diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..07058e7 --- /dev/null +++ b/.env.example @@ -0,0 +1,15 @@ +# Database Configuration +DATABASE_URL=postgresql://portfolio:portfolio_dev@localhost:5432/portfolio +POSTGRES_USER=portfolio +POSTGRES_PASSWORD=portfolio_dev +POSTGRES_DB=portfolio + +# Application Settings +DASH_DEBUG=true +SECRET_KEY=change-me-in-production + +# Logging +LOG_LEVEL=INFO + +# Optional: dbt profile (defaults to profiles.yml) +# DBT_PROFILES_DIR=. diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml new file mode 100644 index 0000000..2e69fed --- /dev/null +++ b/.gitea/workflows/ci.yml @@ -0,0 +1,35 @@ +name: CI + +on: + push: + branches: + - development + - staging + - main + pull_request: + branches: + - development + +jobs: + lint-and-test: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install ruff pytest + + - name: Run linter + run: ruff check . + + - name: Run tests + run: pytest tests/ -v --tb=short diff --git a/.gitea/workflows/deploy-production.yml b/.gitea/workflows/deploy-production.yml new file mode 100644 index 0000000..6cb8837 --- /dev/null +++ b/.gitea/workflows/deploy-production.yml @@ -0,0 +1,44 @@ +name: Deploy to Production + +on: + push: + branches: + - main + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Deploy to Production Server + uses: appleboy/ssh-action@v1.0.3 + with: + host: ${{ secrets.PROD_HOST }} + username: ${{ secrets.PROD_USER }} + key: ${{ secrets.PROD_SSH_KEY }} + script: | + set -euo pipefail + + cd ~/apps/personal-portfolio + + echo "Pulling latest changes..." + git fetch origin main + git reset --hard origin/main + + echo "Activating virtual environment..." + source .venv/bin/activate + + echo "Installing dependencies..." + pip install -r requirements.txt --quiet + + echo "Running dbt models..." + cd dbt && dbt run --profiles-dir . && cd .. + + echo "Restarting application..." + docker compose down + docker compose up -d + + echo "Waiting for health check..." + sleep 10 + curl -f http://localhost:8050/health || exit 1 + + echo "Production deployment complete!" diff --git a/.gitea/workflows/deploy-staging.yml b/.gitea/workflows/deploy-staging.yml new file mode 100644 index 0000000..5a26df2 --- /dev/null +++ b/.gitea/workflows/deploy-staging.yml @@ -0,0 +1,44 @@ +name: Deploy to Staging + +on: + push: + branches: + - staging + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Deploy to Staging Server + uses: appleboy/ssh-action@v1.0.3 + with: + host: ${{ secrets.STAGING_HOST }} + username: ${{ secrets.STAGING_USER }} + key: ${{ secrets.STAGING_SSH_KEY }} + script: | + set -euo pipefail + + cd ~/apps/personal-portfolio + + echo "Pulling latest changes..." + git fetch origin staging + git reset --hard origin/staging + + echo "Activating virtual environment..." + source .venv/bin/activate + + echo "Installing dependencies..." + pip install -r requirements.txt --quiet + + echo "Running dbt models..." + cd dbt && dbt run --profiles-dir . && cd .. + + echo "Restarting application..." + docker compose down + docker compose up -d + + echo "Waiting for health check..." + sleep 10 + curl -f http://localhost:8050/health || exit 1 + + echo "Staging deployment complete!" diff --git a/.gitignore b/.gitignore index 36b13f1..49c7a7f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,28 @@ -# ---> Python +# ==================== +# Project-Specific +# ==================== + +# Processed data (generated, not source) +data/*/processed/ + +# Reports (generated) +reports/ + +# Backups +backups/ + +# Notebook exports +notebooks/*.html + +# dbt +dbt/target/ +dbt/dbt_packages/ +dbt/logs/ + +# ==================== +# Python +# ==================== + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -174,3 +198,4 @@ cython_debug/ # PyPI configuration file .pypirc +dbt/.user.yml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..ed808f5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,33 