title": "Node Connections",
|
||||||
|
"xanchor": "left",
|
||||||
|
"titleside": "right",
|
||||||
|
},
|
||||||
|
"line_width": edge_thickness,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
# # Set Color by using the nodes DataFrame with its Color Attribute. The sequence matters!
|
||||||
|
colors = list(nx.get_node_attributes(graph, "color").values())
|
||||||
|
node_names = list(nx.get_node_attributes(graph, "name").values())
|
||||||
|
# ids = list(nx.get_node_attributes(graph, "id").values())
|
||||||
|
# print(ids)
|
||||||
|
|
||||||
|
# # Get the Node Text
|
||||||
|
# node_names = []
|
||||||
|
# for key, value in nodes.items():
|
||||||
|
# if "name" in value:
|
||||||
|
# node_names.append(value["name"])
|
||||||
|
# else:
|
||||||
|
# node_names.append(value["firstname"] + " " + value["lastname"])
|
||||||
|
# # Add Color and Names to the Scatter Plot.
|
||||||
|
# print(colors)
|
||||||
|
# print(node_names)
|
||||||
|
node_trace.marker.color = colors
|
||||||
|
node_trace.text = node_names
|
||||||
|
|
||||||
|
# Highlight the Node Size in regards to the selected Metric.
|
||||||
|
if metric != "None":
|
||||||
|
node_trace.marker.size = list(np.sqrt(metrics[metric]) * 200)
|
||||||
|
|
||||||
|
# Add Relation_Type as a Description for the edges.
|
||||||
|
if edge_annotation:
|
||||||
|
edge_type_list = []
|
||||||
|
for row in edges:
|
||||||
|
edge_type_list.append(row["type"])
|
||||||
|
|
||||||
|
edge_weights_trace.text = edge_type_list
|
||||||
|
|
||||||
|
# Return the Plotly Figure
|
||||||
|
return go.Figure(
|
||||||
|
data=[edge_trace, edge_weights_trace, node_trace],
|
||||||
|
layout=go.Layout(
|
||||||
|
title="<br>Network graph made with Python",
|
||||||
|
titlefont_size=16,
|
||||||
|
showlegend=False,
|
||||||
|
hovermode="closest",
|
||||||
|
margin={"b": 20, "l": 5, "r": 5, "t": 20},
|
||||||
|
annotations=[
|
||||||
|
{
|
||||||
|
"showarrow": False,
|
||||||
|
"text": f"Companies (Red) & Person (Blue) Relation \n node_count: {len(nodes)}, edge_count: {len(edges)}",
|
||||||
|
"xref": "paper",
|
||||||
|
"yref": "paper",
|
||||||
|
"x": 0,
|
||||||
|
"y": 0.1,
|
||||||
|
"xanchor": "left",
|
||||||
|
"yanchor": "bottom",
|
||||||
|
"font": {"size": 14},
|
||||||
|
}
|
||||||
|
],
|
||||||
|
xaxis={"showgrid": False, "zeroline": False, "showticklabels": False},
|
||||||
|
yaxis={"showgrid": False, "zeroline": False, "showticklabels": False},
|
||||||
|
),
|
||||||
|
)
|
||||||
210
src/aki_prj23_transparenzregister/utils/networkx/network_3d.py
Normal file
210
src/aki_prj23_transparenzregister/utils/networkx/network_3d.py
Normal file
@ -0,0 +1,210 @@
|
|||||||
|
"""This Module contains the Method to create a 3D Network Graph with NetworkX."""
|
||||||
|
import networkx as nx
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
|
||||||
|
|
||||||
|
def create_3d_graph( # noqa : PLR0913
|
||||||
|
graph: nx.Graph,
|
||||||
|
nodes: dict,
|
||||||
|
edges: list,
|
||||||
|
metrics: pd.DataFrame,
|
||||||
|
metric: str | None,
|
||||||
|
layout: str,
|
||||||
|
edge_annotation: bool,
|
||||||
|
edge_thickness: int,
|
||||||
|
) -> go.Figure:
|
||||||
|
"""This Method creates a 3D Network in Plotly with a Scatter Graph and retuns it.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
graph (_type_): NetworkX Graph.
|
||||||
|
nodes (_type_): List of Nodes
|
||||||
|
edges (_type_): List of Edges
|
||||||
|
metrics (_type_): DataFrame with the MEtrics
|
||||||
|
metric (_type_): Selected Metric
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
_type_: Plotly Figure
|
||||||
|
"""
|
||||||
|
# 3d spring layout
|
||||||
|
pos = nx.spring_layout(graph, dim=3)
|
||||||
|
match layout:
|
||||||
|
case "Spring":
|
||||||
|
pos = nx.spring_layout(graph, dim=3)
|
||||||
|
# case "Bipartite":
|
||||||
|
# pos = nx.bipartite_layout(graph, dim=3)
|
||||||
|
case "Circular":
|
||||||
|
pos = nx.circular_layout(graph, dim=3)
|
||||||
|
case "Kamada Kawai":
|
||||||
|
pos = nx.kamada_kawai_layout(graph, dim=3)
|
||||||
|
# case "Planar":
|
||||||
|
# pos = nx.planar_layout(graph, dim=3)
|
||||||
|
case "Random":
|
||||||
|
pos = nx.random_layout(graph, dim=3)
|
||||||
|
# case "Shell":
|
||||||
|
# pos = nx.shell_layout(graph, dim=3)
|
||||||
|
# case "Spectral":
|
||||||
|
# pos = nx.spectral_layout(graph, dim=3)
|
||||||
|
# case "Spiral":
|
||||||
|
# pos = nx.spiral_layout(graph, dim=3)
|
||||||
|
# case "Multipartite":
|
||||||
|
# pos = nx.multipartite_layout(graph, dim=3)
|
||||||
|
|
||||||
|
# Initialize Variables to set the Position of the Edges.
|
||||||
|
edge_x = []
|
||||||
|
edge_y = []
|
||||||
|
edge_z = []
|
||||||
|
|
||||||
|
# Initialize Node Position Variables.
|
||||||
|
node_x = []
|
||||||
|
node_y = []
|
||||||
|
node_z = []
|
||||||
|
|
||||||
|
# Initialize Position Variables for the Description Text of the edges.
|
||||||
|
edge_weight_x = []
|
||||||
|
edge_weight_y = []
|
||||||
|
edge_weight_z = []
|
||||||
|
|
||||||
|
# Getting the Positions from NetworkX and assign them to the variables.
|
||||||
|
for edge in graph.edges():
|
||||||
|
x0, y0, z0 = pos[edge[0]]
|
||||||
|
x1, y1, z1 = pos[edge[1]]
|
||||||
|
|
||||||
|
edge_x.append(x0)
|
||||||
|
edge_x.append(x1)
|
||||||
|
|
||||||
|
edge_y.append(y0)
|
||||||
|
edge_y.append(y1)
|
||||||
|
|
||||||
|
edge_z.append(z0)
|
||||||
|
edge_z.append(z1)
|
||||||
|
|
||||||
|
# Calculate edge mid
|
||||||
|
edge_weight_x.append((x0 + x1) / 2)
|
||||||
|
edge_weight_y.append((y0 + y1) / 2)
|
||||||
|
edge_weight_z.append((z0 + z1) / 2)
|
||||||
|
|
||||||
|
# Add the Edges to the scatter plot according to their Positions.
|
||||||
|
edge_trace = go.Scatter3d(
|
||||||
|
x=edge_x,
|
||||||
|
y=edge_y,
|
||||||
|
z=edge_z,
|
||||||
|
mode="lines",
|
||||||
|
line={"color": "rgb(125,125,125)", "width": 1},
|
||||||
|
hoverinfo="none",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Add the Edgedescriptiontext to the scatter plot according to its Position.
