mirror of
https://github.com/fhswf/aki_prj23_transparenzregister.git
synced 2025-05-14 10:28:46 +02:00
Added 2d and 3d network to dash
This commit is contained in:
Tim
parent
e45f3a3b98
commit
bcb6df8e5d
@ -1,18 +1,20 @@
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.networkx_style {
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float: right;
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margin-top: 20px;
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margin-left: 20px;
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margin-left: 10px;
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margin-right: 20px;
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border: 1px solid;
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border-color: blue;
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width: 45%;
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height: 500px;
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width: 57%;
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height: 100%;
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}
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.top_companytable_style {
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float: left;
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margin-top: 20px;
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margin-right: 20px;
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margin-left: 20px;
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margin-right: 10px;
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border: 1px solid;
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width: 45%;
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width: 37%;
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height: 100%;
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}
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@ -3,14 +3,32 @@ import dash
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import networkx as nx
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import pandas as pd
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import plotly.graph_objects as go
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from dash import Input, Output, callback, html
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from dash import Input, Output, callback, html, dcc, dash_table, ctx
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import dash_daq as daq
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from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
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find_all_company_relations,
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find_top_companies,
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get_all_person_relations,
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get_all_company_relations,
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filter_relation_type,
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filter_relation_with_more_than_one_connection,
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create_edge_and_node_list
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)
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from aki_prj23_transparenzregister.utils.networkx.network_3d import (
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initialize_network,
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create_3d_graph,
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)
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from aki_prj23_transparenzregister.utils.networkx.network_2d import (
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create_2d_graph,
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)
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# Get Data
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person_relation = filter_relation_type(get_all_person_relations(), "HAFTENDER_GESELLSCHAFTER")
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company_relation = filter_relation_with_more_than_one_connection(get_all_company_relations(), "id_company_to", "id_company_from")
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dash.register_page(
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__name__,
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@ -29,99 +47,14 @@ dash.register_page(
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def networkGraph(EGDE_VAR: None) -> go.Figure:
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# find_all_company_relations()
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edges = []
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for index, row in find_all_company_relations().iterrows():
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edges.append([row["company_name"], row["connected_company_name"]])
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network_graph = nx.Graph()
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network_graph.add_edges_from(edges)
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pos = nx.spring_layout(network_graph)
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graph, metrices = initialize_network(nodes = nodes, edges = edges)
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# print(graph)
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metric = None
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network = create_3d_graph(graph, nodes, edges, metrices, metric)
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# edges trace
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edge_x = []
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edge_y = []
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for edge in network_graph.edges():
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x0, y0 = pos[edge[0]]
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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_y.append(y0)
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edge_y.append(y1)
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edge_y.append(None)
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edge_trace = go.Scatter(
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x=edge_x,
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y=edge_y,
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line={"color": "black", "width": 1},
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hoverinfo="none",
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showlegend=False,
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mode="lines",
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)
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# nodes trace
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node_x = []
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node_y = []
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text = []
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for node in network_graph.nodes():
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x, y = pos[node]
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node_x.append(x)
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node_y.append(y)
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text.append(node)
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node_trace = go.Scatter(
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x=node_x,
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y=node_y,
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text=text,
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mode="markers+text",
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showlegend=False,
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hoverinfo="none",
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marker={"color": "pink", "size": 50, "line": {"color": "black", "width": 1}},
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)
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# layout
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layout = {
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"plot_bgcolor": "white",
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"paper_bgcolor": "white",
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"margin": {"t": 10, "b": 10, "l": 10, "r": 10, "pad": 0},
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"xaxis": {
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"linecolor": "black",
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"showgrid": False,
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"showticklabels": False,
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"mirror": True,
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},
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"yaxis": {
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"linecolor": "black",
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"showgrid": False,
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"showticklabels": False,
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"mirror": True,
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},
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}
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print(nx.eigenvector_centrality(network_graph))
