now ruff confirm

src/aki_prj23_transparenzregister/ui/archive/networkx_dash_overall.py

@@ -56,12 +56,9 @@ def find_all_company_relations() -> pd.DataFrame:
 
 
 # Plotly figure
-def network_graph() -> go.Figure:
+def create_network_graph() -> go.Figure:
     """Create a NetworkX Graph.
 
-    Args:
-        EGDE_VAR (None): _description_
-
     Returns:
         go.Figure: _description_
     """
@@ -175,16 +172,13 @@ app.layout = html.Div(
     # Input('metric-dropdown', 'value'),
     [Input("EGDE_VAR", "value")],
 )
-def update_output(edge_var: None) -> go.Figure:
+def update_output() -> go.Figure:
     """Just Returns the go Figure of Plotly.
 
-    Args:
-        EGDE_VAR (None): _description_
-
     Returns:
-        go.Figure: _description_
+        go.Figure: Returns a HTML Figure for Plotly.
     """
-    return network_graph(edge_var)
+    return create_network_graph()
 
 
 if __name__ == "__main__":

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -383,10 +383,15 @@ def update_figure(  # noqa: PLR0913
     """In this Callback the Value of the Dropdown is used to filter the Data. In Addition it takes the filter for the Graph metrics and creates a new graph, or switches between 3D and 2D.
 
     Args:
-        selected_metric (_type_): _description_
+        selected_metric (_type_): Selected Value
-        switch_value (bool): _description_
+        switch_value (bool): True if 2D, False if 3D
-        c_relation_filter_value (_type_): _description_
+        switch_edge_annotaion_value: True if Edge should have a description, Flase = No Descritpion
-        p_relation_filter_value (_type_): _description_
+        c_relation_filter_value (_type_): Variable with String value of Relation Type for Companies
+        p_relation_filter_value (_type_): Variable with String value of Relation Type for Persons
+        layout: String of the Layout Dropdown
+        metric_dropdown_value: String of the Metric Dropdown
+        slider_value: Sets the size of the Edge Connections
+
 
     Returns:
         Network Graph(Plotly Figure): Plotly Figure in 3 or 2D
@@ -415,21 +420,21 @@ def update_figure(  # noqa: PLR0913
                 slider_value,  # type: ignore
             ),
         )
-    else:
+
-        return (
+    return (
-            table_dict,
+        table_dict,
-            table_columns,
+        table_columns,
-            create_3d_graph(
+        create_3d_graph(
-                graph,
+            graph,
-                nodes,
+            nodes,
-                edges,
+            edges,
-                metrics,
+            metrics,
-                selected_metric,
+            selected_metric,
-                layout,
+            layout,
-                switch_edge_annotaion_value,
+            switch_edge_annotaion_value,
-                slider_value,  # type: ignore
+            slider_value,  # type: ignore
-            ),
+        ),
-        )
+    )
 
 
 @callback(
@@ -438,7 +443,7 @@ def update_figure(  # noqa: PLR0913
         Input("dropdown_data_soruce_filter", "value"),
     ],
 )
-def update_Dropdown(datasource_value: str) -> str:
+def update_dropdown(datasource_value: str) -> str:
     """_summary_.
 
     Args:

src/aki_prj23_transparenzregister/utils/networkx/network_2d.py

@@ -5,7 +5,7 @@ import pandas as pd
 import plotly.graph_objects as go
 
 
-def create_2d_graph(
+def create_2d_graph(  # noqa PLR0913
     graph: nx.Graph,
     nodes: dict,
     edges: list,
@@ -23,6 +23,9 @@ def create_2d_graph(
         edges (_type_): List of Edges
         metrics (_type_): DataFrame with the MEtrics
         metric (_type_): Selected Metric
+        edge_annotation: Enables the Description of Edges
+        edge_thickness: Int Value of the Edge thickness
+        layout: String which defines the Graph Layout
 
     Returns:
         _type_: Plotly Figure

src/aki_prj23_transparenzregister/utils/networkx/networkx_data.py

@@ -277,6 +277,14 @@ def create_edge_and_node_list(
 def find_company_relations(
     selected_company_id: int,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """Finds all Relations for the given Company id.
+
+    Args:
+        selected_company_id: Id of the Company which Relations should be returned.
+
+    Returns:
+        Two Dataframes
+    """
     relations_company_query = (
         session.query(
             to_company.id.label("id_company_to"),
@@ -343,16 +351,14 @@ def create_edge_and_node_list_for_company(
     nodes: dict = {}
     edges: list = []
 
-    COLOR_COMPANY = "blue"
+    for _, row in company_relations.iterrows():
-
+        if nodes.get(row["id_company_from"]) is None:
-    for _index, row in company_relations.iterrows():
-        if node := nodes.get(row["id_company_from"]) is None:
             nodes[row["id_company_from"]] = {
                 "id": row["id_company_from"],
                 "name": row["name_company_from"],
                 "color": COLOR_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"],
@@ -369,10 +375,10 @@ def create_edge_and_node_list_for_company(
 
 
 def get_all_metrics_from_id(company_id: int) -> pd.Series:
-    """_summary_
+    """Returns all Metric for the given ID.
 
     Args:
-        company_id (int): _description_
+        company_id (int): Id of the Company.
 
     Returns:
         pd.DataFrame: _description_
@@ -391,10 +397,10 @@ def get_all_metrics_from_id(company_id: int) -> pd.Series:
 
 @lru_cache
 def get_relations_number_from_id(id: str) -> tuple[int, int, int]:
-    """_summary_
+    """Returns all Relation in 1, 2 and 3 lvl of one Node.
 
     Args:
-        company_id (int): _description_
+        id (int): String of the Company or Person Id.
 
     Returns:
         tuple[int,int,int]: _description_

tests/ui/networkx_dash_test.py

@@ -1,7 +1,7 @@
 """Test for the NetworkX Component."""
-from aki_prj23_transparenzregister.ui import networkx_dash
+from aki_prj23_transparenzregister.ui.archive import networkx_dash
 
 
-def network_graph(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

@@ -29,7 +29,10 @@ 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
-    assert (
+    assert list(metrics.columns) == [
-        list(metrics.columns)
+        "degree",
-        == ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        "eigenvector",
-    )
+        "betweeness",
+        "closeness",
+        "pagerank",
+    ]

tests/utils/networkx/network_3d_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 +29,10 @@ 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",
-        ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        "eigenvector",
-    )
+        "betweeness",
+        "closeness",
+        "pagerank",
+    ]

tests/utils/networkx/network_base_test.py

@@ -31,7 +31,10 @@ def test_initialize_network() -> None:
     graph, metrics = initialize_network(edges=edges, nodes=nodes)
     assert isinstance(graph, nx.Graph)
     assert isinstance(metrics, pd.DataFrame)
-    assert (
+    assert list(metrics.columns) == [
-        list(metrics.columns)
+        "degree",
-        == ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        "eigenvector",
-    )
+        "betweeness",
+        "closeness",
+        "pagerank",
+    ]

tests/utils/networkx/networkx_data_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 +29,10 @@ 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",
-        ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],
+        "eigenvector",
-    )
+        "betweeness",
+        "closeness",
+        "pagerank",
+    ]