solved errors

src/aki_prj23_transparenzregister/ui/pages/home.py

@@ -1,10 +1,11 @@
 """Content of home page."""
+from functools import lru_cache
 import dash
 import dash_daq as daq
 import networkx as nx
 import pandas as pd
 import plotly.graph_objects as go
-from dash import Input, Output, callback, dash_table, dcc, html
+from dash import Input, Output, callback, dash_table, dcc, html, ctx
 
 from aki_prj23_transparenzregister.utils.networkx.network_2d import (
     create_2d_graph,
@@ -157,53 +158,7 @@ layout = html.Div(
     )
 )
 
-
+# @lru_cache(200)
-@callback(
-    Output("my-graph", "figure"),
-    [
-        Input("dropdown", "value"),
-        Input("switch", "on"),
-        Input("dropdown_company_relation_filter", "value"),
-        Input("dropdown_person_relation_filter", "value"),
-    ],
-    prevent_initial_call=True,
-    allow_duplicate=True,
-)
-def update_figure(
-    selected_metric: str,
-    switch_value: bool,
-    c_relation_filter_value: str,
-    p_relation_filter_value: str,
-) -> go.Figure:
-    """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_
-        switch_value (bool): _description_
-        c_relation_filter_value (_type_): _description_
-        p_relation_filter_value (_type_): _description_
-
-    Returns:
-        Network Graph(Plotly Figure): Plotly Figure in 3 or 2D
-    """
-    _ = c_relation_filter_value, p_relation_filter_value
-
-    # find_top_companies()
-
-    metric = None if selected_metric == "None" else selected_metric
-
-    # if triggered_id == 'dropdown_companyrelation_filter' or triggered_id == 'dropdown_personrelation_filter':
-    #     print(selected_value)
-    #     print(metrics)
-    #     print(graph)
-    #     graph, metrics = update_graph_data(person_relation_type= p_relation_filter, company_relation_type= c_relation_filter)
-
-    if switch_value:
-        return create_2d_graph(graph, nodes, edges, metrics, metric)
-    else:
-        return create_3d_graph(graph, nodes, edges, metrics, metric)
-
-
 def update_graph_data(
     person_relation_type: str = "HAFTENDER_GESELLSCHAFTER",
     company_relation_type: str = "GESCHAEFTSFUEHRER",
@@ -222,3 +177,50 @@ def update_graph_data(
 
     graph, metrics = initialize_network(nodes=nodes, edges=edges)
     return graph, metrics
+
+
+@callback(
+    Output("my-graph", "figure"),
+    [
+        Input("dropdown", "value"),
+        Input("switch", "on"),
+        Input("dropdown_company_relation_filter", "value"),
+        Input("dropdown_person_relation_filter", "value"),
+    ],
+    prevent_initial_call=True,
+    allow_duplicate=True,
+)
+# @lru_cache(20)
+def update_figure(
+    selected_metric: str,
+    switch_value: bool,
+    c_relation_filter_value: str,
+    p_relation_filter_value: str,
+) -> go.Figure:
+    """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_
+        switch_value (bool): _description_
+        c_relation_filter_value (_type_): _description_
+        p_relation_filter_value (_type_): _description_
+
+    Returns:
+        Network Graph(Plotly Figure): Plotly Figure in 3 or 2D
+    """
+    _ = c_relation_filter_value, p_relation_filter_value
+    # triggered_id = ctx.triggered_id
+
+    # if triggered_id == 'dropdown_companyrelation_filter' or triggered_id == 'dropdown_personrelation_filter':
+        # print(selected_value)
+        # print(metrics)
+        # print(graph)
+    graph, metrics = update_graph_data(person_relation_type= p_relation_filter_value, company_relation_type= c_relation_filter_value)
+
+    if switch_value:
+        return create_2d_graph(graph, nodes, edges, metrics, selected_metric)
+    else:
+        return create_3d_graph(graph, nodes, edges, metrics, selected_metric)
+
+
+

src/aki_prj23_transparenzregister/utils/networkx/network_2d.py

@@ -1,5 +1,6 @@
 """This Module contains the Method to create a 2D Network Graph with NetworkX."""
 import networkx as nx
+import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -107,8 +108,8 @@ def create_2d_graph(
     node_trace.text = node_names
 
     # Highlight the Node Size in regards to the selected Metric.
-    if metric is not None:
+    if metric != "None":
-        node_trace.marker.size = list(metrics[metric] * 500)
+        node_trace.marker.size = list(np.sqrt(metrics[metric]) * 200)
 
