Extracted Data-Extraction Metrhods into separate Files

src/aki_prj23_transparenzregister/ui/networkx_dash.py

@@ -109,22 +109,6 @@ def networkGraph(company_id: int) -> go.Figure:
     # figure
     return go.Figure(data=[edge_trace, node_trace], layout=layout)
 
-
-# Dash App
-
-
-# app = Dash(__name__)
-
-# app.title = "Dash Networkx"
-
-# 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(
@@ -135,14 +119,3 @@ def networkx_component(company_id: int):
     )
     return layout
 
-# callback(
-#     Output("my-graph", "figure", allow_duplicate=True),
-#     [Input("EGDE_VAR", "value")],
-#     prevent_initial_call=True,
-# )
-# def update_output() -> None:
-#     return networkGraph()
-
-
-# if __name__ == "__main__":
-#     app.run(debug=True)

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -4,9 +4,10 @@ from dash import html
 import pandas as pd
 import networkx as nx
 import plotly.graph_objects as go
-from dash import Dash, Input, Output, dcc, html, callback
+from dash import Dash, Input, Output, dcc, html, callback, dash_table
 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_top_companies, find_all_company_relations
 
 dash.register_page(
     __name__,
@@ -20,37 +21,6 @@ dash.register_page(
     ],
 )
 
-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:
     # find_all_company_relations()
@@ -58,10 +28,6 @@ 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)
@@ -154,14 +120,7 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     # figure
     return go.Figure(data=[edge_trace, node_trace], layout=layout)
 
-
+df = find_top_companies()
-# Dash App
-
-
-# app = Dash(__name__)
-
-# app.title = "Dash Networkx"
-
 layout = html.Div(
     
     children = html.Div(
@@ -169,12 +128,14 @@ layout = html.Div(
             html.Div(
                 className="top_companytable_style",
                 children=[
-                    html.I("Write your EDGE_VAR")
+                    html.I("Write your EDGE_VAR"),
+                    dash_table.DataTable(df.to_dict('records'), [{"name": i, "id": i} for i in df.columns])
                 ]
             ),
             html.Div(
                 className="networkx_style",
                 children=[
+                    dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='demo-dropdown'),
                     html.I("Write your EDGE_VAR"),
                     html.Br(),
                     # dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='metric-dropdown'),
@@ -193,4 +154,5 @@ 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/utils/networkx/__init__.py

@@ -0,0 +1 @@
+"""Everything regarding data extraction for NetworkX Graphs."""

src/aki_prj23_transparenzregister/utils/networkx/networkx_data.py

@@ -0,0 +1,48 @@
+from aki_prj23_transparenzregister.utils.sql import connector, entities
+from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
+import pandas as pd
+
+def find_all_company_relations() -> pd.DataFrame:
+    """_summary_
+
+    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
+    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"].values[0])        
+        connected_company_name.append(companies_df.loc[companies_df["company_id"] == row["company_relation_company2_id"]]["company_name"].values[0])
+    
+    companies_relations_df["company_name"] = company_name
+    companies_relations_df["connected_company_name"] = connected_company_name
+    
+    return companies_relations_df
+
+def find_top_companies() -> pd.DataFrame:
+    """_summary_
+
+    Returns:
+        pd.DataFrame: _description_
+    """
+    session = connector.get_session(JsonFileConfigProvider("./secrets.json"))
+    query_companies = session.query(entities.Company) #.all()
+
+    companies_df: pd.DataFrame = pd.read_sql(str(query_companies), session.bind)  # type: ignore
+    companies_df = companies_df.head()
+    companies_df = companies_df[["company_name"]]
+    companies_df["Platzierung"] = [1,2,3,4,5]
+    companies_df["Umsatz M€"] = [1,2,3,4,5]
+    companies_df = companies_df[['Platzierung', 'company_name', 'Umsatz M€']]
+    print(companies_df)
+    return companies_df