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@@ -374,7 +286,7 @@
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{
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- "execution_count": 41,
+ "execution_count": 13,
"metadata": {},
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{
@@ -383,7 +295,7 @@
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]
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+ "execution_count": 13,
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@@ -418,16 +330,16 @@
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{
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- "execution_count": 42,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'net_income': 23484.67, 'equity': 65083.84, 'current_assets': 357613.61}"
+ "{'net_income': 100238.5, 'equity': 165322.34, 'current_assets': 435344.07}"
]
},
- "execution_count": 42,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
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@@ -502,7 +414,7 @@
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{
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+ "execution_count": 15,
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@@ -518,7 +430,7 @@
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{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 16,
"metadata": {},
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{
@@ -526,24 +438,30 @@
"output_type": "stream",
"text": [
"MultiIndex([('Aktiva', 'Unnamed: 0_level_1'),\n",
- " ('Aktiva', '31.12.2020 EUR'),\n",
- " ('Aktiva', '31.12.2019 EUR')],\n",
+ " ('Aktiva', '31.12.2021 EUR'),\n",
+ " ('Aktiva', '31.12.2020 EUR')],\n",
" )\n",
"Aktiva Unnamed: 0_level_1 object\n",
+ " 31.12.2021 EUR object\n",
" 31.12.2020 EUR object\n",
- " 31.12.2019 EUR object\n",
"dtype: object\n",
"MultiIndex([('Passiva', 'Unnamed: 0_level_1'),\n",
- " ('Passiva', '31.12.2020 EUR'),\n",
- " ('Passiva', '31.12.2019 EUR')],\n",
+ " ('Passiva', '31.12.2021 EUR'),\n",
+ " ('Passiva', '31.12.2020 EUR')],\n",
" )\n",
"Passiva Unnamed: 0_level_1 object\n",
+ " 31.12.2021 EUR object\n",
" 31.12.2020 EUR object\n",
- " 31.12.2019 EUR object\n",
"dtype: object\n",
"Index(['Angaben zur Identifikation der Gesellschaft laut Registergericht', 'Angaben zur Identifikation der Gesellschaft laut Registergericht.1'], dtype='object')\n",
"Angaben zur Identifikation der Gesellschaft laut Registergericht object\n",
"Angaben zur Identifikation der Gesellschaft laut Registergericht.1 object\n",
+ "dtype: object\n",
+ "MultiIndex([('Kreditentwicklung', 'Unnamed: 0_level_1'),\n",
+ " ( 'Betrag', 'EUR')],\n",
+ " )\n",
+ "Kreditentwicklung Unnamed: 0_level_1 object\n",
+ "Betrag EUR object\n",
"dtype: object\n"
]
},
@@ -553,7 +471,7 @@
"{}"
]
},
- "execution_count": 46,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -574,19 +492,46 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 22,
"metadata": {},
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{
- "ename": "KeyError",
- "evalue": "'Passiva'",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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11\u001b[0m \u001b[39mreturn\u001b[39;00m result\n\u001b[0;32m
14\u001b[0m bilanz \u001b[39m=\u001b[39m get_bilanz(sample_report)\n\u001b[1;32m--->
15\u001b[0m bilanz[\u001b[39m\"\u001b[39;49m\u001b[39mPassiva\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39mhead()\n",
- "\u001b[1;31mKeyError\u001b[0m: 'Passiva'"
- ]
+ "data": {
+ "text/html": [
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+ "
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+ ],
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@@ -600,6 +545,8 @@
" StringIO(str(tag.findNext(\"table\", {\"class\": \"std_table\"})))\n",
" )[0]\n",
" result[pos] = pos_results\n",
+ " else:\n",
+ " result[pos] = pd.DataFrame([])\n",
" return result\n",
"\n",
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@@ -609,58 +556,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Int64Index([0, 1], dtype='int64')\n",
- "Index(['Unnamed: 0', 'Anhang', '31.12.2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', 'Anhang', '2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Aufgliederung nach Tätigkeitsbereichen', '2021 TEUR',\n",