|
||||||
|
edge_weights_trace = go.Scatter3d(
|
||||||
|
x=edge_weight_x,
|
||||||
|
y=edge_weight_y,
|
||||||
|
z=edge_weight_z,
|
||||||
|
mode="text",
|
||||||
|
marker_size=1,
|
||||||
|
text=[0.45, 0.7, 0.34],
|
||||||
|
textposition="top center",
|
||||||
|
hovertemplate="weight: %{text}<extra></extra>",
|
||||||
|
)
|
||||||
|
|
||||||
|
# Getting the Positions from NetworkX and assign it to the variables.
|
||||||
|
for node in graph.nodes():
|
||||||
|
x, y, z = pos[node]
|
||||||
|
node_x.append(x)
|
||||||
|
node_y.append(y)
|
||||||
|
node_z.append(z)
|
||||||
|
|
||||||
|
# Add the Nodes to the scatter plot according to their Positions.
|
||||||
|
node_trace = go.Scatter3d(
|
||||||
|
x=node_x,
|
||||||
|
y=node_y,
|
||||||
|
z=node_z,
|
||||||
|
mode="markers",
|
||||||
|
name="actors",
|
||||||
|
marker={
|
||||||
|
"symbol": "circle",
|
||||||
|
"size": 6,
|
||||||
|
"color": "blue",
|
||||||
|
"colorscale": "Viridis",
|
||||||
|
"line": {"color": "rgb(50,50,50)", "width": 0.5},
|
||||||
|
},
|
||||||
|
# text=labels,
|
||||||
|
hoverinfo="text",
|
||||||
|
)
|
||||||
|
|
||||||
|
axis = {
|
||||||
|
"showbackground": False,
|
||||||
|
"showline": False,
|
||||||
|
"zeroline": False,
|
||||||
|
"showgrid": False,
|
||||||
|
"showticklabels": False,
|
||||||
|
"title": "",
|
||||||
|
}
|
||||||
|
|
||||||
|
layout = go.Layout(
|
||||||
|
title="Social Graph",
|
||||||
|
showlegend=False,
|
||||||
|
scene={
|
||||||
|
"xaxis": dict(axis),
|
||||||
|
"yaxis": dict(axis),
|
||||||
|
"zaxis": dict(axis),
|
||||||
|
},
|
||||||
|
margin={"t": 10},
|
||||||
|
hovermode="closest",
|
||||||
|
annotations=[
|
||||||
|
{
|
||||||
|
"showarrow": False,
|
||||||
|
"text": f"Companies (Red) & Person (Blue) Relation \n node_count: {len(nodes)}, edge_count: {len(edges)}",
|
||||||
|
"xref": "paper",
|
||||||
|
"yref": "paper",
|
||||||
|
"x": 0,
|
||||||
|
"y": 0.1,
|
||||||
|
"xanchor": "left",
|
||||||
|
"yanchor": "bottom",
|
||||||
|
"font": {"size": 14},
|
||||||
|
}
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
# Set Color by using the nodes DataFrame with its Color Attribute. The sequence matters!
|
||||||
|
colors = list(nx.get_node_attributes(graph, "color").values())
|
||||||
|
node_names = list(nx.get_node_attributes(graph, "name").values())
|
||||||
|
# node_names = []
|
||||||
|
# for key, value in nodes.items():
|
||||||
|
# if "name" in value:
|
||||||
|
# node_names.append(value["name"])
|
||||||
|
# else:
|
||||||
|
# node_names.append(value["firstname"] + " " + value["lastname"])
|
||||||
|
# Add Color and Names to the Scatter Plot.
|
||||||
|
# Add Color and Names to the Scatter Plot.
|
||||||
|
node_trace.marker.color = colors
|
||||||
|
node_trace.text = node_names
|
||||||
|
|
||||||
|
# Highlight the Node Size in regards to the selected Metric.
|
||||||
|
if metric != "None":
|
||||||
|
node_trace.marker.size = list(np.cbrt(metrics[metric]) * 200)
|
||||||
|
|
||||||
|
# Set the Color and width of Edges for better highlighting.
|
||||||
|
edge_colors = []
|
||||||
|
for row in edges:
|
||||||
|
if row["type"] == "HAFTENDER_GESELLSCHAFTER":
|
||||||
|
edge_colors.append("rgb(255,0,0)")
|
||||||
|
else:
|
||||||
|
edge_colors.append("rgb(255,105,180)")
|
||||||
|
edge_trace.line = {"color": edge_colors, "width": edge_thickness}
|
||||||
|
|
||||||
|
# Add Relation_Type as a Description for the edges.
|
||||||
|
if edge_annotation:
|
||||||
|
edge_type_list = []
|
||||||
|
for row in edges:
|
||||||
|
edge_type_list.append(row["type"])
|
||||||
|
|
||||||
|
edge_weights_trace.text = edge_type_list
|
||||||
|
|
||||||
|
# Set Color by using the nodes DataFrame with its Color Attribute. The sequence matters!
|
||||||
|
colors = list(nx.get_node_attributes(graph, "color").values())
|
||||||
|
node_trace.marker.color = colors
|
||||||
|
|
||||||
|
# Return the Plotly Figure
|
||||||
|
data = [edge_trace, edge_weights_trace, node_trace]
|
||||||
|
return go.Figure(data=data, layout=layout)
|
||||||
108
src/aki_prj23_transparenzregister/utils/networkx/network_base.py
Normal file
108
src/aki_prj23_transparenzregister/utils/networkx/network_base.py
Normal file
@ -0,0 +1,108 @@
|
|||||||
|
"""This Module has a Method to initialize a Network with NetworkX which can be used for 2D and 3D Graphs."""
|
||||||
|
import networkx as nx
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
|
||||||
|
def initialize_network(edges: list, nodes: dict) -> tuple[nx.Graph, pd.DataFrame]:
|
||||||
|
"""This Method creates a Network from the Framework NetworkX with the help of a Node and Edge List. Furthemore it creates a DataFrame with the most important Metrics.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
edges (list): List with the connections between Nodes.
|
||||||
|
nodes (dict): Dict with all Nodes.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Graph: Plotly Figure
|
||||||
|
Metrices: DataFrame with Metrics
|
||||||
|
"""
|
||||||
|
# create edge dataframe
|
||||||
|
df_edges = pd.DataFrame(edges, columns=["from", "to", "type"])
|
||||||
|
graph = nx.from_pandas_edgelist(
|
||||||
|
df_edges, source="from", target="to", edge_attr="type"
|
||||||
|
)
|
||||||
|
|
||||||
|
# update node attributes from dataframe
|
||||||
|
nx.set_node_attributes(graph, nodes)
|
||||||
|
# Create a DataFrame with all Metrics
|
||||||
|
# Create a DataFrame with all Metrics
|
||||||
|
metrics = pd.DataFrame(
|
||||||
|
columns=[
|
||||||
|
"eigenvector",
|
||||||
|
"degree",
|
||||||
|
"betweenness",
|
||||||
|
"closeness",
|
||||||
|
"pagerank",
|
||||||
|
"category",
|
||||||
|
"designation",
|
||||||
|
"id",
|
||||||
|
]
|
||||||
|
)
|
||||||
|
metrics["eigenvector"] = nx.eigenvector_centrality(graph).values()
|
||||||
|
metrics["degree"] = nx.degree_centrality(graph).values()
|
||||||
|
metrics["betweenness"] = nx.betweenness_centrality(graph).values()
|
||||||
|
metrics["closeness"] = nx.closeness_centrality(graph).values()
|
||||||
|
metrics["pagerank"] = nx.pagerank(graph).values()
|
||||||
|
metrics["category"] = nx.get_node_attributes(graph, "type").values()
|
||||||
|
metrics["designation"] = nx.get_node_attributes(graph, "name").values()
|
||||||
|
metrics["id"] = nx.get_node_attributes(graph, "id").values()
|
||||||
|
|
||||||
|
return graph, metrics
|
||||||
|
|
||||||
|
|
||||||
|
def initialize_network_with_reduced_metrics(
|
||||||
|
edges: list, nodes: dict
|
||||||
|
) -> tuple[nx.Graph, pd.DataFrame]:
|
||||||
|
"""This Method creates a Network from the Framework NetworkX with the help of a Node and Edge List. Furthemore it creates a DataFrame with the most important Metrics.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
edges (list): List with the connections between Nodes.