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measure_vector = {}
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network_metrics_df = pd.DataFrame()
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measure_vector = nx.eigenvector_centrality(network_graph)
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network_metrics_df["eigenvector"] = measure_vector.values()
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measure_vector = nx.degree_centrality(network_graph)
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network_metrics_df["degree"] = measure_vector.values()
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measure_vector = nx.betweenness_centrality(network_graph)
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network_metrics_df["betweeness"] = measure_vector.values()
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measure_vector = nx.closeness_centrality(network_graph)
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network_metrics_df["closeness"] = measure_vector.values()
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# measure_vector = nx.pagerank(network_graph)
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# network_metrics_df["pagerank"] = measure_vector.values()
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# measure_vector = nx.average_degree_connectivity(network_graph)
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# network_metrics_df["average_degree"] = measure_vector.values()
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print(network_metrics_df)
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# figure
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return go.Figure(data=[edge_trace, node_trace], layout=layout)
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company_relation_type_filter = get_all_person_relations()["relation_type"].unique()
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print(company_relation_type_filter)
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person_relation_type_filter = get_all_company_relations()["relation_type"].unique()
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df = find_top_companies()
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@ -130,44 +63,122 @@ with open("src/aki_prj23_transparenzregister/ui/assets/network_graph.html") as f
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layout = html.Div(
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children=[
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# NOTE lib dir created by NetworkX has to be placed in assets
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html.Iframe(
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src="assets/network_graph.html",
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style={"height": "100vh", "width": "100vw"},
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allow="*",
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)
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]
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# children = html.Div(
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# children=[
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# html.Div(
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# className="top_companytable_style",
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# children=[
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# html.Title(title="Top Ten Unternehmen", style={"align": "mid"}),
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# dash_table.DataTable(df.to_dict('records'), [{"name": i, "id": i} for i in df.columns])
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# ]
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# ),
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# html.Div(
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# className="networkx_style",
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# children=[
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# html.Header(title="Social Graph"),
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# dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='demo-dropdown'),
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# "Text",
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# dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
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# # dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='metric-dropdown'),
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# dcc.Graph(id="my-graph"),
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# ]
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# )
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# ]
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# )
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# children=[
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# # NOTE lib dir created by NetworkX has to be placed in assets
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# # html.Iframe(
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# # src="assets/network_graph.html",
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# # style={"height": "100vh", "width": "100vw"},
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# # allow="*",
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# # )
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# ]
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children = html.Div(
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children=[
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html.Div(
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className="top_companytable_style",
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children=[
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html.Title(title="Top Ten Unternehmen", style={"align": "mid"}),
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dash_table.DataTable(df.to_dict('records'), [{"name": i, "id": i} for i in df.columns])
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]
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),
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html.Div(
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className="networkx_style",
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children=[
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html.Header(title="Social Graph"),
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"Company Relation Type Filter:",
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dcc.Dropdown(company_relation_type_filter, company_relation_type_filter[0], id='dropdown_companyrelation_filter'),
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"Person Relation Type Filter:",
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dcc.Dropdown(person_relation_type_filter, person_relation_type_filter[0], id='dropdown_personrelation_filter'),
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"Choose Graph Metric:",
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dcc.Dropdown(['None','eigenvector', 'degree', 'betweeness', 'closeness'], 'None', id='dropdown'),
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# "Text",
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# dcc.Input(id="EGDE_VAR", type="text", value="K", debounce=True),
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daq.BooleanSwitch(id='switch', on=False),
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# html.Div(id='switch'),
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# dcc.Dropdown(['eigenvector', 'degree', 'betweeness', 'closeness'], 'eigenvector', id='metric-dropdown'),
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# dcc.Graph(id="my-graph"),
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dcc.Graph(figure = network, id='my-graph'),
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# html.Div(id='my-graph'),
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]
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)
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]
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)
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)
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@callback(
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Output("my-graph", "figure"),
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# Input('metric-dropdown', 'value'),
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[Input("EGDE_VAR", "value")],
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[Input("dropdown", "value"),
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Input("switch", "on"),
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Input("dropdown_companyrelation_filter", "value"),
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Input("dropdown_personrelation_filter", "value")],
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prevent_initial_call=True,
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allow_duplicate=True
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)
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def update_output(EGDE_VAR: None) -> None:
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def update_figure(selected_value, on, c_relation_filter, p_relation_filter):
