Added Realtion_count MEthod

src/aki_prj23_transparenzregister/ui/archive/networkx_dash.py

@@ -34,16 +34,15 @@ def find_company_relations(company_id: int) -> pd.DataFrame:
     connected_company_name = []
 
     for _, row in companies_relations_df.iterrows():
-        # print(companies_df.loc[companies_df["company_id"] == row["relation_id"]]["company_name"].values[0])
         company_name.append(
             companies_df.loc[companies_df["company_id"] == row["relation_id"]][
                 "company_name"
-            ].values[0]
+            ].iloc[0]
         )
         connected_company_name.append(
             companies_df.loc[
                 companies_df["company_id"] == row["company_relation_company2_id"]
-            ]["company_name"].values[0]
+            ]["company_name"].iloc[0]
         )
 
     # print(company_name)
@@ -54,7 +53,7 @@ def find_company_relations(company_id: int) -> pd.DataFrame:
 
 
 # Plotly figure
-def networkGraph(company_id: int) -> go.Figure:
+def network_graph(company_id: int) -> go.Figure:
     """_summary_.
 
     Args:
@@ -63,14 +62,10 @@ def networkGraph(company_id: int) -> go.Figure:
     Returns:
         go.Figure: _description_
     """
-    # df = find_company_relations(test_company)
     edges = []
-    for index, row in find_company_relations(company_id).iterrows():
+    for _, 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)
-    # 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)
@@ -151,6 +146,6 @@ def networkx_component(company_id: int) -> html.Div:
     """
     return html.Div(
         [
-            dcc.Graph(id="my-graph", figure=networkGraph(company_id)),
+            dcc.Graph(id="my-graph", figure=network_graph(company_id)),
         ]
     )

src/aki_prj23_transparenzregister/ui/archive/networkx_dash_overall.py

@@ -34,18 +34,16 @@ def find_all_company_relations() -> pd.DataFrame:
     # 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]
+            ].iloc[0]
         )
 
         connected_company_name.append(
             companies_df.loc[
                 companies_df["company_id"] == row["company_relation_company2_id"]
-            ]["company_name"].values[0]
+            ]["company_name"].iloc[0]
         )
         # print(connected_company_name)
 
@@ -58,7 +56,7 @@ def find_all_company_relations() -> pd.DataFrame:
 
 
 # Plotly figure
-def networkGraph(EGDE_VAR: None) -> go.Figure:
+def network_graph() -> go.Figure:
     """Create a NetworkX Graph.
 
     Args:
@@ -67,15 +65,10 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     Returns:
         go.Figure: _description_
     """
-    # find_all_company_relations()
-
     edges = []
-    for index, row in find_all_company_relations().iterrows():
+    for _, 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)
@@ -141,7 +134,6 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
         },
     }
 
-    print(nx.eigenvector_centrality(network_graph))
     measure_vector = {}
     network_metrics_df = pd.DataFrame()
 
@@ -157,20 +149,11 @@ def networkGraph(EGDE_VAR: None) -> go.Figure:
     measure_vector = nx.closeness_centrality(network_graph)
     network_metrics_df["closeness"] = measure_vector.values()
 
-    # measure_vector = nx.pagerank(network_graph)
-    # network_metrics_df["pagerank"] = measure_vector.values()
-
-    # measure_vector = nx.average_degree_connectivity(network_graph)
-    # network_metrics_df["average_degree"] = measure_vector.values()
-    print(network_metrics_df)
-
     # figure
     return go.Figure(data=[edge_trace, node_trace], layout=layout)
 
 
 # Dash App
-
-
 app = Dash(__name__)
 
 app.title = "Dash Networkx"
@@ -192,7 +175,7 @@ app.layout = html.Div(
     # Input('metric-dropdown', 'value'),
     [Input("EGDE_VAR", "value")],
 )
-def update_output(EGDE_VAR: None) -> go.Figure:
+def update_output(edge_var: None) -> go.Figure:
     """Just Returns the go Figure of Plotly.
 
     Args:
@@ -201,7 +184,7 @@ def update_output(EGDE_VAR: None) -> go.Figure:
     Returns:
         go.Figure: _description_
     """
-    return networkGraph(EGDE_VAR)
+    return network_graph(edge_var)
 
 
 if __name__ == "__main__":

src/aki_prj23_transparenzregister/ui/archive/ui_elements.py

@@ -7,8 +7,8 @@ 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.archive.networkx_dash import networkx_component
+from aki_prj23_transparenzregister.utils.sql import entities
 