- " 'Vorjahr TEUR'],\n",
- " dtype='object')\n",
- "Index(['Aufgliederung nach Inland und Ausland', '2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', '31.12.2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', '31.12.2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', '31.12.2021'], dtype='object')\n",
- "Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
- "Int64Index([0, 1, 2], dtype='int64')\n",
- "Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', '31.12.2021 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
- "Index(['Unnamed: 0', '2021 Anzahl MA', 'Vorjahr Anzahl MA'], dtype='object')\n",
- "MultiIndex([('Art des Geschäfts', 'Unnamed: 0_level_1'),\n",
- " ('Art der Beziehung', 'Gesellschafterin TEUR'),\n",
- " ('Art der Beziehung', 'Verbundene Unternehmen TEUR')],\n",
+ "MultiIndex([('Aktiva', 'Unnamed: 0_level_1'),\n",
+ " ('Aktiva', '31.12.2021 EUR'),\n",
+ " ('Aktiva', '31.12.2020 EUR')],\n",
" )\n",
- "Int64Index([0, 1], dtype='int64')\n",
- "MultiIndex([( 'Unnamed: 0_level_0', ...),\n",
- " ('Anschaffungs- oder Herstellungskosten', ...),\n",
- " ('Anschaffungs- oder Herstellungskosten', ...),\n",
- " ('Anschaffungs- oder Herstellungskosten', ...),\n",
- " ('Anschaffungs- oder Herstellungskosten', ...)],\n",
+ "MultiIndex([('Passiva', 'Unnamed: 0_level_1'),\n",
+ " ('Passiva', '31.12.2021 EUR'),\n",
+ " ('Passiva', '31.12.2020 EUR')],\n",
" )\n",
- "MultiIndex([('Unnamed: 0_level_0', ...),\n",
- " ( 'Abschreibungen', ...),\n",
- " ( 'Abschreibungen', ...),\n",
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- " ( 'Abschreibungen', ...)],\n",
- " )\n",
- "MultiIndex([('Unnamed: 0_level_0', 'Unnamed: 0_level_1'),\n",
- " ( 'Buchwerte', 'Stand 31.12.2021 EUR'),\n",
- " ( 'Buchwerte', 'Stand 31.12.2020 EUR')],\n",
- " )\n",
- "Index(['Nichtfinanzieller Leistungsindikator', 'Unnamed: 1', '2021', '2020',\n",
- " '2019'],\n",
- " dtype='object')\n",
- "Index(['Gewinn- und Verlustrechnung', '2021 TEUR', 'Vorjahr TEUR',\n",
- " 'Veränderung TEUR'],\n",
- " dtype='object')\n",
- "Index(['Bilanz', '31.12.2021 TEUR', 'Vorjahr TEUR', 'Veränderung TEUR'], dtype='object')\n"
+ "Index(['Angaben zur Identifikation der Gesellschaft laut Registergericht', 'Angaben zur Identifikation der Gesellschaft laut Registergericht.1'], dtype='object')\n",
+ "MultiIndex([('Kreditentwicklung', 'Unnamed: 0_level_1'),\n",
+ " ( 'Betrag', 'EUR')],\n",
+ " )\n"
]
}
],
@@ -698,7 +612,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.7"
+ "version": "3.11.3"
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},
diff --git a/poetry.lock b/poetry.lock
index a275658..a2368dd 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4823 +1,5554 @@
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diff --git a/pyproject.toml b/pyproject.toml
index 36ce091..2aa450f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ version = "0.1.0"
SQLAlchemy = {version = "^1.4.46", extras = ["mypy"]}
dash = "^2.11.1"
dash-bootstrap-components = "^1.4.2"
+deutschland = {git = "https://github.com/TrisNol/deutschland.git", branch = "hotfix/python-3.11-support"}
loguru = "^0.7.0"
matplotlib = "^3.7.1"
plotly = "^5.14.1"
@@ -48,6 +49,10 @@ seaborn = "^0.12.2"
selenium = "^4.10.0"
tqdm = "^4.65.0"
+# TODO Add dependent libraries (i.e., deutshcland, plotly, etc)
+[tool.poetry.extras]
+ingest = ["selenium"]
+
[tool.poetry.group.develop.dependencies]
black = {extras = ["jupyter"], version = "^23.3.0"}
jupyterlab = "^4.0.0"
diff --git a/src/aki_prj23_transparenzregister/apps/__init__.py b/src/aki_prj23_transparenzregister/apps/__init__.py
new file mode 100644
index 0000000..69748f7
--- /dev/null
+++ b/src/aki_prj23_transparenzregister/apps/__init__.py
@@ -0,0 +1 @@
+"""Main applications."""
diff --git a/src/aki_prj23_transparenzregister/apps/enrich_company_financials.py b/src/aki_prj23_transparenzregister/apps/enrich_company_financials.py
new file mode 100644
index 0000000..ce39892
--- /dev/null
+++ b/src/aki_prj23_transparenzregister/apps/enrich_company_financials.py
@@ -0,0 +1,63 @@
+"""Add financial data to companies."""