|
||||||
|
nodes (dict): Dict with all Nodes.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Graph: Plotly Figure
|
||||||
|
Metrices: DataFrame with Metrics
|
||||||
|
"""
|
||||||
|
# create edge dataframe
|
||||||
|
df_edges = pd.DataFrame(edges, columns=["from", "to", "type"])
|
||||||
|
graph = nx.from_pandas_edgelist(
|
||||||
|
df_edges, source="from", target="to", edge_attr="type"
|
||||||
|
)
|
||||||
|
|
||||||
|
# update node attributes from dataframe
|
||||||
|
nx.set_node_attributes(graph, nodes)
|
||||||
|
|
||||||
|
# Create a DataFrame with all Metrics
|
||||||
|
metrics = pd.DataFrame(
|
||||||
|
columns=["degree", "betweenness", "closeness", "category", "designation", "id"]
|
||||||
|
)
|
||||||
|
# metrics["eigenvector"] = nx.eigenvector_centrality(graph).values()
|
||||||
|
metrics["degree"] = nx.degree_centrality(graph).values()
|
||||||
|
metrics["betweenness"] = nx.betweenness_centrality(graph).values()
|
||||||
|
metrics["closeness"] = nx.closeness_centrality(graph).values()
|
||||||
|
# metrics["pagerank"] = nx.pagerank(graph).values()
|
||||||
|
metrics["category"] = nx.get_node_attributes(graph, "type").values()
|
||||||
|
metrics["designation"] = nx.get_node_attributes(graph, "name").values()
|
||||||
|
metrics["id"] = nx.get_node_attributes(graph, "id").values()
|
||||||
|
|
||||||
|
return graph, metrics
|
||||||
|
|
||||||
|
|
||||||
|
def initialize_network_without_metrics(edges: list, nodes: dict) -> nx.Graph:
|
||||||
|
"""This Method creates a Network from the Framework NetworkX with the help of a Node and Edge List. Furthemore it creates a DataFrame with the most important Metrics.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
edges (list): List with the connections between Nodes.
|
||||||
|
nodes (dict): Dict with all Nodes.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Graph: Plotly Figure
|
||||||
|
"""
|
||||||
|
# create edge dataframe
|
||||||
|
df_edges = pd.DataFrame(edges, columns=["from", "to", "type"])
|
||||||
|
graph = nx.from_pandas_edgelist(
|
||||||
|
df_edges, source="from", target="to", edge_attr="type"
|
||||||
|
)
|
||||||
|
|
||||||
|
# update node attributes from dataframe
|
||||||
|
nx.set_node_attributes(graph, nodes)
|
||||||
|
|
||||||
|
return graph
|
||||||
@ -0,0 +1,382 @@
|
|||||||
|
"""Module to receive and filter Data for working with NetworkX."""
|
||||||
|
from functools import lru_cache
|
||||||
|
|
||||||
|
import networkx as nx
|
||||||
|
import pandas as pd
|
||||||
|
from sqlalchemy.orm import aliased
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import (
|
||||||
|
initialize_network_with_reduced_metrics,
|
||||||
|
initialize_network_without_metrics,
|
||||||
|
)
|
||||||
|
from aki_prj23_transparenzregister.utils.sql import entities
|
||||||
|
|
||||||
|
# Gets the Session Key for the DB Connection.
|
||||||
|
|
||||||
|
# Alias for Company table for the base company
|
||||||
|
to_company = aliased(entities.Company, name="to_company")
|
||||||
|
|
||||||
|
# Alias for Company table for the head company
|
||||||
|
from_company = aliased(entities.Company, name="from_company")
|
||||||
|
|
||||||
|
COLOR_COMPANY = "blue"
|
||||||
|
COLOR_PERSON = "red"
|
||||||
|
|
||||||
|
|
||||||
|
def find_all_company_relations() -> pd.DataFrame:
|
||||||
|
"""_summary_.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: _description_
|
||||||
|
"""
|
||||||
|
session = SessionHandler.session
|
||||||
|
assert session # noqa: S101
|
||||||
|
query_companies = session.query(entities.Company) # .all()
|
||||||
|
query_relations = session.query(entities.CompanyRelation) # .all()
|
||||||
|
|
||||||
|
companies_df: pd.DataFrame = pd.read_sql(str(query_companies), session.bind) # type: ignore
|
||||||
|
companies_relations_df: pd.DataFrame = pd.read_sql(str(query_relations), session.bind) # type: ignore
|
||||||
|
companies_relations_df = companies_relations_df[
|
||||||
|
["relation_id", "company_relation_company2_id"]
|
||||||
|
]
|
||||||
|
company_name = []
|
||||||
|
connected_company_name = []
|
||||||
|
|
||||||
|
companies_relations_df = companies_relations_df.head()
|
||||||
|
|
||||||
|
for _, row in companies_relations_df.iterrows():
|
||||||
|
company_name.append(
|
||||||
|
companies_df.loc[companies_df["company_id"] == row["relation_id"]][
|
||||||
|
"company_name"
|
||||||
|
].iloc[0]
|
||||||
|
)
|
||||||
|
connected_company_name.append(
|
||||||
|
companies_df.loc[
|
||||||
|
companies_df["company_id"] == row["company_relation_company2_id"]
|
||||||
|
]["company_name"].iloc[0]
|
||||||
|
)
|
||||||
|
|
||||||
|
companies_relations_df["company_name"] = company_name
|
||||||
|
companies_relations_df["connected_company_name"] = connected_company_name
|
||||||
|
|
||||||
|
return companies_relations_df
|
||||||
|
|
||||||
|
|
||||||
|
def get_all_company_relations() -> pd.DataFrame:
|
||||||
|
"""This Methods makes a Database Request for all Companies and their relations, modifies the ID Column and returns the Result as an DataFrame.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
DataFrame: DataFrame with all Relations between Companies.
|
||||||
|
"""
|
||||||
|
session = SessionHandler.session
|
||||||
|
assert session # noqa: S101
|
||||||
|
# Query to fetch relations between companies
|
||||||
|
relations_company_query = (
|
||||||
|
session.query(
|
||||||
|
to_company.id.label("id_company_to"),
|
||||||
|
to_company.name.label("name_company_to"),
|
||||||
|
entities.CompanyRelation.relation.label("relation_type"),
|
||||||
|
from_company.name.label("name_company_from"),
|
||||||
|
from_company.id.label("id_company_from"),
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
entities.CompanyRelation,
|
||||||
|
entities.CompanyRelation.company_id == to_company.id,
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
from_company,
|
||||||
|
entities.CompanyRelation.company2_id == from_company.id,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
str(relations_company_query)
|
||||||
|
company_relations = pd.read_sql_query(str(relations_company_query), session.bind) # type: ignore
|
||||||
|
|
||||||
|
company_relations["id_company_from"] = company_relations["id_company_from"].apply(
|
||||||
|
lambda x: f"c_{x}"
|
||||||
|
)
|
||||||
|
company_relations["id_company_to"] = company_relations["id_company_to"].apply(
|
||||||
|
lambda x: f"c_{x}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return company_relations
|
||||||
|
|
||||||
|
|
||||||
|
def get_all_person_relations() -> pd.DataFrame:
|
||||||
|
"""This Methods makes a Database Request for all Persons and their relations, modifies the ID Column and returns the Result as an DataFrame.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
DataFrame: DataFrame with all Relations between Persons and Companies.
