from pathlib import Path
from data_engine import DataContext

BASE = Path(__file__).parents[1]

# Check that profiling and data quality checks return expected results
def test_profile_and_quality():
    ctx=DataContext.from_path(BASE/"examples"/"dirty_orders.csv")
    assert ctx.profile()["rows"] == 8
    assert ctx.profile()["columns"] == 5
    assert ctx.quality_report()["issue_count"] > 0

# Verify that schema validation detects invalid data
def test_schema_validation_detects_failures():
    ctx=DataContext.from_path(BASE/"examples"/"dirty_orders.csv")
    result=ctx.validate_schema((BASE/"examples"/"expected_schema.json").read_text())
    assert result["valid"] is False

# Verify that read-only SQL queries can be executed successfully
def test_readonly_sql():
    ctx=DataContext.from_path(BASE/"examples"/"dirty_orders.csv")
    result=ctx.execute_sql("SELECT COUNT(*) AS n FROM dataset")
    assert result["ok"] is True
    assert result["preview"][0]["n"] == 8