lmiranda
d0f32edba7
- Add StatCan CMHC parser to fetch rental data from Statistics Canada API - Create year spine (2014-2025) as time dimension driver instead of census - Add CMA-level rental and income intermediate models - Update mart_neighbourhood_overview to use rental years as base - Fix neighbourhood_service queries to match dbt schema - Add CMHC data loading to pipeline script Data now flows correctly: 158 neighbourhoods × 12 years = 1,896 records Rent data available 2019-2025, crime data 2014-2024 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
55 lines
1.2 KiB
Python
55 lines
1.2 KiB
Python
#!/usr/bin/env python3
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"""Initialize database schema.
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Usage:
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python scripts/db/init_schema.py
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This script creates all SQLAlchemy tables in the database.
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Run this after docker-compose up to initialize the schema.
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"""
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from portfolio_app.toronto.models import create_tables, get_engine # noqa: E402
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def main() -> int:
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"""Initialize the database schema."""
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print("Initializing database schema...")
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try:
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engine = get_engine()
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# Test connection
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from sqlalchemy import text
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with engine.connect() as conn:
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result = conn.execute(text("SELECT 1"))
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result.fetchone()
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print("Database connection successful")
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# Create all tables
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create_tables()
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print("Schema created successfully")
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# List created tables
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from sqlalchemy import inspect
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inspector = inspect(engine)
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tables = inspector.get_table_names()
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print(f"Created tables: {', '.join(tables)}")
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return 0
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except Exception as e:
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print(f"Error: {e}", file=sys.stderr)
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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