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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{
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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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" <td>Tetau</td>\n",
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" <td>Nicolas</td>\n",
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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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" <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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" <td>1. Staiger Grundstücksverwaltung GmbH & Co. KG</td>\n",
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" <td>KOMMANDITIST</td>\n",
|
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" <td>Tutsch</td>\n",
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" <td>Rosemarie</td>\n",
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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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" <th>3</th>\n",
|
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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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" <td>Staiger</td>\n",
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" <td>Marc</td>\n",
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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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" <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",
|
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" <td>Michaela</td>\n",
|
||||
" <td>1971-03-03</td>\n",
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" </tr>\n",
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" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||
" <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",
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||||
"</table>\n",
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"<p>14896 rows × 5 columns</p>\n",
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"</div>"
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],
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"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
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -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)),
|
||||
]
|
||||
)
|
||||
@@ -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)
|
||||
@@ -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)
|
||||
@@ -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 |
|
||||
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>
|
||||
+14
-24
File diff suppressed because one or more lines are too long
Generated
+388
-435
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+7
-1
@@ -55,6 +55,9 @@ cachetools = "^5.3.1"
|
||||
dash = "^2.14.1"
|
||||
dash-auth = "^2.0.0"
|
||||
dash-bootstrap-components = "^1.5.0"
|
||||
dash-daq = "^0.5.0"
|
||||
dash_cytoscape = "^0.2.0"
|
||||
dashvis = "^0.1.3"
|
||||
datetime = "^5.2"
|
||||
deutschland = {git = "https://github.com/TrisNol/deutschland.git", branch = "hotfix/python-3.11-support"}
|
||||
frozendict = "^2.3.8"
|
||||
@@ -63,9 +66,10 @@ matplotlib = "^3.8.1"
|
||||
pgeocode = "^0.4.1"
|
||||
psycopg2-binary = "^2.9.7"
|
||||
pymongo = "^4.6.0"
|
||||
python = "^3.11"
|
||||
python = ">=3.11,<3.13"
|
||||
python-dotenv = "^1.0.0"
|
||||
rapidfuzz = "^3.5.2"
|
||||
scipy = "^1.11.3"
|
||||
seaborn = "^0.13.0"
|
||||
selenium = "^4.15.2"
|
||||
spacy = "^3.6.1"
|
||||
@@ -109,6 +113,7 @@ SQLAlchemy = {version = "*", extras = ["mypy"]}
|
||||
black = "*"
|
||||
loguru-mypy = "*"
|
||||
mypy = "*"
|
||||
networkx-stubs = "*"
|
||||
pandas-stubs = "*"
|
||||
pip-audit = "*"
|
||||
pip-licenses = "*"
|
||||
@@ -121,6 +126,7 @@ types-setuptools = "*"
|
||||
types-six = "*"
|
||||
types-tabulate = "*"
|
||||
types-tqdm = "*"
|
||||
types-urllib3 = "*"
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.4.2"
|
||||
|
||||
@@ -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 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 = {
|
||||
"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.
|
||||
|
||||
Args:
|
||||
@@ -353,4 +379,34 @@ def network_layout(selected_company_id: int) -> html:
|
||||
Returns:
|
||||
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."""
|
||||
from functools import lru_cache
|
||||
|
||||
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(
|
||||
__name__,
|
||||
path="/",
|
||||
title="Transparenzregister: Home",
|
||||
redirect_from=[
|
||||
"/unternehmensdetails",
|
||||
"/personendetails",
|
||||
"/unternehmensdetails/",
|
||||
"/personendetails/",
|
||||
],
|
||||
# TODO Fix redirect in unit tests
|
||||
# redirect_from=[
|
||||
# "/unternehmensdetails",
|
||||
# "/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(
|
||||
[
|
||||
"eigenvector",
|
||||
"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(
|
||||
[
|
||||
html.H1("This is our Home page", style={"margin": 0}),
|
||||
html.Div("This is our Home page content."),
|
||||
]
|
||||
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."""
|
||||
@@ -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},
|
||||
),
|
||||
)
|
||||
@@ -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)
|
||||
@@ -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."""
|
||||
|
||||
from collections.abc import Generator
|
||||
|
||||
import pytest
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
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:
|
||||
|
||||
@@ -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
|
||||
@@ -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
|
||||
@@ -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
|
||||
@@ -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)
|
||||
@@ -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