from stl_parser import parse

result = parse('[Einstein] -> [Theory_Relativity] ::mod(confidence=0.98, rule="empirical")')
stmt = result.statements[0]
print(stmt.source.name)          # "Einstein"
print(stmt.modifiers.confidence)  # 0.98

from stl_parser import stl

stmt = stl("[Rain]", "[Flooding]").mod(confidence=0.85, rule="causal", strength=0.8).build()
print(str(stmt))  # [Rain] -> [Flooding] ::mod(confidence=0.85, rule="causal", strength=0.8)

from stl_parser import stl, stl_doc

doc = stl_doc(
    stl("[Rain]", "[Flooding]").mod(rule="causal", confidence=0.85),
    stl("[Flooding]", "[Evacuation]").mod(rule="causal", confidence=0.90),
)
print(len(doc.statements))  # 2

from stl_parser import parse, load_schema, validate_against_schema

schema = load_schema("docs/schemas/causal.stl.schema")
result = parse('[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85, strength=0.8)')
validation = validate_against_schema(result, schema)
print(f"Valid: {validation.is_valid}")

from stl_parser import validate_llm_output

# Spaces in anchors, wrong arrow, missing ::, typo, value out of range — all fixed
result = validate_llm_output("[Big Problem] -> [Smart Solution] mod(confience=1.2, strenght=0.3)")
print(result.statements[0])
# [Big_Problem] -> [Smart_Solution] ::mod(confidence=1.0, strength=0.3)

print(f"Valid: {result.is_valid}, Repairs: {len(result.repairs)}")
# Valid: True, Repairs: 6

from stl_parser import parse, find, find_all, filter_statements, select

result = parse("""
[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85)
[Sun] -> [Evaporation] ::mod(rule="causal", confidence=0.90)
[Wind] -> [Erosion] ::mod(rule="causal", confidence=0.70)
""")

# Find first match ("source" resolves to source.name)
stmt = find(result, source="Rain")

# Filter returns a new ParseResult
high_conf = filter_statements(result, confidence__gte=0.85)
print(len(high_conf.statements))  # 2

# Extract a single field from all statements
names = select(result, field="source")   # ["Rain", "Sun", "Wind"]
confs = select(result, field="confidence")  # [0.85, 0.9, 0.7]

from stl_parser import parse, stl_diff, stl_patch

before = parse('[A] -> [B] ::mod(confidence=0.8)')
after  = parse('[A] -> [B] ::mod(confidence=0.9)')

diff = stl_diff(before, after)
print(diff.summary)  # {added: 0, removed: 0, modified: 1}

patched = stl_patch(before, diff)

from stl_parser import STLEmitter, stream_parse

# Write statements to a file
with STLEmitter("output.stl") as emitter:
    emitter.emit("[A]", "[B]", confidence=0.9, rule="causal")
    emitter.emit("[C]", "[D]", confidence=0.8, rule="logical")

# Stream-parse a file
for stmt in stream_parse("output.stl"):
    print(f"{stmt.source.name} -> {stmt.target.name}")

from stl_parser import parse, extract_chains, STLGraph

result = parse("""
[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85)
[Flooding] -> [Evacuation] ::mod(rule="causal", confidence=0.90)
[Evacuation] -> [Shelter] ::mod(rule="causal", confidence=0.80)
""")

chains = extract_chains(result, min_length=1)
print(STLGraph.format_chains(chains))
# Chain 1: [Rain] → [Flooding] → [Evacuation] → [Shelter]

from stl_parser import parse, effective_confidence

# Statement with a timestamp
result = parse('[Fact_X] -> [Conclusion_Y] ::mod(confidence=0.95, timestamp="2020-01-01T00:00:00Z")')
stmt = result.statements[0]

# Compute decayed confidence (exponential half-life)
decayed = effective_confidence(stmt, half_life_days=365)
print(f"Original: {stmt.modifiers.confidence}, Decayed: {decayed:.4f}")