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- Rename dbt project from toronto_housing to portfolio - Restructure dbt models into domain subdirectories: - shared/ for cross-domain dimensions (dim_time) - staging/toronto/, intermediate/toronto/, marts/toronto/ - Update SQLAlchemy models for raw_toronto schema - Add explicit cross-schema FK relationships for FactRentals - Namespace figure factories under figures/toronto/ - Namespace notebooks under notebooks/toronto/ - Update Makefile with domain-specific targets and env loading - Update all documentation for multi-dashboard structure This enables adding new dashboard projects (e.g., /football, /energy) without structural conflicts or naming collisions. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
"""SQLAlchemy models for dimension tables."""
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from datetime import date
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from geoalchemy2 import Geometry
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from sqlalchemy import Boolean, Date, Integer, Numeric, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from .base import Base
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# Schema constants
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RAW_TORONTO_SCHEMA = "raw_toronto"
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class DimTime(Base):
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"""Time dimension table (shared across all projects).
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Note: Stays in public schema as it's a shared dimension.
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"""
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__tablename__ = "dim_time"
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__table_args__ = {"schema": "public"}
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date_key: Mapped[int] = mapped_column(Integer, primary_key=True)
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full_date: Mapped[date] = mapped_column(Date, nullable=False, unique=True)
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year: Mapped[int] = mapped_column(Integer, nullable=False)
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month: Mapped[int] = mapped_column(Integer, nullable=False)
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quarter: Mapped[int] = mapped_column(Integer, nullable=False)
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month_name: Mapped[str] = mapped_column(String(20), nullable=False)
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is_month_start: Mapped[bool] = mapped_column(Boolean, default=True)
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class DimCMHCZone(Base):
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"""CMHC zone dimension table with PostGIS geometry."""
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__tablename__ = "dim_cmhc_zone"
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__table_args__ = {"schema": RAW_TORONTO_SCHEMA}
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zone_key: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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zone_code: Mapped[str] = mapped_column(String(10), nullable=False, unique=True)
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zone_name: Mapped[str] = mapped_column(String(100), nullable=False)
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geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
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class DimNeighbourhood(Base):
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"""City of Toronto neighbourhood dimension.
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Note: No FK to fact tables in V1 - reference overlay only.
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"""
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__tablename__ = "dim_neighbourhood"
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__table_args__ = {"schema": RAW_TORONTO_SCHEMA}
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neighbourhood_id: Mapped[int] = mapped_column(Integer, primary_key=True)
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name: Mapped[str] = mapped_column(String(100), nullable=False)
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geometry = mapped_column(Geometry("POLYGON", srid=4326), nullable=True)
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population: Mapped[int | None] = mapped_column(Integer, nullable=True)
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land_area_sqkm: Mapped[float | None] = mapped_column(Numeric(10, 4), nullable=True)
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pop_density_per_sqkm: Mapped[float | None] = mapped_column(
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Numeric(10, 2), nullable=True
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)
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pct_bachelors_or_higher: Mapped[float | None] = mapped_column(
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Numeric(5, 2), nullable=True
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)
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median_household_income: Mapped[float | None] = mapped_column(
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Numeric(12, 2), nullable=True
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)
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pct_owner_occupied: Mapped[float | None] = mapped_column(
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Numeric(5, 2), nullable=True
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)
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pct_renter_occupied: Mapped[float | None] = mapped_column(
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Numeric(5, 2), nullable=True
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)
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census_year: Mapped[int] = mapped_column(Integer, default=2021)
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class DimPolicyEvent(Base):
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"""Policy event dimension for time-series annotation."""
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__tablename__ = "dim_policy_event"
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__table_args__ = {"schema": RAW_TORONTO_SCHEMA}
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event_id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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event_date: Mapped[date] = mapped_column(Date, nullable=False)
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effective_date: Mapped[date | None] = mapped_column(Date, nullable=True)
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level: Mapped[str] = mapped_column(
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String(20), nullable=False
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) # federal/provincial/municipal
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category: Mapped[str] = mapped_column(
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String(20), nullable=False
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) # monetary/tax/regulatory/supply/economic
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title: Mapped[str] = mapped_column(String(200), nullable=False)
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description: Mapped[str | None] = mapped_column(Text, nullable=True)
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expected_direction: Mapped[str] = mapped_column(
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String(10), nullable=False
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) # bearish/bullish/neutral
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source_url: Mapped[str | None] = mapped_column(String(500), nullable=True)
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confidence: Mapped[str] = mapped_column(
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String(10), default="medium"
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) # high/medium/low
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