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triggered_id = ctx.triggered_id
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find_top_companies()
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return networkGraph(EGDE_VAR)
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if selected_value == "None":
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metric = None
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else:
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metric = selected_value
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if triggered_id == 'switch':
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if on:
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return update_mode(on, selected_value)
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else:
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return create_3d_graph(graph, nodes, edges, metrices, metric)
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elif triggered_id == 'dropdown':
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if on:
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return update_mode(on, selected_value)
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else:
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return create_3d_graph(graph, nodes, edges, metrices, metric)
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# print(c_relation_filter)
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# print(p_relation_filter)
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# if triggered_id == 'dropdown_companyrelation_filter' or triggered_id == 'dropdown_personrelation_filter':
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# print("Hallo")
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# print(selected_value)
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# graph, metrices = update_graph_data(person_relation_type= p_relation_filter, company_relation_type= c_relation_filter)
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# if on:
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# return update_mode(on, selected_value)
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# else:
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# return create_3d_graph(graph, nodes, edges, metrices, metric)
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# print(metrices)
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# print(graph)
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def update_mode(value, metric):
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if metric == "None":
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metric = None
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if value == True:
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return create_2d_graph(graph, nodes, edges, metrices, metric)
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def update_graph_data(person_relation_type = "HAFTENDER_GESELLSCHAFTER", company_relation_type = "GESCHAEFTSFUEHRER"):
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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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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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print(company_relation)
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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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#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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# print(edges)
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graph, metrices = initialize_network(nodes = nodes, edges = edges)
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return graph, metrices
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128
src/aki_prj23_transparenzregister/utils/networkx/network_2d.py
Normal file
128
src/aki_prj23_transparenzregister/utils/networkx/network_2d.py
Normal file
@ -0,0 +1,128 @@
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import networkx as nx
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import pandas as pd
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import plotly.graph_objects as go
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def initialize_network(edges: list, nodes: list):
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# create edges from dataframe
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df_edges = pd.DataFrame(edges)
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graph = nx.from_pandas_edgelist(df_edges, source="from", target="to", edge_attr="type")
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# update node attributes from dataframe
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nx.set_node_attributes(graph, nodes)
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metrices = pd.DataFrame(columns=["degree", "eigenvector", "betweeness", "closeness", "pagerank"])
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metrices["eigenvector"] = nx.eigenvector_centrality(graph).values()
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metrices["degree"] = nx.degree_centrality(graph).values()
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metrices["betweeness"] = nx.betweenness_centrality(graph).values()
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metrices["closeness"] = nx.closeness_centrality(graph).values()
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metrices["pagerank"] = nx.pagerank(graph).values()
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return graph, metrices
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def create_2d_graph(graph, nodes, edges,metrices, metric):
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edge_x = []
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edge_y = []
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pos = nx.spring_layout(graph)
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edge_weight_x = []
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edge_weight_y = []
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G = graph
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for edge in G.edges():
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x0, y0 = pos[edge[0]]
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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_y.append(y0)
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edge_y.append(y1)
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edge_y.append(None)
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# edge_weight_x.append(x1 + x1 - x0)
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# edge_weight_y.append(y1 + y1 - y0)
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# edge_weight_x.append(x0 + x0 - x1)
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# edge_weight_y.append(y0 + y0 - y1)
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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_trace = go.Scatter(
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x=edge_x, y=edge_y,
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line=dict(width=0.5, color='#888'),
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hoverinfo='none',
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mode='lines')
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edge_weights_trace = go.Scatter(x=edge_weight_x,y= edge_weight_y, 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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node_x = []
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node_y = []
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for node in G.nodes():
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x, y = pos[node]
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node_x.append(x)
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node_y.append(y)
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node_trace = go.Scatter(
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x=node_x, y=node_y,
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mode='markers',
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hoverinfo='text',
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marker=dict(
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showscale=True,
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colorscale='YlGnBu',
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reversescale=True,
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color=[],
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size=10,
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colorbar=dict(
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thickness=15,
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title='Node Connections',
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xanchor='left',
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titleside='right'
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),
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line_width=2))
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#Set Color by using the nodes DataFrame with its Color Attribute. The sequence matters!