 COLORS = {
     "light": "#edefef",
@@ -363,6 +363,4 @@ def network_layout(selected_company_id: int) -> html:
     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}"])

src/aki_prj23_transparenzregister/ui/company_elements.py

@@ -15,6 +15,7 @@ from aki_prj23_transparenzregister.utils.networkx.network_base import initialize
 from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
     create_edge_and_node_list_for_company,
     find_company_relations,
+    get_relations_number_from_id,
 )
 
 COLORS = {
@@ -379,13 +380,37 @@ def network_layout(selected_company_id: int) -> html.Div:
         The html div to create the network tab of the company page.
     """
     person_relations, company_relations = find_company_relations(selected_company_id)
+
+    # get_all_metrics_from_id(selected_company_id)
+    get_relations_number_from_id(f"c_{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    
+    # Initialize the Network and receive the Graph and a DataFrame with Metrics
-    if nodes != {}: 
+    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, node_annotation=False, edge_thickness=1)
+        figure = create_2d_graph(
-    
+            graph,
-        return html.Div( children=[dcc.Graph(figure=figure, id="company-graph", className="graph-style")])
+            nodes,
-    return html.Div([html.H3(f"Leider gibt es keine Verbindungen vom Unternehmen mit ID: {selected_company_id}")])
+            edges,
+            metrics,
+            metric,
+            layout="Spring",
+            edge_annotation=True,
+            node_annotation=False,
+            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}"
+            )
+        ]
+    )

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -369,7 +369,7 @@ def update_graph_data(
     allow_duplicate=True,
 )
 # @lru_cache(20)
-def update_figure(
+def update_figure(  # noqa: PLR0913
     selected_metric: str,
     switch_value: bool,
     # switch_node_annotaion_value: bool,

src/aki_prj23_transparenzregister/utils/enum_types.py

@@ -11,38 +11,6 @@ class SentimentLabel(MultiValueEnum):
     NEGATIVE = -1, "negative"
     NEUTRAL = 0, "neutral"
 
-    @staticmethod
-    def get_string_from_enum(value: int | None) -> str:
-        """Translates relation name into a RelationTypeEnum.
-
-        If no translation can be found a warning is given.
-
-        Args:
-            relation_name: The name of the relation to be translated.
-
-        Returns:
-            The identified translation or None if no translation can be found.
-        """
-        tmp = RelationTypeEnum(value)
-        if value is None:
-            raise ValueError("A relation type needs to be given.")
-        name = {
-            RelationTypeEnum.GESCHAEFTSFUEHRER: "Geschäftsführer",
-            RelationTypeEnum.KOMMANDITIST: "Kommanditist",
-            RelationTypeEnum.VORSTAND: "Vorstand",
-            RelationTypeEnum.PROKURIST: "Prokurist",
-            RelationTypeEnum.LIQUIDATOR: "Liquidator",
-            RelationTypeEnum.INHABER: "Inhaber",
-            RelationTypeEnum.PERSOENLICH_HAFTENDER_GESELLSCHAFTER: "Persönlich haftender Gesellschafter",
-            RelationTypeEnum.ORGANISATION: "Organisation",
-            RelationTypeEnum.PARTNER: "Partner",
-            RelationTypeEnum.DIREKTOR: "Direktor",
-            RelationTypeEnum.RECHTSNACHFOLGER: "Rechtsnachfolger",
-        }.get(tmp)
-        if name is not None:
-            return name
-        raise ValueError(f'Relation type "{value}" is not yet implemented!')
-
 
 class FinancialKPIEnum(Enum):
     """Financial KPI keys."""

src/aki_prj23_transparenzregister/utils/networkx/network_3d.py

@@ -5,7 +5,7 @@ import pandas as pd
 import plotly.graph_objects as go
 
 
-def create_3d_graph(
+def create_3d_graph(  # noqa : PLR0913
     graph: nx.Graph,
     nodes: dict,
     edges: list,

src/aki_prj23_transparenzregister/utils/networkx/network_base.py

@@ -34,5 +34,66 @@ def initialize_network(edges: list, nodes: dict) -> tuple[nx.Graph, pd.DataFrame
     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", "eigenvector", "betweenness", "closeness", "pagerank"]
+    )
+    # 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

src/aki_prj23_transparenzregister/utils/networkx/networkx_data.py

@@ -1,8 +1,15 @@
 """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.config.config_providers import JsonFileConfigProvider
+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 connector, entities
 from aki_prj23_transparenzregister.utils.sql.connector import get_session
 
@@ -15,6 +22,9 @@ 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_.
@@ -40,12 +50,12 @@ def find_all_company_relations() -> pd.DataFrame:
         company_name.append(
             companies_df.loc[companies_df["company_id"] == row["relation_id"]][
                 "company_name"
-            ].values[0]
+            ].iloc[0]
         )
         connected_company_name.append(
             companies_df.loc[
                 companies_df["company_id"] == row["company_relation_company2_id"]
-            ]["company_name"].values[0]
+            ]["company_name"].iloc[0]
         )
 
     companies_relations_df["company_name"] = company_name
@@ -213,19 +223,16 @@ def create_edge_and_node_list(
     nodes: dict = {}
     edges: list = []
 