+import typing
+
+from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
+from aki_prj23_transparenzregister.utils.data_extraction.bundesanzeiger import (
+ Bundesanzeiger,
+)
+from aki_prj23_transparenzregister.utils.mongo.company_mongo_service import (
+ CompanyMongoService,
+)
+from aki_prj23_transparenzregister.utils.mongo.connector import MongoConnector
+
+
+def work(company: typing.Any, company_service: CompanyMongoService) -> None:
+ """Process company regarding financials.
+
+ Args:
+ company (dict): Company to process
+ company_service (CompanyMongoService): Interface to Company collection on MongoDB
+ """
+ yearly_results = Bundesanzeiger().get_information(
+ company["name"], company["location"]["city"]
+ )
+ yearly_results_data = {}
+ for _index, row in yearly_results.iterrows():
+ yearly_results_data[row.jahr] = {
+ "auditors": [auditor.to_dict() for auditor in row.auditors],
+ "financials": row.financial_results,
+ }
+
+ company_service.add_yearly_results(company["_id"], yearly_results_data)
+
+
+if __name__ == "__main__":
+ import concurrent.futures
+
+ from loguru import logger
+
+ config_provider = JsonFileConfigProvider("./secrets.json")
+
+ mongo_connector = MongoConnector(config_provider.get_mongo_connection_string())
+ company_service = CompanyMongoService(mongo_connector)
+
+ num_threads = 25
+ companies = company_service.get_where_no_financial_results()
+ with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
+ # Submit tasks for each entry in the list
+ future_to_entry = {
+ executor.submit(work, entry, company_service): entry for entry in companies
+ }
+
+ # with tqdm(total=len(companies)) as pbar:
+ # Wait for all tasks to complete
+ for future in concurrent.futures.as_completed(future_to_entry):
+ entry = future_to_entry[future]
+ logger.info(entry["name"])
+ try:
+ # Get the result of the completed task (if needed)
+ result = future.result()
+ # pbar.set_description(entry["name"])
+ # pbar.update(1)
+ except Exception as e:
+ logger.error(f"Error processing entry {e}")
diff --git a/src/aki_prj23_transparenzregister/models/auditor.py b/src/aki_prj23_transparenzregister/models/auditor.py
new file mode 100644
index 0000000..28856a8
--- /dev/null
+++ b/src/aki_prj23_transparenzregister/models/auditor.py
@@ -0,0 +1,18 @@
+"""Auditor model."""
+from dataclasses import asdict, dataclass
+
+
+@dataclass
+class Auditor:
+ """Auditor."""
+
+ name: str
+ company: str | None
+
+ def to_dict(self) -> dict:
+ """_summary_.
+
+ Returns:
+ dict: _description_
+ """
+ return asdict(self)
diff --git a/src/aki_prj23_transparenzregister/models/company.py b/src/aki_prj23_transparenzregister/models/company.py
index dedd7cd..d160826 100644
--- a/src/aki_prj23_transparenzregister/models/company.py
+++ b/src/aki_prj23_transparenzregister/models/company.py
@@ -45,19 +45,61 @@ class CompanyRelationship(ABC):
location: Location
+class FinancialKPIEnum(Enum):
+ """Financial KPI keys."""
+
+ # Umsatz || Erlöse
+ REVENUE = "revenue"
+ # Jahresüberschuss || Nettoeinkommen
+ NET_INCOME = "net_income"
+ # Ebit
+ EBIT = "ebit"
+ # Ebitda
+ EBITDA = "ebitda"
+ # Bruttogewinn
+ GROSS_PROFIT = "gross_profit"
+ # Betriebsgewinn
+ OPERATING_PROFIT = "operating_profit"
+ # Bilanzsumme
+ ASSETS = "assets"
+ # Gesamtverbindlichkeiten
+ LIABILITIES = "liabilities"
+ # Eigenkapital
+ EQUITY = "equity"
+ # Umlaufvermögen
+ CURRENT_ASSETS = "current_assets"
+ # Kurzfristige Verbindlichkeiten
+ CURRENT_LIABILITIES = "current_liabilities"
+ # Langfristige Verbindlichkeiten
+ LONG_TERM_DEBT = "long_term_debt"
+ # Kurzfristige Verbindlichkeiten
+ SHORT_TERM_DEBT = "short_term_debt"
+ # Barmittel
+ CASH_AND_CASH_EQUIVALENTS = "cash_and_cash_equivalents"
+ # Dividende
+ DIVIDENDS = "dividends"
+ # Cash Flow
+ CASH_FLOW = "cash_flow"
+
+
+@dataclass
+class YearlyResult:
+ """Company yearly result."""