|
||||||
|
"""
|
||||||
|
session = SessionHandler.session
|
||||||
|
assert session # noqa: S101
|
||||||
|
relations_person_query = (
|
||||||
|
session.query(
|
||||||
|
entities.Company.id.label("id_company"),
|
||||||
|
entities.Company.name.label("name_company"),
|
||||||
|
entities.PersonRelation.relation.label("relation_type"),
|
||||||
|
entities.Person.id.label("id_person"),
|
||||||
|
entities.Person.lastname.label("lastname"),
|
||||||
|
entities.Person.firstname.label("firstname"),
|
||||||
|
entities.Person.date_of_birth.label("date_of_birth"),
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
entities.PersonRelation,
|
||||||
|
entities.PersonRelation.company_id == entities.Company.id,
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
entities.Person,
|
||||||
|
entities.PersonRelation.person_id == entities.Person.id,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
person_relations = pd.read_sql_query(str(relations_person_query), session.bind) # type: ignore
|
||||||
|
|
||||||
|
person_relations["id_company"] = person_relations["id_company"].apply(
|
||||||
|
lambda x: f"c_{x}"
|
||||||
|
)
|
||||||
|
person_relations["id_person"] = person_relations["id_person"].apply(
|
||||||
|
lambda x: f"p_{x}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return person_relations
|
||||||
|
|
||||||
|
|
||||||
|
def filter_relation_type(
|
||||||
|
relation_dataframe: pd.DataFrame, selected_relation_type: str
|
||||||
|
) -> pd.DataFrame:
|
||||||
|
"""This Method filters the given DataFrame based on the selected Relation Type and returns it.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
relation_dataframe (pd.DataFrame): DataFrame must contain a column named "relation_type".
|
||||||
|
selected_relation_type (str): String with the filter value.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
relation_dataframe (pd.DataFrame): The filtered DataFrame which now only contains entries with the selected Relation Type.
|
||||||
|
"""
|
||||||
|
return relation_dataframe.loc[
|
||||||
|
relation_dataframe["relation_type"] == selected_relation_type
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def create_edge_and_node_list(
|
||||||
|
person_relations: pd.DataFrame, company_relations: pd.DataFrame
|
||||||
|
) -> tuple[dict, list]:
|
||||||
|
"""In this Method the given DataFrames with the relation will be morphed to Edge and Node lists and enhanced by a coloring for companies and person Nodes.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
person_relations (pd.DataFrame): _description_
|
||||||
|
company_relations (pd.DataFrame): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
_type_: _description_
|
||||||
|
"""
|
||||||
|
nodes: dict = {}
|
||||||
|
edges: list = []
|
||||||
|
|
||||||
|
# Iterate over person relations
|
||||||
|
for _, row in person_relations.iterrows():
|
||||||
|
if nodes.get(row["id_company"]) is None:
|
||||||
|
nodes[row["id_company"]] = {
|
||||||
|
"id": row["id_company"],
|
||||||
|
"name": row["name_company"],
|
||||||
|
"color": COLOR_COMPANY,
|
||||||
|
"type": "company",
|
||||||
|
}
|
||||||
|
if nodes.get(row["id_person"]) is None:
|
||||||
|
nodes[row["id_person"]] = {
|
||||||
|
"id": row["id_person"],
|
||||||
|
"name": str(row["firstname"]) + " " + str(row["lastname"]),
|
||||||
|
"date_of_birth": row["date_of_birth"],
|
||||||
|
"color": COLOR_PERSON,
|
||||||
|
"type": "person",
|
||||||
|
}
|
||||||
|
edges.append(
|
||||||
|
{
|
||||||
|
"from": row["id_person"],
|
||||||
|
"to": row["id_company"],
|
||||||
|
"type": row["relation_type"],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
for _, row in company_relations.iterrows():
|
||||||
|
if nodes.get(row["id_company_from"]) is None: # noqa
|
||||||
|
nodes[row["id_company_from"]] = {
|
||||||
|
"id": row["id_company_from"],
|
||||||
|
"name": row["name_company_from"],
|
||||||
|
"color": COLOR_COMPANY,
|
||||||
|
"type": "company",
|
||||||
|
}
|
||||||
|
if nodes.get(row["id_company_to"]) is None:
|
||||||
|
nodes[row["id_company_to"]] = {
|
||||||
|
"id": row["id_company_to"],
|
||||||
|
"name": row["name_company_to"],
|
||||||
|
"color": COLOR_COMPANY,
|
||||||
|
"type": "company",
|
||||||
|
}
|
||||||
|
edges.append(
|
||||||
|
{
|
||||||
|
"from": row["id_company_from"],
|
||||||
|
"to": row["id_company_to"],
|
||||||
|
"type": row["relation_type"],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
return nodes, edges
|
||||||
|
|
||||||
|
|
||||||
|
def find_company_relations(
|
||||||
|
selected_company_id: int,
|
||||||
|
) -> tuple[pd.DataFrame, pd.DataFrame]:
|
||||||
|
"""Finds all Relations for the given Company id.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
selected_company_id: Id of the Company which Relations should be returned.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Two Dataframes
|
||||||
|
"""
|
||||||
|
session = SessionHandler.session
|
||||||
|
assert session # noqa: S101
|
||||||
|
relations_company = (
|
||||||
|
session.query(
|
||||||
|
to_company.id.label("id_company_to"),
|
||||||
|
to_company.name.label("name_company_to"),
|
||||||
|
entities.CompanyRelation.relation.label("relation_type"),
|
||||||
|
from_company.name.label("name_company_from"),
|
||||||
|
from_company.id.label("id_company_from"),
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
entities.CompanyRelation,
|
||||||
|
entities.CompanyRelation.company_id == to_company.id,
|
||||||
|
)
|
||||||
|
.join(
|
||||||
|
from_company,
|
||||||
|
entities.CompanyRelation.company2_id == from_company.id,
|
||||||
|
)
|
||||||
|
.filter(
|
||||||
|
(from_company.id == selected_company_id)
|
||||||
|
| (to_company.id == selected_company_id)
|
||||||
|
)
|
||||||
|
.all()
|
||||||
|
)
|
||||||
|
|