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + args: ['--maxkb=1000'] + exclude: ^data/(raw/|toronto/raw/geo/) + - id: check-merge-conflict + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.9 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.8.0 + hooks: + - id: mypy + additional_dependencies: + - pydantic>=2.0 + - pandas-stubs + - types-requests + args: [--ignore-missing-imports] + exclude: ^(tests/|dbt/) + +ci: + autofix_commit_msg: "style: auto-fix by pre-commit hooks" + autoupdate_commit_msg: "chore: update pre-commit hooks" diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..b77dcbe --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.defaultInterpreterPath": "/home/leomiranda/WorkDev/personal/personal-portfolio/.venv/bin/python" +} diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..78e5da7 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,354 @@ +# CLAUDE.md + +Working context for Claude Code on the Analytics Portfolio project. + +--- + +## Project Status + +**Last Completed Sprint**: 9 (Neighbourhood Dashboard Transition) +**Current State**: Ready for deployment sprint or new features +**Branch**: `development` (feature branches merge here) + +--- + +## Quick Reference + +### Run Commands + +```bash +# Setup & Database +make setup # Install deps, create .env, init pre-commit +make docker-up # Start PostgreSQL + PostGIS (auto-detects x86/ARM) +make docker-down # Stop containers +make docker-logs # View container logs +make db-init # Initialize database schema +make db-reset # Drop and recreate database (DESTRUCTIVE) + +# Data Loading +make load-data # Load Toronto data from APIs, seed dev data +make load-data-only # Load Toronto data without dbt or seeding +make seed-data # Seed sample development data + +# Application +make run # Start Dash dev server + +# Testing & Quality +make test # Run pytest +make test-cov # Run pytest with coverage +make lint # Run ruff linter +make format # Run ruff formatter +make typecheck # Run mypy type checker +make ci # Run all checks (lint, typecheck, test) + +# dbt +make dbt-run # Run dbt models +make dbt-test # Run dbt tests +make dbt-docs # Generate and serve dbt documentation + +# Maintenance +make clean # Remove build artifacts and caches +``` + +### Branch Workflow + +1. Create feature branch FROM `development`: `git checkout -b feature/{sprint}-{description}` +2. Work and commit on feature branch +3. Merge INTO `development` when complete +4. `development` -> `staging` -> `main` for releases + +--- + +## Code Conventions + +### Import Style + +| Context | Style | Example | +|---------|-------|---------| +| Same directory | Single dot | `from .neighbourhood import NeighbourhoodRecord` | +| Sibling directory | Double dot | `from ..schemas.neighbourhood import CensusRecord` | +| External packages | Absolute | `import pandas as pd` | + +### Module Responsibilities + +| Directory | Contains | Purpose | +|-----------|----------|---------| +| `schemas/` | Pydantic models | Data validation | +| `models/` | SQLAlchemy ORM | Database persistence | +| `parsers/` | API/CSV extraction | Raw data ingestion | +| `loaders/` | Database operations | Data loading | +| `services/` | Query functions | dbt mart queries, business logic | +| `figures/` | Chart factories | Plotly figure generation | +| `callbacks/` | Dash callbacks | In `pages/{dashboard}/callbacks/` | +| `errors/` | Exception classes | Custom exceptions | +| `utils/` | Helper modules | Markdown loading, shared utilities | + +### Type Hints + +Use Python 3.10+ style: +```python +def process(items: list[str], config: dict[str, int] | None = None) -> bool: + ... +``` + +### Error Handling + +```python +# errors/exceptions.py +class PortfolioError(Exception): + """Base exception.""" + +class ParseError(PortfolioError): + """PDF/CSV parsing failed.""" + +class ValidationError(PortfolioError): + """Pydantic or business rule validation