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import networkx as nx
# import plotly.graph_objects as go
from pyvis.network import Network
from pathlib import Path
def create_network(data, include_types, type_to_position):
G = nx.DiGraph()
for record in data:
asset_type = record.get("asset_type")
parent_type = record.get("parent_type")
child = record.get("external_id")
parent = record.get("parent_external_id")
# Filter nodes based on allowed types.
add_child = include_types is None or asset_type in include_types
add_parent = include_types is None or parent_type in include_types
if add_child and child:
node_attrs = {"type": asset_type, "label": record.get("asset_name")}
if record.get("uri"):
node_attrs["uri"] = record.get("uri")
G.add_node(child, **node_attrs)
if parent and add_parent:
node_attrs = {"type": parent_type, "label": record.get("parent_name")}
if record.get("uri"):
node_attrs["uri"] = record.get("uri")
G.add_node(parent, **node_attrs)
if child and parent and add_child and add_parent:
G.add_edge(parent, child)
# Assign 'layer' attribute for each node based on its type.
for node in G.nodes():
node_type = G.nodes[node].get('type')
G.nodes[node]['layer'] = type_to_position.get(node_type, 1)
return G
# def visualize_network_plotly(data, include_types, type_to_position):
# """
# Build and return a Plotly figure of a network graph.
# Parameters:
# data (list): JSON array of records.
# include_types (set): Allowed asset types.
# type_to_position (dict): Mapping of asset types to numeric layer positions.
# Returns:
# go.Figure: The Plotly figure with the network visualization.
# """
# # Create network graph
# G = create_network(data, include_types, type_to_position)
# # Compute positions using a multipartite layout (vertical alignment)
# pos = nx.multipartite_layout(G, subset_key="layer", align="vertical")
# # Build edge coordinates for Plotly
# edge_x, edge_y = [], []
# for source, target in G.edges():
# x0, y0 = pos[source]
# x1, y1 = pos[target]
# edge_x.extend([x0, x1, None])
# edge_y.extend([y0, y1, None])
# edge_trace = go.Scatter(
# x=edge_x, y=edge_y,
# line=dict(width=1, color='#888'),
# hoverinfo='none',
# mode='lines'
# )
# # Build node coordinates and text lists.
# node_x, node_y = [], []
# node_text_trunc, node_text_hover, node_color = [], [], []
# for node in G.nodes():
# x, y = pos[node]
# node_x.append(x)
# node_y.append(y)
# full_label = G.nodes[node].get('label', str(node))
# truncated_label = full_label[0:30] # truncated version for display
# # Check for a "uri" attribute (lowercase) and create a clickable link if present.
# uri = G.nodes[node].get('uri')
# uri_link = f'<br><a href="{uri}" target="_blank">{uri}</a>' if uri else ""
# node_text_trunc.append(f"{truncated_label}<br>Type: {G.nodes[node].get('type')}")
# node_text_hover.append(f"{full_label}<br>Type: {G.nodes[node].get('type')}{uri_link}")
# node_color.append(G.nodes[node]['layer'])
# node_trace = go.Scatter(
# x=node_x, y=node_y,
# mode='markers+text',
# textposition="bottom center",
# hoverinfo='text',
# marker=dict(
# showscale=True,
# colorscale='YlGnBu',
# reversescale=True,
# color=node_color,
# size=15,
# colorbar=dict(title='Layer')
# ),
# text=node_text_trunc, # Display truncated text on the plot.
# hovertext=node_text_hover # Full text (with clickable URL if present) for hover.
# )
# # Create the Plotly figure.
# fig = go.Figure(data=[edge_trace, node_trace],
# layout=go.Layout(
# title='<br>Integrated Network Graph Visualization',
# titlefont_size=16,
# dragmode='pan',
# showlegend=False,
# hovermode='closest',
# margin=dict(b=20, l=5, r=5, t=40),
# width=1000,
# height=800,
# xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
# yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
# ))
# return fig
def filter_network(data, include_types, include_fields):
"""
Filter the data to only include records of the specified types
and remove specified fields.
Parameters:
data (list): List of JSON records.
include_types (set): Asset types to keep.
include_fields (set): Fields to remove from records.
Returns:
list: Filtered data.