||||||
|
company_relations = pd.DataFrame(
|
||||||
|
relations_company,
|
||||||
|
columns=[
|
||||||
|
"id_company_to",
|
||||||
|
"name_company_to",
|
||||||
|
"relation_type",
|
||||||
|
"name_company_from",
|
||||||
|
"id_company_from",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
||||||
|
company_relations["id_company_from"] = company_relations["id_company_from"].apply(
|
||||||
|
lambda x: f"c_{x}"
|
||||||
|
)
|
||||||
|
company_relations["id_company_to"] = company_relations["id_company_to"].apply(
|
||||||
|
lambda x: f"c_{x}"
|
||||||
|
)
|
||||||
|
|
||||||
|
return pd.DataFrame(), company_relations # company_relations
|
||||||
|
|
||||||
|
|
||||||
|
def create_edge_and_node_list_for_company(
|
||||||
|
company_relations: pd.DataFrame,
|
||||||
|
) -> tuple[dict, list]:
|
||||||
|
"""In this Method the given DataFrames with the relation will be morphed to Edge and Node lists and enhanced by a coloring for companies and person Nodes.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
person_relations (pd.DataFrame): _description_
|
||||||
|
company_relations (pd.DataFrame): _description_
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
_type_: _description_
|
||||||
|
"""
|
||||||
|
nodes: dict = {}
|
||||||
|
edges: list = []
|
||||||
|
|
||||||
|
for _, row in company_relations.iterrows():
|
||||||
|
if nodes.get(row["id_company_from"]) is None:
|
||||||
|
nodes[row["id_company_from"]] = {
|
||||||
|
"id": row["id_company_from"],
|
||||||
|
"name": row["name_company_from"],
|
||||||
|
"color": COLOR_COMPANY,
|
||||||
|
"type": "company",
|
||||||
|
}
|
||||||
|
if nodes.get(row["id_company_to"]) is None:
|
||||||
|
nodes[row["id_company_to"]] = {
|
||||||
|
"id": row["id_company_to"],
|
||||||
|
"name": row["name_company_to"],
|
||||||
|
"color": COLOR_COMPANY,
|
||||||
|
"type": "company",
|
||||||
|
}
|
||||||
|
edges.append(
|
||||||
|
{
|
||||||
|
"from": row["id_company_from"],
|
||||||
|
"to": row["id_company_to"],
|
||||||
|
"type": row["relation_type"],
|
||||||
|
}
|
||||||
|
)
|
||||||
|
return nodes, edges
|
||||||
|
|
||||||
|
|
||||||
|
def get_all_metrics_from_id(company_id: int) -> pd.Series:
|
||||||
|
"""Returns all Metric for the given ID.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
company_id (int): Id of the Company.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: _description_
|
||||||
|
"""
|
||||||
|
# Get Data
|
||||||
|
person_df = get_all_person_relations()
|
||||||
|
company_df = get_all_company_relations()
|
||||||
|
|
||||||
|
# Create Edge and Node List from data
|
||||||
|
nodes_tmp, edges_tmp = create_edge_and_node_list(person_df, company_df)
|
||||||
|
graph, metrics = initialize_network_with_reduced_metrics(
|
||||||
|
nodes=nodes_tmp, edges=edges_tmp
|
||||||
|
)
|
||||||
|
filtered_metrics = metrics.loc[metrics["id"] == company_id]
|
||||||
|
if len(filtered_metrics) == 0:
|
||||||
|
return pd.Series([])
|
||||||
|
return filtered_metrics.iloc[0]
|
||||||
|
|
||||||
|
|
||||||
|
@lru_cache
|
||||||
|
def get_relations_number_from_id(id: int) -> tuple[int, int, int]:
|
||||||
|
"""Returns all Relation in 1, 2 and 3 lvl of one Node.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
id (int): String of the Company or Person Id.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple[int,int,int]: _description_
|
||||||
|
"""
|
||||||
|
# Get Data
|
||||||
|
person_df = get_all_person_relations()
|
||||||
|
company_df = get_all_company_relations()
|
||||||
|
|
||||||
|
# Create Edge and Node List from data
|
||||||
|
nodes_tmp, edges_tmp = create_edge_and_node_list(person_df, company_df)
|
||||||
|
graph = initialize_network_without_metrics(nodes=nodes_tmp, edges=edges_tmp)
|
||||||
|
|
||||||
|
neighbors = nx.all_neighbors(graph, id)
|
||||||
|
|
||||||
|
relations_lv1 = set(neighbors)
|
||||||
|
relations_lv2 = set()
|
||||||
|
relations_lv3 = set()
|
||||||
|
|
||||||
|
for node in relations_lv1:
|
||||||
|
relations_lv2 |= set(nx.all_neighbors(graph, node))
|
||||||
|
|
||||||
|
relations_lv2.discard(id)
|
||||||
|
|
||||||
|
for sub_node in relations_lv2:
|
||||||
|
relations_lv3 |= set(nx.all_neighbors(graph, sub_node))
|
||||||
|
|
||||||
|
relations_lv2.difference(relations_lv3)
|
||||||
|
|
||||||
|
return (len(relations_lv1), len(relations_lv2), len(relations_lv3))
|
||||||
@ -1,8 +1,20 @@
|
|||||||
"""Tests for company elements."""
|
"""Tests for company elements."""
|
||||||
|
|
||||||
|
from collections.abc import Generator
|
||||||
|
|
||||||
|
import pytest
|
||||||
from sqlalchemy.orm import Session
|
from sqlalchemy.orm import Session
|
||||||
|
|
||||||
from aki_prj23_transparenzregister.ui import company_elements, data_elements
|
from aki_prj23_transparenzregister.ui import company_elements, data_elements
|
||||||
|
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(autouse=True)
|
||||||
|
def _set_session(full_db: Session) -> Generator[None, None, None]:
|
||||||
|
"""Sets a session for the dash application to be used."""
|
||||||
|
SessionHandler.session = full_db
|
||||||
|
yield
|
||||||
|
SessionHandler.session = None
|
||||||
|
|
||||||
|
|
||||||
def test_import() -> None:
|
def test_import() -> None:
|
||||||
|
|||||||
195
tests/ui/home_page_test.py
Normal file
195
tests/ui/home_page_test.py
Normal file
@ -0,0 +1,195 @@
|
|||||||
|
"""Test for the Home Page."""
|
||||||
|
import datetime
|
||||||
|
from collections.abc import Generator
|
||||||
|
from unittest.mock import patch
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pytest
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.ui.pages import home
|
||||||
|
|
||||||
|
|
||||||
|
def test_import() -> None:
|
||||||
|
"""Checks if an import co company_stats_dash can be made."""