+
+ year: int
+ kpis: dict[FinancialKPIEnum, float]
+
+
@dataclass
class Company:
- """_summary_.
-
- Returns:
- _type_: _description_
- """
+ """Company dataclass."""
id: CompanyID
location: Location
name: str
last_update: str
relationships: list[CompanyRelationship]
+ # yearly_results: list[FinancialResults]
def to_dict(self) -> dict:
"""_summary_.
diff --git a/src/aki_prj23_transparenzregister/utils/data_extraction/__init__.py b/src/aki_prj23_transparenzregister/utils/data_extraction/__init__.py
new file mode 100644
index 0000000..8adbeba
--- /dev/null
+++ b/src/aki_prj23_transparenzregister/utils/data_extraction/__init__.py
@@ -0,0 +1 @@
+"""Everything regarding data extraction from various sources."""
diff --git a/src/aki_prj23_transparenzregister/utils/data_extraction/bundesanzeiger.py b/src/aki_prj23_transparenzregister/utils/data_extraction/bundesanzeiger.py
new file mode 100644
index 0000000..fd5f1ab
--- /dev/null
+++ b/src/aki_prj23_transparenzregister/utils/data_extraction/bundesanzeiger.py
@@ -0,0 +1,183 @@
+"""Fetch data from Bundesanzeiger."""
+import re
+
+import pandas as pd
+from bs4 import BeautifulSoup
+from deutschland.bundesanzeiger import Bundesanzeiger as Ba
+
+from aki_prj23_transparenzregister.models.auditor import Auditor
+from aki_prj23_transparenzregister.models.company import FinancialKPIEnum
+
+pd.options.mode.chained_assignment = None # type: ignore
+
+
+class Bundesanzeiger:
+ """Bundesanzeiger wrapper to export relevant information."""
+
+ def get_information(self, company_name: str, city: str | None) -> pd.DataFrame:
+ """Extract relevant information from all found yearly results for the given company.
+
+ Args:
+ company_name (str): Name of the company to search for
+ city (Optional[str]): City where the company is registered
+
+ Returns:
+ pd.DataFrame: Result
+ """
+ ba = Ba()
+ # Get Bundesanzeiger entries for company
+ reports = ba.get_reports(f"{company_name} {city}")
+ # Transform to list of data
+ report_contents = []
+ for key in reports:
+ report_contents.append(reports[key])
+
+ if len(report_contents) == 0:
+ return pd.DataFrame()
+ # Transform to DataFrame and filter out irrelevant entries
+ df_data = pd.DataFrame(report_contents)
+ df_data = self.filter_reports(df_data)
+
+ # Filter out entries of different companies
+ df_data = df_data.loc[df_data.company == company_name]
+
+ # Add Auditor information
+ df_data["auditors"] = df_data.raw_report.apply(self.extract_auditors)
+
+ # Add Financial information
+ df_data["financial_results"] = df_data.raw_report.apply(
+ self.extract_financial_results
+ )
+
+ # Remove irrelevant columns
+ return df_data.drop(["raw_report"], axis=1)
+
+ def filter_reports(self, df_reports: pd.DataFrame) -> pd.DataFrame:
+ """Returns only reports of type `Jahresabschluss` and extracts the year of the report.
+
+ Args:
+ df_reports (pd.DataFrame): DataFrame containing list of reports
+
+ Returns:
+ pd.DataFrame: Filtered and pruned DataFrame
+ """
+ df_reports["type"] = df_reports.name.apply(lambda name: name.split(" ")[0])
+ df_reports = df_reports.loc[df_reports.type == "Jahresabschluss"]
+ df_reports["jahr"] = df_reports.name.apply(
+ lambda name: name.split(" ")[-1].split(".")[-1]
+ )
+ return df_reports.drop(["name", "report", "type"], axis=1)
+
+ def extract_auditor_company(self, report: str) -> str | None:
+ """Extract the name of an auditor company from the given yearly results report.
+
+ Args:
+ report (str): Yearly results report as raw string
+
+ Returns:
+ str | None: Name of the auditor company if found, otherwise None
+ """
+ soup = BeautifulSoup(report, features="html.parser")
+ temp = soup.find_all("b")
+ for elem in temp:
+ br = elem.findChildren("br")
+ if len(br) > 0:
+ return elem.text.split("\n")[1].strip()
+ return None
+
+ def extract_auditors(self, report: str) -> list:
+ """Find the list of auditors involved in the given yearly results report.
