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import json
import networkx as nx
import matplotlib.pyplot as plt
import os
# Define the path to your index.json file
index_file_path = "graphs/index.json"
# Load the data from index.json
def load_index_data(file_path):
"""Load the index.json file and parse its contents."""
with open(file_path, "r") as file:
data = json.load(file)
return data
def load_entity_file(entity_info):
"""Load the entity-specific JSON file if file_path is provided."""
file_path = entity_info.get("file_path")
if file_path and os.path.exists(file_path):
try:
with open(file_path, "r") as file:
data = json.load(file)
return data
except json.JSONDecodeError as e:
print(f"Error loading JSON file at {file_path}: {e}")
return None
elif file_path:
print(f"File not found: {file_path}")
return None
def build_graph(data):
"""Builds a directed graph based on entities and relationships."""
G = nx.DiGraph()
# Add nodes for each entity, excluding file placeholders
excluded_nodes = {"patient_protection._tmp", "phsa_sec_340b", "medicade_tmp"}
for entity_id, entity_info in data["entities"].items():
if entity_id in excluded_nodes:
continue
label = entity_info.get("label", entity_id)
G.add_node(entity_id, label=label, domain=entity_info.get("inherits_from", "Default"))
# Load entity file if specified
entity_data = load_entity_file(entity_info)
if isinstance(entity_data, dict): # Check if the loaded data is a dictionary
for relationship in entity_data.get("relationships", []):
source = relationship["source"]
target = relationship["target"]
relationship_label = relationship["attributes"].get("relationship", "related_to")
G.add_edge(source, target, label=relationship_label)
else:
print(f"Skipping entity {entity_id} due to invalid data format.")
# Add edges for each relationship in index.json
for relationship in data["relationships"]:
source = relationship["source"]
target = relationship["target"]
relationship_label = relationship["attributes"].get("relationship", "related_to")
G.add_edge(source, target, label=relationship_label)
return G
# Visualize the graph using Matplotlib
def visualize_graph(G, title="Inferred Contextual Relationships"):
"""Visualizes the graph with nodes and relationships, using domain colors and improved layout."""
# Use different colors for each domain
color_map = {
"Legislation": "lightcoral",
"Healthcare Systems": "lightgreen",
"Healthcare Policies": "lightblue",
"Default": "lightgrey"
}
# Set node colors based on their domains
node_colors = [color_map.get(G.nodes[node].get("domain", "Default"), "lightgrey") for node in G.nodes]
# Use Kamada-Kawai layout for better spacing of nodes
pos = nx.kamada_kawai_layout(G)
# Draw nodes with domain-specific colors
plt.figure(figsize=(15, 10))
nx.draw_networkx_nodes(G, pos, node_size=3000, node_color=node_colors, alpha=0.8)
nx.draw_networkx_labels(G, pos, font_size=9, font_color="black", font_weight="bold")
# Draw edges with labels
nx.draw_networkx_edges(G, pos, arrowstyle="->", arrowsize=15, edge_color="gray", connectionstyle="arc3,rad=0.1")
edge_labels = {(u, v): d["label"] for u, v, d in G.edges(data=True)}
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color="red", font_size=8)
# Set plot title and display
plt.title(title, fontsize=14)
plt.axis("off")
plt.show()
# Main execution
if __name__ == "__main__":
# Load data from the index.json
data = load_index_data(index_file_path)
# Build the graph with entities and relationships
G = build_graph(data)
# Visualize the graph
visualize_graph(G) |