|
||||||
|
assert home is not None
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.tim()
|
||||||
|
def test_person_relation_type_filter() -> None:
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.ui.pages.home.get_all_person_relations"
|
||||||
|
) as mock_filter:
|
||||||
|
data = [
|
||||||
|
{"relation_type": "Eigentümer"},
|
||||||
|
{"relation_type": "Inhaber"},
|
||||||
|
{"relation_type": "Eigentümer"},
|
||||||
|
]
|
||||||
|
mock_filter.return_value = pd.DataFrame(data)
|
||||||
|
assert list(home.person_relation_type_filter()) == ["Eigentümer", "Inhaber"]
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.tim()
|
||||||
|
def test_company_relation_type_filter() -> None:
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.ui.pages.home.get_all_company_relations"
|
||||||
|
) as mock_filter:
|
||||||
|
data = [
|
||||||
|
{"relation_type": "Eigentümer"},
|
||||||
|
{"relation_type": "Inhaber"},
|
||||||
|
{"relation_type": "Eigentümer"},
|
||||||
|
]
|
||||||
|
mock_filter.return_value = pd.DataFrame(data)
|
||||||
|
assert list(home.company_relation_type_filter()) == ["Eigentümer", "Inhaber"]
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.tim()
|
||||||
|
def test_update_table() -> None:
|
||||||
|
metrics = pd.DataFrame(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"designation": "Mustermann, Max",
|
||||||
|
"category": "Person",
|
||||||
|
"centrality": 3.14,
|
||||||
|
"betweenness": 42,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"designation": "Musterfrau, Martina",
|
||||||
|
"category": "Person",
|
||||||
|
"centrality": 42,
|
||||||
|
"betweenness": 3.14,
|
||||||
|
},
|
||||||
|
]
|
||||||
|
)
|
||||||
|
selected_metric = "centrality"
|
||||||
|
|
||||||
|
expected_result_df = [
|
||||||
|
{
|
||||||
|
"designation": "Musterfrau, Martina",
|
||||||
|
"category": "Person",
|
||||||
|
"centrality": 42.0,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"designation": "Mustermann, Max",
|
||||||
|
"category": "Person",
|
||||||
|
"centrality": 3.14,
|
||||||
|
},
|
||||||
|
]
|
||||||
|
expected_result_columns = [
|
||||||
|
{"name": "designation", "id": "designation"},
|
||||||
|
{"name": "category", "id": "category"},
|
||||||
|
{"name": "centrality", "id": "centrality"},
|
||||||
|
]
|
||||||
|
|
||||||
|
result_df, result_columns = home.update_table(selected_metric, metrics)
|
||||||
|
assert result_df == expected_result_df
|
||||||
|
assert result_columns == expected_result_columns
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
|
def _get_person_relations() -> Generator:
|
||||||
|
data = [
|
||||||
|
{
|
||||||
|
"id_company": 1,
|
||||||
|
"name_company": "0 10 24 Telefondienste GmbH",
|
||||||
|
"relation_type": "GESCHAEFTSFUEHRER",
|
||||||
|
"id_person": 1,
|
||||||
|
"lastname": "Tetau",
|
||||||
|
"firstname": "Nicolas",
|
||||||
|
"date_of_birth": datetime.date(1971, 1, 2),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 1,
|
||||||
|
"name_company": "0 10 24 Telefondienste GmbH",
|
||||||
|
"relation_type": "PROKURIST",
|
||||||
|
"id_person": 2,
|
||||||
|
"lastname": "Dammast",
|
||||||
|
"firstname": "Lutz",
|
||||||
|
"date_of_birth": datetime.date(1966, 12, 6),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"id_person": 3,
|
||||||
|
"lastname": "Tutsch",
|
||||||
|
"firstname": "Rosemarie",
|
||||||
|
"date_of_birth": datetime.date(1941, 10, 9),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "HAFTENDER_GESELLSCHAFTER",
|
||||||
|
"id_person": 4,
|
||||||
|
"lastname": "Staiger",
|
||||||
|
"firstname": "Marc",
|
||||||
|
"date_of_birth": datetime.date(1969, 10, 22),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "HAFTENDER_GESELLSCHAFTER",
|
||||||
|
"id_person": 5,
|
||||||
|
"lastname": "Staiger",
|
||||||
|
"firstname": "Michaela",
|
||||||
|
"date_of_birth": datetime.date(1971, 3, 3),
|
||||||
|
},
|
||||||
|
]
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.ui.pages.home.get_all_person_relations"
|
||||||
|
) as mock_get_person_relations:
|
||||||
|
mock_get_person_relations.return_value = pd.DataFrame(data)
|
||||||
|
yield
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
|
def _get_company_relations() -> Generator:
|
||||||
|
data = [
|
||||||
|
{
|
||||||
|
"id_company_to": 2,
|
||||||
|
"name_company_to": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "GESCHAEFTSFUEHRER",
|
||||||
|
"name_company_from": "Staiger I. Verwaltung-GmbH",
|
||||||
|
"id_company_from": 3226,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 3,
|
||||||
|
"name_company_to": "1 A Autenrieth Kunststofftechnik GmbH & Co. KG",
|
||||||
|
"relation_type": "GESCHAEFTSFUEHRER",
|
||||||
|
"name_company_from": "Autenrieth Verwaltungs-GmbH",
|
||||||
|
"id_company_from": 3324,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 5,
|
||||||
|
"name_company_to": "2. Schaper Objekt GmbH & Co. Kiel KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"name_company_from": "Multi-Center Warenvertriebs GmbH",
|
||||||
|
"id_company_from": 2213,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 6,
|
||||||
|
"name_company_to": "AASP Filmproduktionsgesellschaft mbH & Co. Leonie KG",
|
||||||
|
"relation_type": "INHABER",
|
||||||
|
"name_company_from": "ABN AMRO Structured Products Gesellschaft für Fondsbeteiligungen mbH",
|
||||||
|
"id_company_from": 3332,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 6,
|
||||||
|
"name_company_to": "AASP Filmproduktionsgesellschaft mbH & Co. Leonie KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"name_company_from": "Kallang GmbH",
|
||||||
|
"id_company_from": 3316,
|
||||||
|
},
|
||||||
|
]
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.ui.pages.home.get_all_company_relations"
|
||||||
|
) as mock_get_person_relations:
|
||||||
|
mock_get_person_relations.return_value = pd.DataFrame(data)
|
||||||
|
yield
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.tim()
|
||||||
|
def test_update_graph_data() -> None:
|
||||||
|
graph_result, metrics_result, nodes_result, edges_result = home.update_graph_data(
|
||||||
|
"HAFTENDER_GESELLSCHAFTER", "GESCHAEFTSFUEHRER"
|
||||||
|
)
|
||||||
|
assert graph_result is not None
|
||||||
103
tests/utils/networkx/network_2d_test.py
Normal file
103
tests/utils/networkx/network_2d_test.py
Normal file
@ -0,0 +1,103 @@
|
|||||||
|
"""Test the initialize Network function."""
|
||||||
|
import datetime
|
||||||
|
from collections.abc import Generator
|
||||||
|
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
import pytest
|
||||||
|
from sqlalchemy.orm import Session
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx import network_2d
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_2d import create_2d_graph
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
|
||||||
|
|
||||||
|
|
||||||
|
def test_import() -> None:
|
||||||
|
"""Checks if an import co company_stats_dash can be made."""
|
||||||
|
assert network_2d is not None
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(autouse=True)
|
||||||
|
def _set_session(full_db: Session) -> Generator[None, None, None]:
|
||||||
|
"""Sets a session for the dash application to be used."""
|
||||||
|
SessionHandler.session = full_db
|
||||||
|
yield
|
||||||
|
SessionHandler.session = None
|
||||||
|
|
||||||
|
|
||||||
|
def test_create_2d_graph() -> None:
|
||||||
|
"""Tests the creation of a 2D Graph."""
|
||||||
|
edges: list = [
|
||||||
|
{"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "p_545", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_1", "to": "c_2", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "c_1", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
]
|
||||||
|
nodes: dict = {
|
||||||
|
"c_53": {
|
||||||
|
"id": "c_53",
|
||||||
|
"name": "1. Freiburger Solarfonds Beteiligungs-KG",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"p_545": {
|
||||||
|
"id": "p_545",
|
||||||
|
"name": "Wetzel, Jürgen",
|
||||||
|
"type": "person",
|
||||||
|
"date_of_birth": datetime.date(1962, 11, 15),
|
||||||
|
"color": "red",
|
||||||
|
},
|
||||||
|
"c_1": {
|
||||||
|
"id": "c_1",
|
||||||
|
"name": "Musterfirma",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"c_2": {
|
||||||
|
"id": "c_2",
|
||||||
|
"name": "Firma 1",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
graph, metrics = initialize_network(edges=edges, nodes=nodes)
|
||||||
|
metric = "None"
|
||||||
|
layout = "Spring"
|
||||||
|
edge_annotation = False
|
||||||
|
edge_thickness = 1
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
metric = "degree"
|
||||||
|
layout = "Circular"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
edge_annotation = True
|
||||||
|
layout = "Kamada Kawai"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
layout = "Random"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
layout = "Shell (only 2D)"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
layout = "Spiral (only 2D)"
|
||||||
|
figure = create_2d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
79
tests/utils/networkx/network_3d_test.py
Normal file
79
tests/utils/networkx/network_3d_test.py
Normal file
@ -0,0 +1,79 @@
|
|||||||
|
"""Test the initialize Network function."""