+
+ Args:
+ report (str): Yearly results report as raw string
+
+ Returns:
+ list[Auditor]: List of Auditors found in the given report
+ """
+ auditor_company = self.extract_auditor_company(report)
+ auditor_regex = r"[a-z A-Z,.'-]+, Wirtschaftsprüfer"
+ hits = re.findall(auditor_regex, report)
+ return [
+ Auditor(hit.replace(", Wirtschaftsprüfer", "").lstrip(), auditor_company)
+ for hit in hits
+ ]
+
+ def __extract_kpis__(self, report: str) -> dict:
+ """Source: https://github.com/bundesAPI/deutschland/pull/87/files#diff-f5b9db5384cf523fcc677056065041e7793bfc4da9cf74c4eebd6fab732739bd.
+
+ Extracts Key Performance Indicators (KPIs) from the financial reports.
+
+ Args:
+ report (str): The yearly report as a parsed string
+
+ Returns:
+ dict: A dictionary containing the extracted KPIs with their report hash as keys and KPIs as values.
+ """
+ kpis = {}
+
+ # Define KPI patterns to search for
+ kpi_patterns = {
+ FinancialKPIEnum.REVENUE.value: r"(?:revenue|umsatz|erlöse)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.NET_INCOME.value: r"(?:net income|jahresüberschuss|nettoeinkommen|Ergebnis nach Steuern)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.EBIT.value: r"(?:ebit|operating income)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.EBITDA.value: r"(?:ebitda)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.GROSS_PROFIT.value: r"(?:gross profit|bruttogewinn)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.OPERATING_PROFIT.value: r"(?:operating profit|betriebsgewinn)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.ASSETS.value: r"(?:total assets|bilanzsumme)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.LIABILITIES.value: r"(?:total liabilities|gesamtverbindlichkeiten)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.EQUITY.value: r"(?:shareholders'? equity|eigenkapital)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.CURRENT_ASSETS.value: r"(?:current assets|umlaufvermögen)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.CURRENT_LIABILITIES.value: r"(?:current liabilities|kurzfristige verbindlichkeiten)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.LONG_TERM_DEBT.value: r"(?:long[-\s]?term debt|langfristige verbindlichkeiten)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.SHORT_TERM_DEBT.value: r"(?:short[-\s]?term debt|kurzfristige verbindlichkeiten)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.CASH_AND_CASH_EQUIVALENTS.value: r"(?:cash (?:and cash equivalents)?|barmittel)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.DIVIDENDS.value: r"(?:dividends?|dividende)[:\s]*([\d,.]+[mmb]?)",
+ FinancialKPIEnum.CASH_FLOW.value: r"(?:cash flow|cashflow|cash flow from operating activities)[:\s]*([\d,.]+[mmb]?)",
+ }
+
+ for kpi, pattern in kpi_patterns.items():
+ match = re.search(pattern, report, flags=re.IGNORECASE | re.UNICODE)
+ if match:
+ value = match.group(1)
+
+ # Clean and validate the extracted number
+ try:
+ if not value: # Check if value is empty
+ cleaned_value = None
+ else:
+ multiplier = 1
+ if value[-1].lower() == "m":
+ value = value[:-1]
+ multiplier = 1_000_000
+ elif value[-1].lower() == "b":
+ value = value[:-1]
+ multiplier = 1_000_000_000
+
+ # Remove commas after checking for multipliers
+ value = value.replace(".", "").replace(",", ".").strip()
+ cleaned_value = float(value) * multiplier
+ except ValueError:
+ cleaned_value = None
+
+ if cleaned_value is not None:
+ kpis[kpi] = cleaned_value
+ return kpis
+
+ def extract_financial_results(self, report: str) -> dict:
+ """Extract financial data from given report.
+
+ Args:
+ report (str): Report to be analyzed
+
+ Returns:
+ dict: Results
+ """
+ report_parsed = (
+ BeautifulSoup(report, features="html.parser").get_text().replace("\n", " ")
+ )
+ return self.__extract_kpis__(report_parsed)
+
+
+if __name__ == "__main__":
+ ba_wrapper = Bundesanzeiger()
+ ba_wrapper.get_information("Atos IT-Dienstleistung und Beratung GmbH", None)
diff --git a/src/aki_prj23_transparenzregister/utils/mongo/company_mongo_service.py b/src/aki_prj23_transparenzregister/utils/mongo/company_mongo_service.py
index 40f4574..c2641bb 100644
--- a/src/aki_prj23_transparenzregister/utils/mongo/company_mongo_service.py
+++ b/src/aki_prj23_transparenzregister/utils/mongo/company_mongo_service.py
@@ -1,7 +1,10 @@
"""CompanyMongoService."""
