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https://github.com/fhswf/aki_prj23_transparenzregister.git
synced 2025-05-13 13:18:46 +02:00
Bug fixes
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@ -11,7 +11,10 @@ from sqlalchemy.orm import Session
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from aki_prj23_transparenzregister.ui import data_elements, finance_elements
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from aki_prj23_transparenzregister.ui.networkx_dash import networkx_component
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from aki_prj23_transparenzregister.utils.networkx.network_2d import create_2d_graph
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from aki_prj23_transparenzregister.utils.networkx.network_base import initialize_network
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from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
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create_edge_and_node_list_for_company,
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find_company_relations,
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)
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@ -378,14 +381,17 @@ def network_layout(selected_company_id: int) -> html.Div:
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"""
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person_relations, company_relations = find_company_relations(selected_company_id)
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# Create Edge and Node List from data
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# nodes, edges = create_edge_and_node_list_for_company(company_relations)
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nodes, edges = create_edge_and_node_list_for_company(company_relations)
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# Initialize the Network and receive the Graph and a DataFrame with Metrics
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# print(nodes)
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# print(edges)
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# print(pd.DataFrame(edges))
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# graph, metrics = initialize_network(nodes=nodes, edges=edges)
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# metric = None
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# figure = create_2d_graph(graph, nodes, edges, metrics, metric)
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if nodes != {}:
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graph, metrics = initialize_network(nodes=nodes, edges=edges)
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metric = "None"
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figure = create_2d_graph(graph, nodes, edges, metrics, metric, layout="Spring", edge_annotation=True, node_annotation=False, edge_thickness=1)
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# selected_company_id
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# print(company_relations)
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return networkx_component(selected_company_id)
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# return html.Div([f"Netzwerk von Unternehmen mit ID: {selected_company_id}"])
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return html.Div( children=[dcc.Graph(figure=figure, id="company-graph", className="graph-style")])
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# return networkx_component(selected_company_id)
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return html.Div([html.H3(f"Leider gibt es keine Verbindungen vom Unternehmen mit ID: {selected_company_id}")])
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@ -66,7 +66,7 @@ graph, metrics = initialize_network(nodes=nodes, edges=edges)
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metric = "None"
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layout = "Spring"
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switch_node_annotaion_value = False
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switch_edge_annotaion_value = True
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switch_edge_annotaion_value = False
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egde_thickness = 1
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network = create_3d_graph(graph, nodes, edges, metrics, metric, layout, switch_node_annotaion_value, switch_edge_annotaion_value, egde_thickness)
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@ -119,6 +119,28 @@ layout = html.Div(
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html.H1(className="header", children=["Social Graph"]),
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html.Div(
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className="filter-wrapper",
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children=[
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html.Div(
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className="filter-wrapper-item",
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children=[
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html.H5(
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className="filter-description",
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children=["Data Source:"],
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),
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dcc.Dropdown(
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["Company Data only", "Person Data only", "Company & Person Data"],
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"Company Data only",
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id="dropdown_data_soruce_filter",
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className="dropdown_style",
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),
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],
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),
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],
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),
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html.Div(
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className="filter-wrapper",
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id= "company_dropdown",
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# style="display: inline;",
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children=[
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html.Div(
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className="filter-wrapper-item",
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@ -137,6 +159,7 @@ layout = html.Div(
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),
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html.Div(
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className="filter-wrapper-item",
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# style="display: None;",
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children=[
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html.H5(
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className="filter-description",
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@ -184,12 +207,12 @@ layout = html.Div(
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# "Bipartite",
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"Circular",
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"Kamada Kawai",
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"Planar",
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# "Planar",
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"Random",
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"Shell",
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"Spectral",
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"Spiral",
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"Multipartite"
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"Shell (only 2D)",
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# "Spectral",
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"Spiral (only 2D)",
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# "Multipartite"
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],
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"Spring",
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id="dropdown_layout",
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@ -292,19 +315,22 @@ def update_graph_data(
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# Get Data
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person_df = get_all_person_relations()
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company_df = get_all_company_relations()
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# print(company_df)
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person_relation = filter_relation_type(person_df, person_relation_type)
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company_relation = filter_relation_type(company_df, company_relation_type)
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# company_relation = filter_relation_with_more_than_one_connection(company_relation, "id_company_to", "id_company_from")
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# print(company_relation)
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# print(len(company_relation))
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# Create Edge and Node List from data
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nodes, edges = create_edge_and_node_list(person_relation, company_relation)
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# node_count = len(nodes)
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# edge_count = len(edges)
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nodes_tmp, edges_tmp = create_edge_and_node_list(person_relation, company_relation)
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node_count = len(nodes_tmp)
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edge_count = len(edges_tmp)
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# print(edges_tmp)
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graph, metrics = initialize_network(nodes=nodes, edges=edges)
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return graph, metrics
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graph, metrics = initialize_network(nodes=nodes_tmp, edges=edges_tmp)
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return graph, metrics, nodes_tmp, edges_tmp
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@callback(
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@ -351,7 +377,9 @@ def update_figure(