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
import plotly.graph_objects as go
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx import network_3d
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_3d import create_3d_graph
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
|
||||||
|
|
||||||
|
|
||||||
|
def test_import() -> None:
|
||||||
|
"""Checks if an import co company_stats_dash can be made."""
|
||||||
|
assert network_3d is not None
|
||||||
|
|
||||||
|
|
||||||
|
def test_create_3d_graph() -> None:
|
||||||
|
"""Tests the creation of a 3D Graph."""
|
||||||
|
edges: list = [
|
||||||
|
{"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "p_545", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_1", "to": "c_2", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "c_1", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
]
|
||||||
|
nodes: dict = {
|
||||||
|
"c_53": {
|
||||||
|
"id": "c_53",
|
||||||
|
"name": "1. Freiburger Solarfonds Beteiligungs-KG",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"p_545": {
|
||||||
|
"id": "p_545",
|
||||||
|
"name": "Wetzel, Jürgen",
|
||||||
|
"type": "person",
|
||||||
|
"date_of_birth": datetime.date(1962, 11, 15),
|
||||||
|
"color": "red",
|
||||||
|
},
|
||||||
|
"c_1": {
|
||||||
|
"id": "c_1",
|
||||||
|
"name": "Musterfirma",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"c_2": {
|
||||||
|
"id": "c_2",
|
||||||
|
"name": "Firma 1",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
graph, metrics = initialize_network(edges=edges, nodes=nodes)
|
||||||
|
metric = "None"
|
||||||
|
layout = "Spring"
|
||||||
|
edge_annotation = False
|
||||||
|
edge_thickness = 1
|
||||||
|
figure = create_3d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
metric = "degree"
|
||||||
|
layout = "Circular"
|
||||||
|
figure = create_3d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
edge_annotation = True
|
||||||
|
layout = "Kamada Kawai"
|
||||||
|
figure = create_3d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
|
|
||||||
|
layout = "Random"
|
||||||
|
figure = create_3d_graph(
|
||||||
|
graph, nodes, edges, metrics, metric, layout, edge_annotation, edge_thickness
|
||||||
|
)
|
||||||
|
assert type(figure) is go.Figure
|
||||||
85
tests/utils/networkx/network_base_test.py
Normal file
85
tests/utils/networkx/network_base_test.py
Normal file
@ -0,0 +1,85 @@
|
|||||||
|
"""Test the initialize Network function."""
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
import networkx as nx
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx import network_base
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx.network_base import (
|
||||||
|
initialize_network,
|
||||||
|
initialize_network_with_reduced_metrics,
|
||||||
|
initialize_network_without_metrics,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def test_import() -> None:
|
||||||
|
"""Checks if an import co company_stats_dash can be made."""
|
||||||
|
assert network_base is not None
|
||||||
|
|
||||||
|
|
||||||
|
def test_initialize_network() -> None:
|
||||||
|
edges: list = [
|
||||||
|
{"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "p_545", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_1", "to": "c_2", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
{"from": "c_53", "to": "c_1", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
]
|
||||||
|
nodes: dict = {
|
||||||
|
"c_53": {
|
||||||
|
"id": "c_53",
|
||||||
|
"name": "1. Freiburger Solarfonds Beteiligungs-KG",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"p_545": {
|
||||||
|
"id": "p_545",
|
||||||
|
"name": "Wetzel, Jürgen",
|
||||||
|
"type": "person",
|
||||||
|
"date_of_birth": datetime.date(1962, 11, 15),
|
||||||
|
"color": "red",
|
||||||
|
},
|
||||||
|
"c_1": {
|
||||||
|
"id": "c_1",
|
||||||
|
"name": "Musterfirma",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blue",
|
||||||
|
},
|
||||||
|
"c_2": {
|
||||||
|
"id": "c_2",
|
||||||
|
"name": "Firma 1",
|
||||||
|
"type": "company",
|
||||||
|
"color": "blur",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
# print(len(edges))
|
||||||
|
# print(len(nodes))
|
||||||
|
graph, metrics = initialize_network(edges=edges, nodes=nodes)
|
||||||
|
assert isinstance(graph, nx.Graph)
|
||||||
|
assert isinstance(metrics, pd.DataFrame)
|
||||||
|
assert list(metrics.columns) == [
|
||||||
|
"eigenvector",
|
||||||
|
"degree",
|
||||||
|
"betweenness",
|
||||||
|
"closeness",
|
||||||
|
"pagerank",
|
||||||
|
"category",
|
||||||
|
"designation",
|
||||||
|
"id",
|
||||||
|
]
|
||||||
|
|
||||||
|
graph_reduced, metrics_reduced = initialize_network_with_reduced_metrics(
|
||||||
|
edges=edges, nodes=nodes
|
||||||
|
)
|
||||||
|
assert isinstance(graph_reduced, nx.Graph)
|
||||||
|
assert isinstance(metrics_reduced, pd.DataFrame)
|
||||||
|
assert list(metrics_reduced.columns) == [
|
||||||
|
"degree",
|
||||||
|
"betweenness",
|
||||||
|
"closeness",
|
||||||
|
"category",
|
||||||
|
"designation",
|
||||||
|
"id",
|
||||||
|
]
|
||||||
|
|
||||||
|
graph = initialize_network_without_metrics(edges=edges, nodes=nodes)
|
||||||
|
assert isinstance(graph_reduced, nx.Graph)
|
||||||
240
tests/utils/networkx/networkx_data_test.py
Normal file
240
tests/utils/networkx/networkx_data_test.py
Normal file
@ -0,0 +1,240 @@
|
|||||||
|
"""Test the initialize Network function."""
|
||||||
|
import datetime
|
||||||
|
from collections.abc import Generator
|
||||||
|
from unittest.mock import patch
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import pytest
|
||||||
|
from sqlalchemy.orm import Session
|
||||||
|
|
||||||
|
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
|
||||||
|
from aki_prj23_transparenzregister.utils.networkx import networkx_data
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
|
def _get_person_relations() -> Generator:
|
||||||
|
data = [
|
||||||
|
{
|
||||||
|
"id_company": 1,
|
||||||
|
"name_company": "0 10 24 Telefondienste GmbH",
|
||||||
|
"relation_type": "GESCHAEFTSFUEHRER",
|
||||||
|
"id_person": 1,
|
||||||
|
"lastname": "Tetau",
|
||||||
|
"firstname": "Nicolas",
|
||||||
|
"date_of_birth": datetime.date(1971, 1, 2),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 1,
|
||||||
|
"name_company": "0 10 24 Telefondienste GmbH",
|
||||||
|
"relation_type": "PROKURIST",
|
||||||
|
"id_person": 2,
|
||||||
|
"lastname": "Dammast",
|
||||||
|
"firstname": "Lutz",
|
||||||
|
"date_of_birth": datetime.date(1966, 12, 6),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"id_person": 3,
|
||||||
|
"lastname": "Tutsch",
|
||||||
|
"firstname": "Rosemarie",
|
||||||
|
"date_of_birth": datetime.date(1941, 10, 9),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"id_person": 4,
|
||||||
|
"lastname": "Staiger",
|
||||||
|
"firstname": "Marc",
|
||||||
|
"date_of_birth": datetime.date(1969, 10, 22),
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company": 2,
|
||||||
|
"name_company": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"id_person": 5,
|
||||||
|
"lastname": "Staiger",
|
||||||
|
"firstname": "Michaela",
|
||||||
|
"date_of_birth": datetime.date(1971, 3, 3),
|
||||||
|
},
|
||||||
|
]
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.utils.networkx.networkx_data.get_all_person_relations"
|
||||||
|
) as mock_get_person_relations:
|
||||||
|
mock_get_person_relations.return_value = pd.DataFrame(data)
|
||||||
|
yield
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
|
def _get_company_relations() -> Generator:
|
||||||
|
data = [
|
||||||
|
{
|
||||||
|
"id_company_to": 2,
|
||||||
|
"name_company_to": "1. Staiger Grundstücksverwaltung GmbH & Co. KG",
|
||||||
|
"relation_type": "HAFTENDER_GESELLSCHAFTER",
|
||||||
|
"name_company_from": "Staiger I. Verwaltung-GmbH",
|
||||||
|
"id_company_from": 3226,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 3,
|
||||||
|
"name_company_to": "1 A Autenrieth Kunststofftechnik GmbH & Co. KG",
|
||||||
|
"relation_type": "HAFTENDER_GESELLSCHAFTER",
|
||||||
|
"name_company_from": "Autenrieth Verwaltungs-GmbH",
|
||||||
|
"id_company_from": 3324,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 5,
|
||||||
|
"name_company_to": "2. Schaper Objekt GmbH & Co. Kiel KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"name_company_from": "Multi-Center Warenvertriebs GmbH",
|
||||||
|
"id_company_from": 2213,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 6,
|
||||||
|
"name_company_to": "AASP Filmproduktionsgesellschaft mbH & Co. Leonie KG",
|
||||||
|
"relation_type": "INHABER",
|
||||||
|
"name_company_from": "ABN AMRO Structured Products Gesellschaft für Fondsbeteiligungen mbH",
|
||||||
|
"id_company_from": 3332,
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id_company_to": 6,
|
||||||
|
"name_company_to": "AASP Filmproduktionsgesellschaft mbH & Co. Leonie KG",
|
||||||
|
"relation_type": "KOMMANDITIST",
|
||||||
|
"name_company_from": "Kallang GmbH",
|
||||||
|
"id_company_from": 3316,
|
||||||
|
},
|
||||||
|
]
|
||||||
|
with patch(
|
||||||
|
"aki_prj23_transparenzregister.utils.networkx.networkx_data.get_all_company_relations"
|
||||||
|
) as mock_get_person_relations:
|
||||||
|
mock_get_person_relations.return_value = pd.DataFrame(data)
|
||||||
|
yield
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(autouse=True)
|
||||||
|
def _set_session(full_db: Session) -> Generator[None, None, None]:
|
||||||
|
"""Sets a session for the dash application to be used."""