-from pymongo.results import InsertOneResult
+from threading import Lock
-from aki_prj23_transparenzregister.models.company import Company, CompanyID
+from bson.objectid import ObjectId
+from pymongo.results import InsertOneResult, UpdateResult
+
+from aki_prj23_transparenzregister.models.company import Company
from aki_prj23_transparenzregister.utils.mongo.connector import MongoConnector
@@ -15,6 +18,7 @@ class CompanyMongoService:
connector (MongoConnector): _description_
"""
self.collection = connector.database["companies"]
+ self.lock = Lock() # Create a lock for synchronization
def get_all(self) -> list[Company]:
"""_summary_.
@@ -22,10 +26,11 @@ class CompanyMongoService:
Returns:
list[Company]: _description_
"""
- result = self.collection.find()
- return list(result)
+ with self.lock:
+ result = self.collection.find()
+ return list(result)
- def get_by_id(self, id: CompanyID) -> Company | None:
+ def get_by_id(self, id: str) -> Company | None:
"""_summary_.
Args:
@@ -34,10 +39,46 @@ class CompanyMongoService:
Returns:
Company | None: _description_
"""
- result = list(self.collection.find({"id": id}))
- if len(result) == 1:
- return result[0]
- return None
+ with self.lock:
+ result = list(self.collection.find({"id": id}))
+ if len(result) == 1:
+ return result[0]
+ return None
+
+ def get_by_object_id(self, _id: str) -> dict | None:
+ """Find an object by given _id.
+
+ Args:
+ _id (str): ID
+
+ Returns:
+ Company | None: Entry if found, otherwise None
+ """
+ with self.lock:
+ result = list(self.collection.find({"_id": ObjectId(_id)}))
+ if len(result) == 1:
+ return result[0]
+ return None
+
+ def get_where_no_financial_results(self) -> list[dict]:
+ """Get all entries that have no yearly_results.
+
+ Returns:
+ list[dict]: List of companies found
+ """
+ with self.lock:
+ return list(
+ self.collection.find({"$or": [{"yearly_results": {"$exists": False}}]})
+ )
+
+ def get_where_yearly_results(self) -> list[dict]:
+ """Get a list of all companies with valid yearly_results (interesting entries for data loader).
+
+ Returns:
+ list[dict]: List of companies
+ """
+ with self.lock:
+ return list(self.collection.find({"yearly_results": {"$gt": {}}}))
def insert(self, company: Company) -> InsertOneResult:
"""_summary_.
@@ -48,4 +89,20 @@ class CompanyMongoService:
Returns:
_type_: _description_
"""
- return self.collection.insert_one(company.to_dict())
+ with self.lock:
+ return self.collection.insert_one(company.to_dict())
+
+ def add_yearly_results(self, _id: str, yearly_results: dict) -> UpdateResult:
+ """Add the `yearly_results` field to a Company entry.
+
+ Args:
+ _id (str): ID of the object
+ yearly_results (dict): Yearly results dictionary
+
+ Returns:
+ UpdateResult: Result
+ """
+ with self.lock:
+ return self.collection.update_one(
+ {"_id": ObjectId(_id)}, {"$set": {"yearly_results": yearly_results}}
+ )
diff --git a/tests/apps/enrich_company_financials_test.py b/tests/apps/enrich_company_financials_test.py
new file mode 100644
index 0000000..22232bd
--- /dev/null
+++ b/tests/apps/enrich_company_financials_test.py
@@ -0,0 +1,36 @@
+"""Tests for the enrich_company_financials module."""
+from unittest.mock import Mock, patch
+
+import pandas as pd
+
+from aki_prj23_transparenzregister.apps import enrich_company_financials
+from aki_prj23_transparenzregister.models.auditor import Auditor
+
+
+def test_import_enrich_company_financials() -> None:
+ """Testing if the enrich_company_financials can be imported."""
+ assert enrich_company_financials
+
+
+@patch(
+ "aki_prj23_transparenzregister.apps.enrich_company_financials.Bundesanzeiger.get_information"
+)
+@patch(
+ "aki_prj23_transparenzregister.apps.enrich_company_financials.CompanyMongoService"
+)
+def test_work(mock_compnay_service: Mock, mock_bundesanzeiger: Mock) -> None:
+ mock_bundesanzeiger.return_value = pd.DataFrame(
+ [
+ {
+ "jahr": "2042",
+ "auditors": [Auditor(name="", company="")],
+ "financial_results": [],
+ }
+ ]
+ )
+ # mock_compnay_service.add_yearly_resreturn_value
+ enrich_company_financials.work(
+ {"_id": "", "name": "ABC AG", "location": {"city": "Haltern am See"}},
+ mock_compnay_service,
+ )
+ assert enrich_company_financials
diff --git a/tests/utils/data_extraction/__init__.py b/tests/utils/data_extraction/__init__.py
new file mode 100644
index 0000000..0388525
--- /dev/null
+++ b/tests/utils/data_extraction/__init__.py
@@ -0,0 +1 @@
+"""Tests for data_extraction."""
