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https://github.com/fhswf/aki_prj23_transparenzregister.git
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Added test
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Tim
366
Jupyter/NetworkX/archive_prod/ui_elements.py
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366
Jupyter/NetworkX/archive_prod/ui_elements.py
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"""Dash elements."""
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import pandas as pd
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import plotly.graph_objs as go
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from cachetools import TTLCache, cached
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from dash import dash_table, dcc, html
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from sqlalchemy.engine import Engine
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from sqlalchemy.orm import Session
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from aki_prj23_transparenzregister.ui.archive.networkx_dash import networkx_component
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from aki_prj23_transparenzregister.utils.sql import entities
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COLORS = {
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"light": "#edefef",
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"lavender-blush": "#f3e8ee",
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"ash-gray": "#bacdb0",
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"cambridge-blue": "#729b79",
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"paynes-gray": "#475b63",
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"raisin-black": "#2e2c2f",
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}
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def get_company_data(session: Session) -> pd.DataFrame:
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"""Creates a session to the database and get's all available company data.
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Args:
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session: A session connecting to the database.
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Returns:
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A dataframe containing all available company data including the corresponding district court.
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"""
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query_company = session.query(entities.Company, entities.DistrictCourt.name).join(
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entities.DistrictCourt
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)
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engine = session.bind
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if not isinstance(engine, Engine):
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raise TypeError
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return pd.read_sql(str(query_company), engine, index_col="company_id")
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def get_finance_data(session: Session) -> pd.DataFrame:
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"""Collects all available company data.
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Args:
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session: A session connecting to the database.
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Returns:
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A dataframe containing all financial data of all companies.
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"""
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query_finance = session.query(
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entities.AnnualFinanceStatement, entities.Company.name, entities.Company.id
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).join(entities.Company)
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engine = session.bind
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if not isinstance(engine, Engine):
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raise TypeError
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return pd.read_sql(str(query_finance), engine)
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@cached( # type: ignore
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cache=TTLCache(maxsize=1, ttl=300),
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key=lambda session: 0 if session is None else str(session.bind),
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)
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def get_options(session: Session | None) -> dict[int, str]:
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"""Collects the search options for the companies.
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Args:
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session: A session connecting to the database.
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Returns:
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A dict containing the company id as key and its name.
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"""
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if not session:
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return {}
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return get_company_data(session)["company_name"].to_dict()
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def create_header(options: dict) -> html:
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"""Creates header for dashboard.
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Args:
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options: A dictionary with company names and ids for the dropdown.
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Returns:
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The html div to create the page's header including the name of the page and the search for companies.
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"""
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return html.Div(
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className="header-wrapper",
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children=[
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html.Div(
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className="header-title",
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children=[
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html.I(
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id="home-button",
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n_clicks=0,
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className="bi-house-door-fill",
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),
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html.H1(
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className="header-title-text",
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children="Transparenzregister für Kapitalgesellschaften",
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),
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],
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),
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html.Div(
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className="header-search",
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children=[
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html.Div(
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className="header-search-dropdown",
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children=[
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dcc.Dropdown(
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id="select_company",
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options=[
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{"label": o, "value": key}
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for key, o in options.items()
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],
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placeholder="Suche nach Unternehmen oder Person",
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),
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],
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),
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],
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),
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],
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)
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def create_company_header(selected_company_name: str) -> html:
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"""Create company header based on selected company.
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Args:
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selected_company_name: The company name that has been chosen in the dropdown.
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Returns:
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The html div to create the company header.
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"""
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return html.Div(
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className="company-header",
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children=[
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html.H1(
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className="company-header-title",
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id="id-company-header-title",
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children=selected_company_name,
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),
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],
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)
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def create_company_stats(selected_company_data: pd.Series) -> html:
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"""Create company stats.
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Args:
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selected_company_data: A series containing all company information of the selected company.
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Returns:
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The html div to create the company stats table and the three small widgets.