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colors = list(nx.get_node_attributes(graph, "color").values())
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node_names = []
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for key, value in nodes.items():
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if 'name' in value.keys():
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node_names.append(value["name"])
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else:
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node_names.append(value["firstname"] + " " + value["lastname"])
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node_trace.marker.color = colors
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node_trace.text = node_names
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if metric != None:
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node_trace.marker.size = list(metrices[metric]*500)
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# print(list(metrices[metric]*500))
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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 go.Figure(data=[edge_trace, edge_weights_trace, node_trace],
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layout=go.Layout(
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title='<br>Network graph made with Python',
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titlefont_size=16,
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showlegend=False,
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hovermode='closest',
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margin=dict(b=20,l=5,r=5,t=40),
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annotations=[ dict(
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text="Python code: <a href='https://plotly.com/ipython-notebooks/network-graphs/'> https://plotly.com/ipython-notebooks/network-graphs/</a>",
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showarrow=False,
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xref="paper", yref="paper",
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x=0.005, y=-0.002 ) ],
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xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
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)
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142
src/aki_prj23_transparenzregister/utils/networkx/network_3d.py
Normal file
142
src/aki_prj23_transparenzregister/utils/networkx/network_3d.py
Normal file
@ -0,0 +1,142 @@
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import networkx as nx
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import pandas as pd
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import plotly.graph_objects as go
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def initialize_network(edges: list, nodes: list):
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# create edges from dataframe
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df_edges = pd.DataFrame(edges)
|
||||
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)
|
||||
|
||||
metrices = pd.DataFrame(columns=["degree", "eigenvector", "betweeness", "closeness", "pagerank"])
|
||||
|
||||
metrices["eigenvector"] = nx.eigenvector_centrality(graph).values()
|
||||
metrices["degree"] = nx.degree_centrality(graph).values()
|
||||
metrices["betweeness"] = nx.betweenness_centrality(graph).values()
|
||||
metrices["closeness"] = nx.closeness_centrality(graph).values()
|
||||
metrices["pagerank"] = nx.pagerank(graph).values()
|
||||
|
||||
return graph, metrices
|
||||
|
||||
def create_3d_graph(graph, nodes, edges,metrices, metric):
|
||||
edge_x = []
|
||||
edge_y = []
|
||||
edge_z = []
|
||||
|
||||
# 3d spring layout
|
||||
pos = nx.spring_layout(graph, dim=3, seed=779)
|
||||
|
||||
for edge in graph.edges():
|
||||
x0, y0, z0 = pos[edge[0]]
|
||||
x1, y1, z1 = pos[edge[1]]
|
||||
|
||||
edge_x.append(x0)
|
||||
edge_x.append(x1)
|
||||
|
||||
edge_y.append(y0)
|
||||
edge_y.append(y1)
|
||||
|
||||
edge_z.append(z0)
|
||||
edge_z.append(z1)
|
||||
|
||||
edge_trace=go.Scatter3d(x=edge_x,
|
||||
y=edge_y,
|
||||
z=edge_z,
|
||||
mode='lines',
|
||||
line=dict(color='rgb(125,125,125)', width=1),
|
||||
hoverinfo='none'
|
||||
)
|
||||
|
||||
node_x = []
|
||||
node_y = []
|
||||
node_z = []
|
||||
|
||||
for node in graph.nodes():
|
||||
x, y, z = pos[node]
|
||||
node_x.append(x)
|
||||
node_y.append(y)
|
||||
node_z.append(z)
|
||||
|
||||
node_trace=go.Scatter3d(x=node_x,
|
||||
y=node_y,
|
||||
z=node_z,
|
||||
mode='markers',
|
||||
name='actors',
|
||||
marker=dict(symbol='circle',
|
||||
size=6,
|
||||
color="blue",
|
||||
colorscale='Viridis',
|
||||
line=dict(color='rgb(50,50,50)', width=0.5)
|
||||
),
|
||||
# text=labels,
|
||||
hoverinfo='text'
|
||||
)
|
||||
|
||||
axis=dict(showbackground=False,
|
||||
showline=False,
|
||||
zeroline=False,
|
||||
showgrid=False,
|
||||
showticklabels=False,
|
||||
title=''
|
||||
)
|
||||
|
||||
layout = go.Layout(
|
||||
title="Social Graph",
|
||||
|
||||
showlegend=False,
|
||||
scene=dict(
|
||||
xaxis=dict(axis),
|
||||
yaxis=dict(axis),
|
||||
zaxis=dict(axis),
|
||||
),
|
||||
margin=dict(
|
||||
t=10
|
||||
),
|
||||
hovermode='closest',
|
||||
annotations=[
|
||||
dict(
|
||||
showarrow=False,
|
||||
text="Companies (Blue) & Person (Red) Relation",
|
||||
xref='paper',
|
||||
yref='paper',
|
||||
x=0,
|
||||
y=0.1,
|
||||
xanchor='left',
|
||||
yanchor='bottom',
|
||||
font=dict(
|
||||
size=14
|
||||
)
|
||||
)
|
||||
], )
|
||||
|
||||
#Set Color by using the nodes DataFrame with its Color Attribute. The sequence matters!