diff --git a/tests/utils/data_extraction/bundesanzeiger_test.py b/tests/utils/data_extraction/bundesanzeiger_test.py
new file mode 100644
index 0000000..30e8007
--- /dev/null
+++ b/tests/utils/data_extraction/bundesanzeiger_test.py
@@ -0,0 +1,111 @@
+from unittest.mock import Mock, patch
+
+import pandas as pd
+
+from aki_prj23_transparenzregister.models.company import FinancialKPIEnum
+from aki_prj23_transparenzregister.utils.data_extraction.bundesanzeiger import (
+ Bundesanzeiger,
+)
+
+
+def test_extract_auditor_company_no_hits() -> None:
+ input_data = """
+
+ Nothing to see here
+
+ """
+ ba = Bundesanzeiger()
+ result = ba.extract_auditor_company(input_data)
+ assert result is None
+
+
+def test_extract_auditor_company() -> None:
+ company_name = "Korrupte Wirtschaftsprüfer GmbH & Co. KG"
+ input_data = f"""
+
+ {company_name}
+
+ Max Mustermann
+
+ """
+ ba = Bundesanzeiger()
+ result = ba.extract_auditor_company(input_data)
+ assert result == company_name
+
+
+def test_extract_kpis() -> None:
+ input_data = """
+ Die Prj23_Transparenzregister GmbH erwirtschaftete einen Jahresüberschuss 10.000,43 €.
+ Des Weiteren sanken die Gesamtverbindlichkeiten 42,00 €
+ """
+ ba = Bundesanzeiger()
+ result = ba.__extract_kpis__(input_data)
+
+ net_income = 10000.43
+ liabilities = 42.00
+
+ assert result[FinancialKPIEnum.NET_INCOME.value] == net_income
+ assert result[FinancialKPIEnum.LIABILITIES.value] == liabilities
+
+
+def test_extracct_financial_results() -> None:
+ input_data = """
+
+ Die Prj23_Transparenzregister GmbH erwirtschaftete einen Jahresüberschuss 10.000,43 €.
+
+ Dies ist ein Platzhalter, der ignoriert werden soll
+ Des Weiteren sanken die Gesamtverbindlichkeiten 42,00 €
+ """
+ ba = Bundesanzeiger()
+ result = ba.extract_financial_results(input_data)
+
+ net_income = 10000.43
+ liabilities = 42.00
+
+ assert result[FinancialKPIEnum.NET_INCOME.value] == net_income
+ assert result[FinancialKPIEnum.LIABILITIES.value] == liabilities
+
+
+def test_filter_reports() -> None:
+ test_data = [
+ {"name": "Bedienungsanleitung", "report": "", "raw_report": ""},
+ {"name": "Jahresabschluss 1998", "report": "", "raw_report": ""},
+ ]
+ test_df = pd.DataFrame(test_data)
+ ba = Bundesanzeiger()
+ result = ba.filter_reports(test_df)
+ assert len(result) == 1
+ assert result.iloc[0].jahr == "1998"
+
+
+@patch(
+ "aki_prj23_transparenzregister.utils.data_extraction.bundesanzeiger.Ba.get_reports"
+)
+def test_get_information(mock_bundesanzeiger: Mock) -> None:
+ mock_bundesanzeiger.return_value = {
+ "1": {
+ "name": "Bedienungsanleitung",
+ "report": "",
+ "company": "",
+ "raw_report": "",
+ },
+ "2": {
+ "name": "Jahresabschluss 1998",
+ "report": "",
+ "company": "PRJ 23 Transparenzregister GmbH",
+ "raw_report": "",
+ },
+ }
+ ba = Bundesanzeiger()
+ result = ba.get_information("PRJ 23 Transparenzregister GmbH", "Iserlohn")
+ assert len(result) == 1
+
+
+@patch(
+ "aki_prj23_transparenzregister.utils.data_extraction.bundesanzeiger.Ba.get_reports"
+)
+def test_get_information_no_results(mock_bundesanzeiger: Mock) -> None:
+ mock_bundesanzeiger.return_value = {}
+ ba = Bundesanzeiger()
+ result = ba.get_information("PRJ 23 Transparenzregister GmbH", "Iserlohn")
+ assert len(result) == 0
diff --git a/tests/utils/mongo/company_mongo_service_test.py b/tests/utils/mongo/company_mongo_service_test.py
index 5078cc2..aa75949 100644
--- a/tests/utils/mongo/company_mongo_service_test.py
+++ b/tests/utils/mongo/company_mongo_service_test.py
@@ -3,7 +3,7 @@ from unittest.mock import Mock
import pytest
-from aki_prj23_transparenzregister.models.company import Company