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"""
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company_data = {
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"col1": ["Unternehmen", "Straße", "Stadt"],
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"col2": [
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selected_company_data["company_name"],
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selected_company_data["company_street"],
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str(
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selected_company_data["company_zip_code"]
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+ " "
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+ selected_company_data["company_city"]
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),
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],
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"col3": ["Branche", "Amtsgericht", "Gründungsjahr"],
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"col4": [
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selected_company_data["company_sector"],
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selected_company_data["district_court_name"],
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"xxx",
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],
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}
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df_company_data = pd.DataFrame(data=company_data)
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return html.Div(
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className="stats-wrapper",
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children=[
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html.Div(
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className="widget-large",
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children=[
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html.H3(
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className="widget-title",
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children="Stammdaten",
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),
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dash_table.DataTable(
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df_company_data.to_dict("records"),
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[{"name": i, "id": i} for i in df_company_data.columns],
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style_table={
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"width": "90%",
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"marginLeft": "auto",
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"marginRight": "auto",
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"paddingBottom": "20px",
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"color": COLORS["raisin-black"],
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},
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# hide header of table
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css=[
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{
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"selector": "tr:first-child",
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"rule": "display: none",
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},
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],
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style_cell={"textAlign": "center"},
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style_cell_conditional=[
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{"if": {"column_id": c}, "fontWeight": "bold"}
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for c in ["col1", "col3"]
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],
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style_data={
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"whiteSpace": "normal",
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"height": "auto",
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},
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),
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],
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),
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html.Div(
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className="widget-small",
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children=[
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html.H3(
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className="widget-title",
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children="Stimmung",
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),
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],
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),
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html.Div(
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className="widget-small",
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children=[
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html.H3(
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className="widget-title",
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children="Aktienkurs",
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),
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html.H1(
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className="widget-content",
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children="123",
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),
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],
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),
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html.Div(
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className="widget-small",
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children=[
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html.H3(
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className="widget-title",
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children="Umsatz",
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),
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html.H1(
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className="widget-content",
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children="1234",
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),
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],
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),
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],
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)
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def create_tabs(selected_company_id: int, selected_finance_df: pd.DataFrame) -> html:
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"""Create tabs for more company information.
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Args:
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selected_company_id: Id of the chosen company in the dropdown.
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selected_finance_df: A dataframe containing all available finance information of the companies.
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Returns:
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The html div to create the tabs of the company page.
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"""
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return html.Div(
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className="tabs",
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children=[
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dcc.Tabs(
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id="tabs",
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value="tab-1",
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children=[
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dcc.Tab(
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label="Kennzahlen",
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value="tab-1",
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className="tab-style",
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selected_className="selected-tab-style",
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children=[kennzahlen_layout(selected_finance_df)],
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),
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dcc.Tab(
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label="Beteiligte Personen",
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value="tab-2",
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className="tab-style",
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selected_className="selected-tab-style",
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),
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dcc.Tab(
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label="Stimmung",
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value="tab-3",
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className="tab-style",
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selected_className="selected-tab-style",
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),
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dcc.Tab(
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label="Verflechtungen",
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value="tab-4",
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className="tab-style",
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selected_className="selected-tab-style",
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children=[network_layout(selected_company_id)],
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),
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],
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),
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html.Div(id="tabs-example-content-1"),
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],
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)
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def kennzahlen_layout(selected_finance_df: pd.DataFrame) -> html:
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"""Create metrics tab.
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Args:
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selected_company_id: Id of the chosen company in the dropdown.
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selected_finance_df: A dataframe containing all available finance information of the companies.
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Returns:
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The html div to create the metrics tab of the company page.
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"""
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return html.Div(
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[
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dcc.Graph(
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figure=financials_figure(
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selected_finance_df, "annual_finance_statement_ebit"
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)
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)
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]
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)
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def financials_figure(selected_finance_df: pd.DataFrame, metric: str) -> go.Figure:
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"""Creates plotly line chart for a specific company and a metric.
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Args:
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selected_finance_df: A dataframe containing all finance information of the selected company.
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metric: The metric that should be visualized.
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Returns:
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A plotly figure showing the available metric data of the company.
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"""
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# create figure
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fig_line = go.Figure()
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# add trace for company 1
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fig_line.add_trace(
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go.Scatter(
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x=selected_finance_df["annual_finance_statement_date"],
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y=selected_finance_df[metric],
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line_color=COLORS["raisin-black"],
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marker_color=COLORS["raisin-black"],
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)
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)
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# set title and labels
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fig_line.update_layout(
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title=metric,
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xaxis_title="Jahr",
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yaxis_title="in Mio.€",
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plot_bgcolor=COLORS["light"],
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)
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return fig_line
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def network_layout(selected_company_id: int) -> html:
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"""Create network tab.
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Args:
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selected_company_id: Id of the chosen company in the dropdown.
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Returns:
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The html div to create the network tab of the company page.
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"""
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return networkx_component(selected_company_id)
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