|
||||
colors = list(nx.get_node_attributes(graph, "color").values())
|
||||
|
||||
node_names = []
|
||||
for key, value in nodes.items():
|
||||
|
||||
if 'name' in value.keys():
|
||||
node_names.append(value["name"])
|
||||
else:
|
||||
node_names.append(value["firstname"] + " " + value["lastname"])
|
||||
|
||||
node_trace.marker.color = colors
|
||||
node_trace.text = node_names
|
||||
|
||||
if metric != None:
|
||||
node_trace.marker.size = list(metrices[metric]*500)
|
||||
print("Test")
|
||||
|
||||
edge_colors = []
|
||||
for row in edges:
|
||||
if row["type"] == "HAFTENDER_GESELLSCHAFTER":
|
||||
edge_colors.append('rgb(255,0,0)')
|
||||
else:
|
||||
edge_colors.append('rgb(255,105,180)')
|
||||
edge_trace.line = dict(color=edge_colors, width=2)
|
||||
|
||||
|
||||
data=[edge_trace, node_trace]
|
||||
return go.Figure(data=data, layout=layout)
|
||||
@ -1,6 +1,18 @@
|
||||
from aki_prj23_transparenzregister.utils.sql import connector, entities
|
||||
from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
|
||||
import pandas as pd
|
||||
from sqlalchemy.orm import aliased
|
||||
import pandas as pd
|
||||
from sqlalchemy import func, text
|
||||
from aki_prj23_transparenzregister.utils.sql.connector import get_session
|
||||
|
||||
session = get_session(JsonFileConfigProvider("secrets.json"))
|
||||
|
||||
# Alias for Company table for the base company
|
||||
to_company = aliased(entities.Company, name="to_company")
|
||||
|
||||
# Alias for Company table for the head company
|
||||
from_company = aliased(entities.Company, name="from_company")
|
||||
|
||||
def find_all_company_relations() -> pd.DataFrame:
|
||||
"""_summary_
|
||||
@ -44,5 +56,137 @@ def find_top_companies() -> pd.DataFrame:
|
||||
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)
|
||||
# print(companies_df)
|
||||
return companies_df
|
||||
|
||||
|
||||
|
||||
def get_all_company_relations():
|
||||
# Query to fetch relations between companies
|
||||
relations_company_query = (
|
||||
session.query(
|
||||
to_company.id.label("id_company_to"),
|
||||
to_company.name.label("name_company_to"),
|
||||
entities.CompanyRelation.relation.label("relation_type"),
|
||||
from_company.name.label("name_company_from"),
|
||||
from_company.id.label("id_company_from"),
|
||||
)
|
||||
.join(
|
||||
entities.CompanyRelation,
|
||||
entities.CompanyRelation.company_id == to_company.id,
|
||||
)
|
||||
.join(
|
||||
from_company,
|
||||
entities.CompanyRelation.company2_id == from_company.id,
|
||||
)
|
||||
)
|
||||
str(relations_company_query)
|
||||
company_relations = pd.read_sql_query(str(relations_company_query), session.bind)
|
||||
|
||||
company_relations['id_company_from'] = company_relations['id_company_from'].apply(lambda x: f"c_{x}")
|
||||
company_relations['id_company_to'] = company_relations['id_company_to'].apply(lambda x: f"c_{x}")
|
||||
|
||||
return company_relations
|
||||
|
||||
def get_all_person_relations():
|
||||
relations_person_query = (
|
||||
session.query(
|
||||
entities.Company.id.label("id_company"),
|
||||
entities.Company.name.label("name_company"),
|
||||
entities.PersonRelation.relation.label("relation_type"),