+from aki_prj23_transparenzregister.models.company import Company, CompanyID, Location
from aki_prj23_transparenzregister.utils.mongo.company_mongo_service import (
CompanyMongoService,
)
@@ -73,7 +73,7 @@ def test_by_id_no_result(mock_mongo_connector: Mock, mock_collection: Mock) -> N
mock_mongo_connector.database = {"companies": mock_collection}
service = CompanyMongoService(mock_mongo_connector)
mock_collection.find.return_value = []
- assert service.get_by_id("Does not exist") is None # type: ignore
+ assert service.get_by_id("Does not exist") is None
def test_by_id_result(mock_mongo_connector: Mock, mock_collection: Mock) -> None:
@@ -87,7 +87,7 @@ def test_by_id_result(mock_mongo_connector: Mock, mock_collection: Mock) -> None
service = CompanyMongoService(mock_mongo_connector)
mock_entry = {"id": "Does exist", "vaue": 42}
mock_collection.find.return_value = [mock_entry]
- assert service.get_by_id("Does exist") == mock_entry # type: ignore
+ assert service.get_by_id("Does exist") == mock_entry
def test_insert(mock_mongo_connector: Mock, mock_collection: Mock) -> None:
@@ -101,4 +101,55 @@ def test_insert(mock_mongo_connector: Mock, mock_collection: Mock) -> None:
service = CompanyMongoService(mock_mongo_connector)
mock_result = 42
mock_collection.insert_one.return_value = mock_result
- assert service.insert(Company(None, None, "", "", [])) == mock_result # type: ignore
+ assert (
+ service.insert(
+ Company(CompanyID("", ""), Location("Hier und Dort"), "", "", [])
+ )
+ == mock_result
+ )
+
+
+def test_get_by_object_id_no_result(
+ mock_mongo_connector: Mock, mock_collection: Mock
+) -> None:
+ mock_mongo_connector.database = {"companies": mock_collection}
+ service = CompanyMongoService(mock_mongo_connector)
+ mock_result: list = []
+ mock_collection.find.return_value = mock_result
+ assert service.get_by_object_id("649f16a1e198338c3b44299e") is None
+
+
+def test_get_by_object_id(mock_mongo_connector: Mock, mock_collection: Mock) -> None:
+ mock_mongo_connector.database = {"companies": mock_collection}
+ service = CompanyMongoService(mock_mongo_connector)
+ mock_result: list = [{"_id": "abc", "brille?": "Fielmann", "Hotel?": "Trivago"}]
+ mock_collection.find.return_value = mock_result
+ assert service.get_by_object_id("612316a1e198338c3b44299e") == mock_result[0]
+
+
+def test_get_where_financial_no_results(
+ mock_mongo_connector: Mock, mock_collection: Mock
+) -> None:
+ mock_mongo_connector.database = {"companies": mock_collection}
+ service = CompanyMongoService(mock_mongo_connector)
+ mock_result: list = [{"_id": "abc", "brille?": "Fielmann", "Hotel?": "Trivago"}]
+ mock_collection.find.return_value = mock_result
+ assert service.get_where_no_financial_results() == mock_result
+
+
+def test_get_where_financial_results(
+ mock_mongo_connector: Mock, mock_collection: Mock
+) -> None:
+ mock_mongo_connector.database = {"companies": mock_collection}
+ service = CompanyMongoService(mock_mongo_connector)
+ mock_result: list = [{"_id": "abc", "brille?": "Fielmann", "Hotel?": "Trivago"}]
+ mock_collection.find.return_value = mock_result
+ assert service.get_where_yearly_results() == mock_result
+
+
+def test_add_yearly_reslults(mock_mongo_connector: Mock, mock_collection: Mock) -> None:
+ mock_mongo_connector.database = {"companies": mock_collection}
+ service = CompanyMongoService(mock_mongo_connector)
+ mock_result: list = [{"_id": "abc", "brille?": "Fielmann", "Hotel?": "Trivago"}]
+ mock_collection.update_one.return_value = mock_result
+ assert service.add_yearly_results("612316a1e198338c3b44299e", {}) == mock_result