"""
filtered_data = []
for record in data:
if record.get("asset_type") in include_types:
# Create a copy to avoid modifying original data
filtered_record = {key: value for key, value in record.items() if key in include_fields}
filtered_data.append(filtered_record)
return filtered_data
def visualize_network_pyvis(data, include_types, type_to_position, output_html = "graph.html"):
width="100%"
height="1200px"
layer_spacing=2.0
node_spacing=2.0
# import tempfile
# output_html = tempfile.NamedTemporaryFile(delete=False)
G = create_network(data, include_types, type_to_position) # Create the network graph
# 1. Assign hierarchy and determine types
nx.set_node_attributes(
G,
{n: type_to_position.get(attrs["type"], 0) for n, attrs in G.nodes(data=True)},
name="layer"
)
types = sorted({attrs["type"] for _, attrs in G.nodes(data=True)})
# 2. Layout
pos = nx.multipartite_layout(G, subset_key="layer", align="horizontal")
pos = {n: (x * layer_spacing * 1000, y * node_spacing * 1000) for n, (x, y) in pos.items()}
# 3. Assign colors
base_colors = ["#e6194B", "#3cb44b", "#ffe119", "#4363d8", "#f58231",
"#911eb4", "#46f0f0", "#f032e6", "#bcf60c", "#fabebe"]
color_map = {t: base_colors[i % len(base_colors)] for i, t in enumerate(types)}
# 4. Build the graph
net = Network(width=width, height=height, directed=True)
net.toggle_physics(False)
net.toggle_drag_nodes(True)
net.show_buttons(filter_=["physics", "interaction", "clustering"])
for node_id, attrs in G.nodes(data=True):
x, y = pos[node_id]
t = attrs["type"]
c = color_map[t]
net.add_node(node_id, label=attrs.get("label", str(node_id)), title=f"{attrs.get('label')}<br>Type: {t}<br>{attrs.get('uri','')}",
x=x, y=y, physics=False, group=t,
color={"background": c, "border": c, "highlight": {"background": c, "border": c}, "hover": {"background": c, "border": c}})
for u, v in G.edges():
net.add_edge(u, v)
# 5. Inject clustering + double-click expand
cluster_js = f"""
<script type="text/javascript">
window.addEventListener('load', () => {{
const types = {types};
const colors = {color_map};
types.forEach(t => {{
network.cluster({{
joinCondition: opts => opts.group === t,
clusterNodeProperties: {{
id: 'cluster:' + t,
label: 'Cluster: ' + t,
shape: 'box',
color: {{
background: colors[t],
border: colors[t],
highlight: {{background: colors[t], border: colors[t]}},
hover: {{background: colors[t], border: colors[t]}}
}}
}}
}});
}});
network.on('doubleClick', params => {{
if (params.nodes.length === 1) {{
const id = params.nodes[0];
if (network.isCluster(id)) {{
network.openCluster(id);
}}
}}
}});
}});
</script>
"""
# 6. Inject a floating HTML legend panel
legend_html = "<div id='legend' style='position:fixed; top:10px; right:10px; background:white; border:1px solid #ccc; padding:10px; font-family:Arial;'><strong>Legend</strong><br>"
for t in types:
c = color_map[t]
legend_html += f"<div style='display:flex; align-items:center; margin-top:5px;'><span style='width:15px; height:15px; background:{c}; border:1px solid #000; display:inline-block; margin-right:5px;'></span>{t}</div>"
legend_html += "</div>"
# net.toggle_physics(True)
# Combine HTML parts
net.save_graph(output_html)
with open(output_html, "r+", encoding="utf-8") as f:
html = f.read().replace("</body>", cluster_js + legend_html + "\n</body>")
f.seek(0); f.write(html); f.truncate()
return output_html
def create_lineage_graph(lineage_data, type="pyvis", output_file="graph.html"):
all_types = {record.get("asset_type") for record in lineage_data if record.get("asset_type")}
# Exclude unwanted types.
exclude_types = {"user", "secret", "org_secret", "commit", "actor", "build_machine"}
types_to_position = {
"organization": 1,
"repo": 2,
"branch": 3,
"workflow": 4,
"workflow_run": 5,
"image": 6,
"pod": 7,
"namespace": 8
}
include_types = all_types - exclude_types
# Generate and display the network visualization.
if type == "plotly":
# fig = visualize_network_plotly(lineage_data, include_types, types_to_position)
return None
else:
output_file = Path(output_file)
output_file.touch(exist_ok=True)
output_file = output_file.resolve()
output_file = str(output_file)
file = visualize_network_pyvis(lineage_data, include_types, types_to_position, output_html=output_file)
return file
if __name__ == "__main__":
# Open the JSON file and load data.
filepath = "demo-data/astro-lineage.json"
with open(filepath, "r") as f:
data = json.load(f)
filename = create_lineage_graph(data, output_file="demo-data/lineage_graph.html")
print(f"Network graph saved to {filename}")
print(f"Open by browsing file:///{filename}")
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