|
||||
entities.Person.id.label("id_person"),
|
||||
entities.Person.lastname.label("lastname"),
|
||||
entities.Person.firstname.label("firstname"),
|
||||
entities.Person.date_of_birth.label("date_of_birth"),
|
||||
)
|
||||
.join(
|
||||
entities.PersonRelation,
|
||||
entities.PersonRelation.company_id == entities.Company.id,
|
||||
)
|
||||
.join(
|
||||
entities.Person,
|
||||
entities.PersonRelation.person_id == entities.Person.id,
|
||||
)
|
||||
)
|
||||
person_relations = pd.read_sql_query(str(relations_person_query), session.bind)
|
||||
|
||||
person_relations['id_company'] = person_relations['id_company'].apply(lambda x: f"c_{x}")
|
||||
person_relations['id_person'] = person_relations['id_person'].apply(lambda x: f"p_{x}")
|
||||
|
||||
|
||||
return person_relations
|
||||
|
||||
|
||||
def filter_relation_type(df: pd.DataFrame, selected_relation_type):
|
||||
df = df.loc[df["relation_type"] == selected_relation_type]
|
||||
return df
|
||||
|
||||
|
||||
def filter_relation_with_more_than_one_connection(df: pd.DataFrame, id_column_name_to, id_column_name_from):
|
||||
# print(df.columns.values)
|
||||
tmp_df = pd.DataFrame(columns= df.columns.values)
|
||||
# print(tmp_df)
|
||||
for _index, row in df.iterrows():
|
||||
count = 0
|
||||
id = row[id_column_name_to]
|
||||
for _index_sub, row_sub in df.iterrows():
|
||||
if id == row_sub[id_column_name_to]:
|
||||
count = count + 1
|
||||
if id == row_sub[id_column_name_from]:
|
||||
count = count + 1
|
||||
if count > 1:
|
||||
break
|
||||
|
||||
if count > 1:
|
||||
# tmp_df = pd.concat([tmp_df, pd.DataFrame(row)])+
|
||||
tmp_df.loc[len(tmp_df)] = row
|
||||
# print(row)
|
||||
count = 0
|
||||
else:
|
||||
count = 0
|
||||
continue
|
||||
# print(tmp_df)
|
||||
count = 0
|
||||
return tmp_df
|
||||
|
||||
|
||||
def create_edge_and_node_list(person_relations: pd.DataFrame, company_relations:pd.DataFrame):
|
||||
nodes = {}
|
||||
edges = []
|
||||
|
||||
COLOR_COMPANY = "blue"
|
||||
COLOR_PERSON = "red"
|
||||
|
||||
# Iterate over person relations
|
||||
for _index, row in person_relations.iterrows():
|
||||
if node:= nodes.get(row['id_company']) is None:
|
||||
nodes[row['id_company']] = {
|
||||
"id": row['id_company'],
|
||||
'name': row['name_company'],
|
||||
'color': COLOR_COMPANY
|
||||
}
|
||||
if node:= nodes.get(row['id_person']) is None:
|
||||
nodes[row['id_person']] = {
|
||||
"id": row['id_person'],
|
||||
'firstname': row['firstname'],
|
||||
'lastname': row['lastname'],
|
||||
'date_of_birth': row['date_of_birth'],
|
||||
'color': COLOR_PERSON
|
||||
}
|
||||
edges.append({'from': row['id_person'], 'to': row['id_company'], 'type': row['relation_type']})
|
||||
|
||||
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:
|
||||
nodes[row['id_company_to']] = {
|
||||
"id": row['id_company_to'],
|
||||
'name': row['name_company_to'],
|
||||
'color': COLOR_COMPANY
|
||||
}
|
||||
edges.append({'from': row['id_company_from'], 'to': row['id_company_to'], 'type': row['relation_type']})
|
||||
return nodes, edges
|
||||
File diff suppressed because one or more lines are too long
Loading…
x
Reference in New Issue
Block a user