Visualization

from semantica.context import ContextGraph
from semantica.visualization import KGVisualizer, AnalyticsVisualizer

graph = ContextGraph(advanced_analytics=True)

for node_id, ntype, content in [
    ("wkstn-047",  "Host",   "Compromised workstation WKSTN-047"),
    ("dc01",       "Host",   "Domain controller DC01"),
    ("jsmith",     "User",   "Compromised user jsmith"),
    ("psexec",     "Tool",   "PsExec lateral movement tool"),
    ("t1021",      "MITRE",  "T1021.002 SMB/Admin Shares"),
]:
    graph.add_node(node_id, ntype, content)

graph.add_edge("wkstn-047", "dc01",    "lateral_movement", weight=1.0)
graph.add_edge("jsmith",    "wkstn-047","session_on",      weight=0.9)
graph.add_edge("psexec",    "wkstn-047","executed_on",     weight=1.0)
graph.add_edge("psexec",    "t1021",   "implements",       weight=0.95)

viz = KGVisualizer()

# Incident network with lateral movement path highlighted
viz.visualize_network(
    graph          = graph.to_dict(),
    output         = "interactive",
    file_path      = "soc/incident_graph.html",
    node_color_by  = "type",
    highlight_path = ["wkstn-047", "dc01"],
    hover_data     = ["type", "content"],
)

# Relationship matrix — who connects to what
viz.visualize_relationship_matrix(
    graph     = graph.to_dict(),
    output    = "interactive",
    file_path = "soc/rel_matrix.html",
)

# Centrality rankings — which host is most pivotal?
av = AnalyticsVisualizer()
av.visualize_centrality_rankings(
    centrality      = {"wkstn-047": 0.35, "dc01": 0.28, "psexec": 0.22, "jsmith": 0.15},
    centrality_type = "degree",
    output          = "interactive",
    file_path       = "soc/centrality.html",
)

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
from semantica.visualization import KGVisualizer, EmbeddingVisualizer, OntologyVisualizer
from semantica.ontology import OntologyGenerator
import numpy as np

graph = ContextGraph(advanced_analytics=True)
ctx   = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    graph_expansion=True,
)
ctx.store([
    "Metformin activates AMPK and reduces hepatic glucose production in Type 2 Diabetes.",
    "Dapagliflozin inhibits SGLT2 and reduces cardiovascular mortality in HFrEF.",
    "Semaglutide agonises GLP-1R and reduces HbA1c in obesity and Type 2 Diabetes.",
], extract_entities=True, extract_relationships=True)

kg_viz = KGVisualizer()
kg_viz.visualize_network(
    graph         = graph.to_dict(),
    output        = "interactive",
    file_path     = "drug_kg.html",
    node_color_by = "type",
    node_size_by  = "degree",
)

# OWL class hierarchy
ontology = OntologyGenerator(
    base_uri="https://purl.obolibrary.org/obo/DRUG_",
    min_occurrences=1,
).generate_from_graph(graph.to_dict())

ov = OntologyVisualizer()
ov.visualize_hierarchy(ontology, output="interactive", file_path="drug_hierarchy.html")
ov.visualize_structure(ontology, output="interactive", file_path="drug_ontology.html")

# UMAP projection and similarity heatmap for drug embeddings
embeddings = np.array([[0.1, 0.2, 0.3], [0.15, 0.22, 0.31], [0.8, 0.7, 0.6]])
labels     = ["Metformin", "Dapagliflozin", "Semaglutide"]

ev = EmbeddingVisualizer()
# UMAP (Uniform Manifold Approximation and Projection) reduces high-dimensional 
# embeddings to 2D while preserving local neighborhood structure
ev.visualize_2d_projection(
    embeddings, labels, method="umap",
    output="interactive", file_path="drug_embeddings.html",
)
ev.visualize_similarity_heatmap(
    embeddings, labels,
    output="interactive", file_path="drug_similarity.html",
)

from semantica.context import ContextGraph
from semantica.change_management import TemporalVersionManager
from semantica.visualization import KGVisualizer, TemporalVisualizer, OntologyVisualizer
from semantica.ontology import OntologyGenerator

graph = ContextGraph(advanced_analytics=True)
vm    = TemporalVersionManager(storage_path="regulatory_versions.db")

for node, ntype, content in [
    ("bcbs-cre20",  "Regulation", "Basel III CRE20 — CRE capital requirements"),
    ("metric-ltv",  "Metric",     "Loan-to-Value Ratio"),
    ("metric-dscr", "Metric",     "Debt Service Coverage Ratio"),
    ("metric-pd",   "Metric",     "Probability of Default"),
    ("metric-lgd",  "Metric",     "Loss Given Default"),
]:
    graph.add_node(node, ntype, content)

graph.add_edge("bcbs-cre20", "metric-ltv",  "requires", weight=1.0)
graph.add_edge("bcbs-cre20", "metric-dscr", "requires", weight=1.0)
graph.add_edge("bcbs-cre20", "metric-pd",   "requires", weight=0.9)
graph.add_edge("bcbs-cre20", "metric-lgd",  "requires", weight=0.9)

kg_viz = KGVisualizer()
kg_viz.visualize_network(
    graph         = graph.to_dict(),
    output        = "static",
    file_path     = "regulatory/regulatory_kg.png",
    node_color_by = "type",
    node_size_by  = "degree",
)

# OWL hierarchy — static PNG for the documentation appendix
ontology = OntologyGenerator(
    base_uri="https://basel.eba.eu/ontology/",
    min_occurrences=1,
).generate_from_graph(graph.to_dict())

ov = OntologyVisualizer()
ov.visualize_hierarchy(ontology, output="static", file_path="regulatory/class_hierarchy.png")

# Graph growth curve across quarterly snapshots
tv = TemporalVisualizer()
tv.visualize_metrics_evolution(
    metrics_history = {
        "node_count": [12, 18, 25, 31],
        "edge_count": [8,  15, 24, 32],
    },
    timestamps = ["2025-01-01", "2025-04-01", "2025-07-01", "2025-10-01"],
    output     = "interactive",
    file_path  = "regulatory/graph_growth.html",
)

# Snapshot comparison — what changed between Q2 and Q3 Basel update?
snap1 = vm.get_version("basel_q2_2025")
snap2 = vm.get_version("basel_q3_2025")
if snap1 and snap2:
    tv.visualize_snapshot_comparison(
        snapshots = {"basel_q2_2025": snap1, "basel_q3_2025": snap2},
        output    = "interactive",
        file_path = "regulatory/snapshot_diff.html",
    )