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# print(selected_value)
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# print(metrics)
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# print(graph)
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graph, metrics = update_graph_data(person_relation_type= p_relation_filter_value, company_relation_type= c_relation_filter_value)
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graph, metrics, nodes, edges = update_graph_data(person_relation_type= p_relation_filter_value, company_relation_type= c_relation_filter_value)
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node_count = len(nodes)
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edge_count = len(edges)
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if switch_value:
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@ -373,4 +401,22 @@ def update_table(metric_dropdown_value: str) -> dict:
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columns =[{"name": i, "id": i} for i in table_df.columns]
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# print(columns)
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return table_df.to_dict("records")
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return table_df.to_dict("records")
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@callback(
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Output("company_dropdown", "style"),
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[
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Input("dropdown_data_soruce_filter", "value"),
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],
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)
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def update_Dropdown(datasource_value: str) -> str:
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style = ""
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match datasource_value:
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case "Company Data only":
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style = "display: inline"
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case "Person Data only":
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style = "display: none"
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case "Company & Person Data":
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style = "display: inline"
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return style
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@ -35,11 +35,11 @@ def create_2d_graph(
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# pos = nx.planar_layout(graph)
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case "Random":
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pos = nx.random_layout(graph)
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case "Shell":
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case "Shell only 2D)":
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pos = nx.shell_layout(graph)
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# case "Spectral":
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# pos = nx.spectral_layout(graph)
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case "Spiral":
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case "Spiral only 2D)":
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pos = nx.spiral_layout(graph)
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# case "Multipartite":
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# pos = nx.multipartite_layout(graph)
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@ -60,15 +60,15 @@ def create_2d_graph(
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x1, y1 = pos[edge[1]]
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edge_x.append(x0)
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edge_x.append(x1)
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edge_x.append(None)
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# edge_x.append(None)
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edge_y.append(y0)
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edge_y.append(y1)
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edge_y.append(None)
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# edge_y.append(None)
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edge_weight_x.append(x0 + ((x1 - x0) / 2))
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edge_weight_y.append(y0 + ((y1 - y0) / 2))
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edge_weight_y.append(None)
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edge_weight_x.append(((x1 + x0) / 2))
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edge_weight_y.append(((y1 + y0) / 2))
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# edge_weight_y.append(None)
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# Add the Edges to the scatter plot according to their Positions.
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edge_trace = go.Scatter(
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x=edge_x,
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@ -82,10 +82,10 @@ def create_2d_graph(
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x=edge_weight_x,
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y=edge_weight_y,
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mode="text",
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marker_size=1,
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marker_size=0.5,
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text=[0.45, 0.7, 0.34],
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textposition="top center",
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hovertemplate="weight: %{text}<extra></extra>",
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hovertemplate="Relation: %{text}<extra></extra>",
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)
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# Getting the Positions from NetworkX and assign it to the variables.
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@ -139,6 +139,7 @@ def create_2d_graph(
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edge_type_list.append(row["type"])
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edge_weights_trace.text = edge_type_list
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# print(edge_type_list)
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if node_annotation:
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print("Test")
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@ -54,6 +54,11 @@ def create_3d_graph(
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node_y = []
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node_z = []
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# Initialize Position Variables for the Description Text of the edges.
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edge_weight_x = []
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edge_weight_y = []
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edge_weight_z = []
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# Getting the Positions from NetworkX and assign them to the variables.
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for edge in graph.edges():
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x0, y0, z0 = pos[edge[0]]
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@ -67,6 +72,11 @@ def create_3d_graph(
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edge_z.append(z0)
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edge_z.append(z1)
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# Calculate edge mid
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edge_weight_x.append((x0+x1)/2)
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edge_weight_y.append((y0+y1)/2)
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edge_weight_z.append((z0+z1)/2)
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# Add the Edges to the scatter plot according to their Positions.
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edge_trace = go.Scatter3d(
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@ -78,6 +88,18 @@ def create_3d_graph(
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hoverinfo="none",
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)
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# Add the Edgedescriptiontext to the scatter plot according to its Position.
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edge_weights_trace = go.Scatter3d(
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x=edge_weight_x,
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y=edge_weight_y,
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z=edge_weight_z,
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mode="text",
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marker_size=1,
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text=[0.45, 0.7, 0.34],
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textposition="top center",
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hovertemplate="weight: %{text}<extra></extra>",
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)
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# Getting the Positions from NetworkX and assign it to the variables.
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for node in graph.nodes():
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x, y, z = pos[node]
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@ -163,6 +185,14 @@ def create_3d_graph(
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edge_colors.append("rgb(255,105,180)")
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edge_trace.line = {"color": edge_colors, "width": edge_thickness}
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# Add Relation_Type as a Description for the edges.
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if edge_annotation:
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edge_type_list = []
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for row in edges:
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edge_type_list.append(row["type"])
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edge_weights_trace.text = edge_type_list
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# Return the Plotly Figure
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data = [edge_trace, node_trace]
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data = [edge_trace,edge_weights_trace, node_trace]
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return go.Figure(data=data, layout=layout)
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