-    COLOR_COMPANY = "blue"
-    COLOR_PERSON = "red"
-
     # Iterate over person relations
-    for _index, row in person_relations.iterrows():
+    for _, row in person_relations.iterrows():
-        if node := nodes.get(row["id_company"]) is None:
+        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 node := nodes.get(row["id_person"]) is None:
+        if nodes.get(row["id_person"]) is None:
             nodes[row["id_person"]] = {
                 "id": row["id_person"],
                 "name": str(row["firstname"]) + " " + str(row["lastname"]),
@@ -241,15 +248,15 @@ def create_edge_and_node_list(
             }
         )
 
-    for _index, row in company_relations.iterrows():
+    for _, row in company_relations.iterrows():
-        if node := nodes.get(row["id_company_from"]) is None:
+        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 node := nodes.get(row["id_company_to"]) is None:
+        if nodes.get(row["id_company_to"]) is None:
             nodes[row["id_company_to"]] = {
                 "id": row["id_company_to"],
                 "name": row["name_company_to"],
@@ -361,7 +368,7 @@ def create_edge_and_node_list_for_company(
     return nodes, edges
 
 
-def get_all_metrics_from_id(company_id: int) -> pd.DataFrame:
+def get_all_metrics_from_id(company_id: int) -> pd.Series:
     """_summary_
 
     Args:
@@ -370,10 +377,20 @@ def get_all_metrics_from_id(company_id: int) -> pd.DataFrame:
     Returns:
         pd.DataFrame: _description_
     """
-    return pd.DataFrame()
+    # 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
+    )
+    return metrics.loc[metrics["id"] == company_id].iloc[0]
 
 
-def get_relations_number_from_id(company_id: int) -> tuple[int, int, int]:
+@lru_cache
+def get_relations_number_from_id(id: str) -> tuple[int, int, int]:
     """_summary_
 
     Args:
@@ -382,4 +399,29 @@ def get_relations_number_from_id(company_id: int) -> tuple[int, int, int]:
     Returns:
         tuple[int,int,int]: _description_
     """
-    return (1, 2, 3)
+    # 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))

src/aki_prj23_transparenzregister/utils/sql/connector.py

@@ -45,7 +45,7 @@ def get_engine(conn_args: SQLConnectionString) -> Engine:
         return sa.create_engine(
             str(conn_args),
             connect_args={"check_same_thread": True},
-            poolclass=SingletonThreadPool
+            poolclass=SingletonThreadPool,
         )
     raise TypeError("The type of the configuration is invalid.")
 

tests/ui/networkx_dash_test.py

@@ -2,6 +2,6 @@
 from aki_prj23_transparenzregister.ui import networkx_dash
 
 
-def networkGraph(Edges: None) -> None:
+def network_graph(Edges: None) -> None:
     """Checks if an import co company_stats_dash can be made."""
     assert networkx_dash is not None

tests/utils/networkx/network_2d_test.py

@@ -1,14 +1,11 @@
 """Test the initialize Network function."""
 import datetime
-from unittest import TestCase
 
 import networkx as nx
 import pandas as pd
 
 from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
 
-tc = TestCase()
-
 
 def test_initialize_network() -> None:
     edges: list = [
@@ -32,7 +29,7 @@ def test_initialize_network() -> None:
     graph, metrics = initialize_network(edges=edges, nodes=nodes)
     assert type(graph) is nx.Graph
     assert type(metrics) is pd.DataFrame
-    tc.assertListEqual(
+    assert (
-        list(metrics.columns),
+        list(metrics.columns)
-        ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        == ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
     )

tests/utils/networkx/network_base_test.py

@@ -1,16 +1,14 @@
 """Test the initialize Network function."""
 import datetime
-from unittest import TestCase
 
 import networkx as nx
 import pandas as pd
+import pytest
 
 from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
 
-tc = TestCase()
-import pytest
 
-@pytest.mark.tim
+@pytest.mark.tim()
 def test_initialize_network() -> None:
     edges: list = [
         {"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
@@ -33,7 +31,7 @@ def test_initialize_network() -> None:
     graph, metrics = initialize_network(edges=edges, nodes=nodes)
     assert isinstance(graph, nx.Graph)
     assert isinstance(metrics, pd.DataFrame)
-    tc.assertListEqual(
+    assert (
-        list(metrics.columns),
+        list(metrics.columns)
-        ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        == ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
     )