     # Add Relation_Type as a Descriptin for the edges.
     edge_type_list = []

src/aki_prj23_transparenzregister/utils/networkx/network_3d.py

@@ -1,5 +1,6 @@
 """This Module contains the Method to create a 3D Network Graph with NetworkX."""
 import networkx as nx
+import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 
@@ -129,8 +130,8 @@ def create_3d_graph(
     node_trace.text = node_names
 
     # Highlight the Node Size in regards to the selected Metric.
-    if metric is not None:
+    if metric != "None":
-        node_trace.marker.size = list(metrics[metric] * 500)
+        node_trace.marker.size = list(np.cbrt(metrics[metric]) * 200)
 
     # Set the Color and width of Edges for better highlighting.
     edge_colors = []

src/aki_prj23_transparenzregister/utils/networkx/networkx_data.py

@@ -97,7 +97,7 @@ def get_all_company_relations() -> pd.DataFrame:
         )
     )
     str(relations_company_query)
-    company_relations = pd.read_sql_query(str(relations_company_query), session.bind)
+    company_relations = pd.read_sql_query(str(relations_company_query), session.bind) # type: ignore
 
     company_relations["id_company_from"] = company_relations["id_company_from"].apply(
         lambda x: f"c_{x}"
@@ -134,7 +134,7 @@ def get_all_person_relations() -> pd.DataFrame:
             entities.PersonRelation.person_id == entities.Person.id,
         )
     )
-    person_relations = pd.read_sql_query(str(relations_person_query), session.bind)
+    person_relations = pd.read_sql_query(str(relations_person_query), session.bind) # type: ignore
 
     person_relations["id_company"] = person_relations["id_company"].apply(
         lambda x: f"c_{x}"
@@ -267,9 +267,6 @@ def create_edge_and_node_list(
 def find_company_relations(
     selected_company_id: int,
 ) -> tuple[pd.DataFrame, pd.DataFrame()]:
-    print("In Method:" + str(selected_company_id))
-    selected_company_id = 312
-    # print( entities.Company.id == selected_company_id)
     relations_company_query = (
         session.query(
             to_company.id.label("id_company_to"),
@@ -286,9 +283,10 @@ def find_company_relations(
             from_company,
             entities.CompanyRelation.company2_id == from_company.id,
         )
-        .where(to_company.id == selected_company_id)
+        .filter((from_company.id == selected_company_id) | (to_company.id == selected_company_id))
     )
-    logger.debug(str(relations_company_query))
+    company_relations = pd.DataFrame(relations_company_query.all())
+    # logger.debug(str(relations_company_query))
     # relations_company_query = relations_company_query.filter(entities.CompanyRelation.company2_id == selected_company_id) # or (entities.CompanyRelation.company2_id == selected_company_id))
     # logger.debug(str(relations_company_query))
     # tmp = str(relations_company_query) + " WHERE from_company.id = " + str(selected_company_id)
@@ -306,10 +304,6 @@ def find_company_relations(
             "id_company_from",
         ],
     )
-    logger.debug(str(relations_company_query))
-    print("query ergebnis:")
-
-    print(company_relations)
     
     company_relations["id_company_from"] = company_relations["id_company_from"].apply(
         lambda x: f"c_{x}"

src/aki_prj23_transparenzregister/utils/sql/connector.py

@@ -40,13 +40,12 @@ def get_engine(conn_args: SQLConnectionString) -> Engine:
             database=conn_args.database,
             port=conn_args.port,
         )
-        return sa.create_engine(url, echo=True)
+        return sa.create_engine(url)
     if isinstance(conn_args, SQLiteConnectionString):
         return sa.create_engine(
             str(conn_args),
             connect_args={"check_same_thread": True},
-            poolclass=SingletonThreadPool,
+            poolclass=SingletonThreadPool
-            echo=True,
         )
     raise TypeError("The type of the configuration is invalid.")
 

tests/utils/networkx/network_base_test.py

@@ -8,8 +8,9 @@ import pandas as pd
 from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
 
 tc = TestCase()
+import pytest
 
-
+@pytest.mark.tim
 def test_initialize_network() -> None:
     edges: list = [
         {"from": "p_545", "to": "c_53", "type": "HAFTENDER_GESELLSCHAFTER"},
@@ -30,8 +31,8 @@ def test_initialize_network() -> None:
         },
     }
     graph, metrics = initialize_network(edges=edges, nodes=nodes)
-    assert type(graph) is nx.Graph
+    assert isinstance(graph, nx.Graph)
-    assert type(metrics) is pd.DataFrame
+    assert isinstance(metrics, pd.DataFrame)
     tc.assertListEqual(
         list(metrics.columns),
         ["degree", "eigenvector", "betweeness", "closeness", "pagerank"],