lmiranda
ead6d91a28
Sprint 3 implementation: - Pydantic schemas for TRREB, CMHC, and dimension data validation - SQLAlchemy models with PostGIS geometry for fact and dimension tables - Parser structure (stubs) for TRREB PDF and CMHC CSV processing Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
"""SQLAlchemy models for dimension tables."""
|
|
|
|
from datetime import date
|
|
|
|
from geoalchemy2 import Geometry
|
|
from sqlalchemy import Boolean, Date, Integer, Numeric, String, Text
|
|
from sqlalchemy.orm import Mapped, mapped_column
|
|
|
|
from .base import Base
|
|
|
|
|
|
class DimTime(Base):
|
|
"""Time dimension table."""
|
|
|
|
__tablename__ = "dim_time"
|
|
|
|
date_key: Mapped[int] = mapped_column(Integer, primary_key=True)
|
|
full_date: Mapped[date] = mapped_column(Date, nullable=False, unique=True)
|
|
year: Mapped[int] = mapped_column(Integer, nullable=False)
|
|
month: Mapped[int] = mapped_column(Integer, nullable=False)
|
|
quarter: Mapped[int] = mapped_column(Integer, nullable=False)
|
|
month_name: Mapped[str] = mapped_column(String(20), nullable=False)
|
|
is_month_start: Mapped[bool] = mapped_column(Boolean, default=True)
|
|
|
|
|
|
class DimTRREBDistrict(Base):
|
|
"""TRREB district dimension table with PostGIS geometry."""
|
|
|
|
__tablename__ = "dim_trreb_district"
|
|
|
|
district_key: Mapped[int] = mapped_column(
|
|
Integer, primary_key=True, autoincrement=True
|
|
)
|
|
district_code: Mapped[str] = mapped_column(String(3), nullable=False, unique=True)
|
|
district_name: Mapped[str] = mapped_column(String(100), nullable=False)
|
|
area_type: Mapped[str] = mapped_column(String(10), nullable=False)
|
|
geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
|
|
|
|
|
|
class DimCMHCZone(Base):
|
|
"""CMHC zone dimension table with PostGIS geometry."""
|
|
|
|
__tablename__ = "dim_cmhc_zone"
|
|
|
|
zone_key: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
|
|
zone_code: Mapped[str] = mapped_column(String(10), nullable=False, unique=True)
|
|
zone_name: Mapped[str] = mapped_column(String(100), nullable=False)
|
|
geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
|
|
|
|
|
|
class DimNeighbourhood(Base):
|
|
"""City of Toronto neighbourhood dimension.
|
|
|
|
Note: No FK to fact tables in V1 - reference overlay only.
|
|
"""
|
|
|
|
__tablename__ = "dim_neighbourhood"
|
|
|
|
neighbourhood_id: Mapped[int] = mapped_column(Integer, primary_key=True)
|
|
name: Mapped[str] = mapped_column(String(100), nullable=False)
|
|
geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
|
|
population: Mapped[int | None] = mapped_column(Integer, nullable=True)
|
|
land_area_sqkm: Mapped[float | None] = mapped_column(Numeric(10, 4), nullable=True)
|
|
pop_density_per_sqkm: Mapped[float | None] = mapped_column(
|
|
Numeric(10, 2), nullable=True
|
|
)
|
|
pct_bachelors_or_higher: Mapped[float | None] = mapped_column(
|
|
Numeric(5, 2), nullable=True
|
|
)
|
|
median_household_income: Mapped[float | None] = mapped_column(
|
|
Numeric(12, 2), nullable=True
|
|
)
|
|
pct_owner_occupied: Mapped[float | None] = mapped_column(
|
|
Numeric(5, 2), nullable=True
|
|
)
|
|
pct_renter_occupied: Mapped[float | None] = mapped_column(
|
|
Numeric(5, 2), nullable=True
|
|
)
|
|
census_year: Mapped[int] = mapped_column(Integer, default=2021)
|
|
|
|
|
|
class DimPolicyEvent(Base):
|
|
"""Policy event dimension for time-series annotation."""
|
|
|
|
__tablename__ = "dim_policy_event"
|
|
|
|
event_id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
|
|
event_date: Mapped[date] = mapped_column(Date, nullable=False)
|
|
effective_date: Mapped[date | None] = mapped_column(Date, nullable=True)
|
|
level: Mapped[str] = mapped_column(
|
|
String(20), nullable=False
|
|
) # federal/provincial/municipal
|
|
category: Mapped[str] = mapped_column(
|
|
String(20), nullable=False
|
|
) # monetary/tax/regulatory/supply/economic
|
|
title: Mapped[str] = mapped_column(String(200), nullable=False)
|
|
description: Mapped[str | None] = mapped_column(Text, nullable=True)
|
|
expected_direction: Mapped[str] = mapped_column(
|
|
String(10), nullable=False
|
|
) # bearish/bullish/neutral
|
|
source_url: Mapped[str | None] = mapped_column(String(500), nullable=True)
|
|
confidence: Mapped[str] = mapped_column(
|
|
String(10), default="medium"
|
|
) # high/medium/low
|