Integrated NetworkX graphs into App

src/aki_prj23_transparenzregister/ui/networkx_dash.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import networkx as nx
 import plotly.graph_objects as go
-from dash import Dash, Input, Output, dcc, html
+from dash import Dash, Input, Output, dcc, html, callback
 from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
 from aki_prj23_transparenzregister.utils.sql import connector, entities
 
@@ -32,10 +32,10 @@ def find_company_relations(company_id: int) -> pd.DataFrame:
     return companies_relations_df
 
 # Plotly figure
-def networkGraph(EGDE_VAR: None) -> go.Figure:
+def networkGraph(company_id: int) -> go.Figure:
     # df = find_company_relations(test_company)
     edges = []
-    for index, row in find_company_relations(test_company).iterrows():
+    for index, row in find_company_relations(company_id).iterrows():
         edges.append([row["company_name"], row["connected_company_name"]])
         # print(row["company_name"], row["connected_company_name"])
     # print(edges)
@@ -113,27 +113,36 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
 # Dash App
 
 
-app = Dash(__name__)
+# app = Dash(__name__)
 
-app.title = "Dash Networkx"
+# app.title = "Dash Networkx"
 
-app.layout = html.Div(
+# app.layout = html.Div(
+#     [
+#         html.I("Write your EDGE_VAR"),
+#         html.Br(),
+#         dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
+#         dcc.Graph(id="my-graph"),
+#     ]
+# )
+def networkx_component(company_id: int):
+    
+    layout = html.Div(
     [
-        html.I("Write your EDGE_VAR"),
+        
-        html.Br(),
+        dcc.Graph(id="my-graph", figure=networkGraph(company_id)),
-        dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
-        dcc.Graph(id="my-graph"),
     ]
-)
+    )
+    return layout
+
+# callback(
+#     Output("my-graph", "figure", allow_duplicate=True),
+#     [Input("EGDE_VAR", "value")],
+#     prevent_initial_call=True,
+# )
+# def update_output() -> None:
+#     return networkGraph()
 
 
-@app.callback(
+# if __name__ == "__main__":
-    Output("my-graph", "figure"),
+#     app.run(debug=True)
-    [Input("EGDE_VAR", "value")],
-)
-def update_output(EGDE_VAR: None) -> None:
-    return networkGraph(EGDE_VAR)
-
-
-if __name__ == "__main__":
-    app.run(debug=True)

src/aki_prj23_transparenzregister/ui/networkx_dash_overall.py

@@ -148,14 +148,16 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
 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"),
+        dcc.Graph(id="my-graph", style={'width': '49%'}),
     ]
 )
 

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -1,14 +1,13 @@
 """Content of home page."""
 import dash
-import networkx as nx
+from dash import html
 import pandas as pd
+import networkx as nx
 import plotly.graph_objects as go
-from dash import Input, Output, callback, html
+from dash import Dash, Input, Output, dcc, html, callback
+from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
+from aki_prj23_transparenzregister.utils.sql import connector, entities
 
-from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
-    find_all_company_relations,
-    find_top_companies,
-)
 
 dash.register_page(
     __name__,
@@ -21,7 +20,36 @@ dash.register_page(
         "/personendetails/",
     ],
 )
+def find_all_company_relations() -> pd.DataFrame:
+    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(): 
+        # print(companies_df.loc[companies_df["company_id"] == row["relation_id"]]["company_name"].values[0]) 
+        # print("TEst")    
+        company_name.append(companies_df.loc[companies_df["company_id"] == row["relation_id"]]["company_name"].values[0])
+        
+        connected_company_name.append(companies_df.loc[companies_df["company_id"] == row["company_relation_company2_id"]]["company_name"].values[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 networkGraph(EGDE_VAR: None) -> go.Figure:
@@ -30,6 +58,10 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     edges = []
     for index, row in find_all_company_relations().iterrows():
         edges.append([row["company_name"], row["connected_company_name"]])
+        # print(row["company_name"], row["connected_company_name"])
+        # print(edges)
+    # edges = df[["relation_id","company_relation_company2_id"]]
+    # edges = [[EGDE_VAR, "B"], ["B", "C"], ["B", "D"]]
     network_graph = nx.Graph()
     network_graph.add_edges_from(edges)
     pos = nx.spring_layout(network_graph)
@@ -99,6 +131,7 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     measure_vector = {}
     network_metrics_df = pd.DataFrame()
 
+    
     measure_vector = nx.eigenvector_centrality(network_graph)
     network_metrics_df["eigenvector"] = measure_vector.values()
 
@@ -122,42 +155,35 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     return go.Figure(data=[edge_trace, node_trace], layout=layout)
 
 
-df = find_top_companies()
+# Dash App
-with open("src/aki_prj23_transparenzregister/ui/assets/network_graph.html") as file:
-    html_content = file.read()
 
 
+# app = Dash(__name__)
+
+# app.title = "Dash Networkx"
+
 layout = html.Div(
+    
+    children = html.Div(
         children=[
-        # NOTE lib dir created by NetworkX has to be placed in assets
+            html.Div(
-        html.Iframe(
+                className="top_companytable_style",
-            src="assets/network_graph.html",
+                children=[
-            style={"height": "100vh", "width": "100vw"},
+                    html.I("Write your EDGE_VAR")
-            allow="*",
+                ]
+            ),
+            html.Div(
+                className="networkx_style",
+                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"),
+                    ]
             )
         ]
-    # children = html.Div(
+    )
-    #     children=[
-    #         html.Div(
-    #             className="top_companytable_style",
-    #             children=[
-    #                 html.Title(title="Top Ten Unternehmen", style={"align": "mid"}),
-    #                 dash_table.DataTable(df.to_dict('records'), [{"name": i, "id": i} for i in df.columns])
-    #             ]
-    #         ),
-    #         html.Div(
-    #             className="networkx_style",
-    #             children=[
-    #                 html.Header(title="Social Graph"),
-    #                 dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='demo-dropdown'),
-    #                 "Text",
-    #                 dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
-    #                 # dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='metric-dropdown'),
-    #                 dcc.Graph(id="my-graph"),
-    #                 ]
-    #         )
-    #     ]
-    # )
 )
 
 
@@ -167,5 +193,4 @@ layout = html.Div(
     [Input("EGDE_VAR", "value")],
 )
 def update_output(EGDE_VAR: None) -> None:
-    find_top_companies()
     return networkGraph(EGDE_VAR)

src/aki_prj23_transparenzregister/ui/ui_elements.py

@@ -0,0 +1,368 @@
+"""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.utils.sql import entities
+from aki_prj23_transparenzregister.ui.networkx_dash import networkx_component
+
+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.
+    """
+    selected_company_id
+    return networkx_component(selected_company_id)
+    # return html.Div([f"Netzwerk von Unternehmen mit ID: {selected_company_id}"])