|
||||||
|
SessionHandler.session = full_db
|
||||||
|
yield
|
||||||
|
SessionHandler.session = None
|
||||||
|
|
||||||
|
|
||||||
|
def test_import() -> None:
|
||||||
|
"""Checks if an import co company_stats_dash can be made."""
|
||||||
|
assert networkx_data is not None
|
||||||
|
|
||||||
|
|
||||||
|
# def test_initialize_network() -> None:
|
||||||
|
# edges: list = [
|
||||||
|
# {"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
# {"from": "c_53", "to": "p_545", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
# {"from": "c_1", "to": "c_2", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
# {"from": "c_53", "to": "c_1", "type": "HAFTENDER_GESELLSCHAFTER"},
|
||||||
|
# ]
|
||||||
|
# nodes: dict = {
|
||||||
|
# "c_53": {
|
||||||
|
# "id": "c_53",
|
||||||
|
# "name": "1. Freiburger Solarfonds Beteiligungs-KG",
|
||||||
|
# "type": "company",
|
||||||
|
# "color": "blue",
|
||||||
|
# },
|
||||||
|
# "p_545": {
|
||||||
|
# "id": "p_545",
|
||||||
|
# "name": "Wetzel, Jürgen",
|
||||||
|
# "type": "person",
|
||||||
|
# "date_of_birth": datetime.date(1962, 11, 15),
|
||||||
|
# "color": "red",
|
||||||
|
# },
|
||||||
|
# "c_1": {
|
||||||
|
# "id": "c_1",
|
||||||
|
# "name": "Musterfirma",
|
||||||
|
# "type": "company",
|
||||||
|
# "color": "blue",
|
||||||
|
# },
|
||||||
|
# "c_2": {
|
||||||
|
# "id": "c_2",
|
||||||
|
# "name": "Firma 1",
|
||||||
|
# "type": "company",
|
||||||
|
# "color": "blur",
|
||||||
|
# },
|
||||||
|
# }
|
||||||
|
|
||||||
|
|
||||||
|
def test_find_all_company_relations() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
company_relations_df = networkx_data.find_all_company_relations()
|
||||||
|
assert type(company_relations_df) is pd.DataFrame
|
||||||
|
|
||||||
|
|
||||||
|
def test_get_all_company_relations() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
company_relations_df = networkx_data.get_all_company_relations()
|
||||||
|
assert type(company_relations_df) is pd.DataFrame
|
||||||
|
|
||||||
|
|
||||||
|
def test_get_all_person_relations() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
company_relations_df = networkx_data.get_all_person_relations()
|
||||||
|
assert type(company_relations_df) is pd.DataFrame
|
||||||
|
|
||||||
|
|
||||||
|
def test_filter_relation_type() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
relation_dataframe = networkx_data.get_all_company_relations()
|
||||||
|
selected_relation_type = "HAFTENDER_GESELLSCHAFTER"
|
||||||
|
company_relations_df = networkx_data.filter_relation_type(
|
||||||
|
relation_dataframe, selected_relation_type
|
||||||
|
)
|
||||||
|
assert type(company_relations_df) is pd.DataFrame
|
||||||
|
|
||||||
|
|
||||||
|
def test_create_edge_and_node_list() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
person_df = networkx_data.get_all_person_relations()
|
||||||
|
company_df = networkx_data.get_all_company_relations()
|
||||||
|
nodes, edges = networkx_data.create_edge_and_node_list(person_df, company_df)
|
||||||
|
assert isinstance(nodes, dict)
|
||||||
|
assert isinstance(edges, list)
|
||||||
|
|
||||||
|
|
||||||
|
def test_find_company_relations() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
selected_company_id = 1
|
||||||
|
company_relations_df, person_df = networkx_data.find_company_relations(
|
||||||
|
selected_company_id
|
||||||
|
)
|
||||||
|
assert type(company_relations_df) is pd.DataFrame
|
||||||
|
assert type(person_df) is pd.DataFrame
|
||||||
|
|
||||||
|
|
||||||
|
def test_create_edge_and_node_list_for_company() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
company_relations = networkx_data.get_all_company_relations()
|
||||||
|
nodes, edges = networkx_data.create_edge_and_node_list_for_company(
|
||||||
|
company_relations
|
||||||
|
)
|
||||||
|
assert isinstance(nodes, dict)
|
||||||
|
assert isinstance(edges, list)
|
||||||
|
|
||||||
|
|
||||||
|
def test_get_all_metrics_from_id() -> None:
|
||||||
|
"""This Test methods tests if the correct type is returned for the corresponding Function."""
|
||||||
|
company_id = 2
|
||||||
|
metrics = networkx_data.get_all_metrics_from_id(company_id)
|
||||||
|
assert type(metrics) is pd.Series
|
||||||
|
|
||||||
|
|
||||||
|
def test_get_relations_number_from_id() -> None:
|
||||||
|
"""This Test methods tests if the correct type and number of relations is received."""
|
||||||
|
# id = "c_2549"
|
||||||
|
id = 2
|
||||||
|
(
|
||||||
|
relations_lvl_1,
|
||||||
|
relations_lvl_2,
|
||||||
|
relations_lvl_3,
|
||||||
|
) = networkx_data.get_relations_number_from_id(id)
|
||||||
|
assert isinstance(relations_lvl_1, int)
|
||||||
|
assert isinstance(relations_lvl_2, int)
|
||||||
|
assert isinstance(relations_lvl_3, int)
|
||||||
Reference in New Issue
Block a user