failed.""" + +class LoadError(PortfolioError): + """Database load operation failed.""" +``` + +### Code Standards + +- Single responsibility functions with verb naming +- Early returns over deep nesting +- Google-style docstrings only for non-obvious behavior +- Module-level constants for magic values +- Pydantic BaseSettings for runtime config + +--- + +## Application Structure + +**Entry Point:** `portfolio_app/app.py` (Dash app factory with Pages routing) + +| Directory | Purpose | Notes | +|-----------|---------|-------| +| `pages/` | Dash Pages (file-based routing) | URLs match file paths | +| `pages/toronto/` | Toronto Dashboard | `tabs/` for layouts, `callbacks/` for interactions | +| `components/` | Shared UI components | metric_card, sidebar, map_controls, time_slider | +| `figures/` | Plotly chart factories | choropleth, bar_charts, scatter, radar, time_series | +| `toronto/` | Toronto data logic | parsers/, loaders/, schemas/, models/ | +| `content/blog/` | Markdown blog articles | Processed by `utils/markdown_loader.py` | +| `notebooks/` | Data documentation | 5 domains: overview, housing, safety, demographics, amenities | + +**Key URLs:** `/` (home), `/toronto` (dashboard), `/blog` (listing), `/blog/{slug}` (articles) + +--- + +## Tech Stack (Locked) + +| Layer | Technology | Version | +|-------|------------|---------| +| Database | PostgreSQL + PostGIS | 16.x | +| Validation | Pydantic | >=2.0 | +| ORM | SQLAlchemy | >=2.0 (2.0-style API only) | +| Transformation | dbt-postgres | >=1.7 | +| Data Processing | Pandas | >=2.1 | +| Geospatial | GeoPandas + Shapely | >=0.14 | +| Visualization | Dash + Plotly | >=2.14 | +| UI Components | dash-mantine-components | Latest stable | +| Testing | pytest | >=7.0 | +| Python | 3.11+ | Via pyenv | + +**Notes**: +- SQLAlchemy 2.0 + Pydantic 2.0 only (never mix 1.x APIs) +- PostGIS extension required in database +- Docker Compose V2 format (no `version` field) +- **Multi-architecture support**: `make docker-up` auto-detects CPU architecture and uses the appropriate PostGIS image (x86_64: `postgis/postgis`, ARM64: `imresamu/postgis`) + +--- + +## Data Model Overview + +### Geographic Reality (Toronto Housing) + +``` +City Neighbourhoods (158) - Primary geographic unit for analysis +CMHC Zones (~20) - Rental data (Census Tract aligned) +``` + +### Star Schema + +| Table | Type | Keys | +|-------|------|------| +| `fact_rentals` | Fact | -> dim_time, dim_cmhc_zone | +| `dim_time` | Dimension | date_key (PK) | +| `dim_cmhc_zone` | Dimension | zone_key (PK), geometry | +| `dim_neighbourhood` | Dimension | neighbourhood_id (PK), geometry | +| `dim_policy_event` | Dimension | event_id (PK) | + +### dbt Layers + +| Layer | Naming | Purpose | +|-------|--------|---------| +| Staging | `stg_{source}__{entity}` | 1:1 source, cleaned, typed | +| Intermediate | `int_{domain}__{transform}` | Business logic | +| Marts | `mart_{domain}` | Final analytical tables | + +--- + +## Deferred Features + +**Stop and flag if a task seems to require these**: + +| Feature | Reason | +|---------|--------| +| Historical boundary reconciliation (140->158) | 2021+ data only for V1 | +| ML prediction models | Energy project scope (future phase) | +| Multi-project shared infrastructure | Build first, abstract second | + +--- + +## Environment Variables + +Required in `.env`: + +```bash +DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio +POSTGRES_USER=portfolio +POSTGRES_PASSWORD= +POSTGRES_DB=portfolio +DASH_DEBUG=true +SECRET_KEY= +LOG_LEVEL=INFO +``` + +--- + +## Script Standards + +All scripts in `scripts/`: +- Include usage comments at top +- Idempotent where possible +- Exit codes: 0 = success, 1 = error +- Use `set -euo pipefail` for bash +- Log to stdout, errors to stderr + +--- + +## Reference Documents + +| Document | Location | Use When | +|----------|----------|----------| +| Project reference | `docs/PROJECT_REFERENCE.md` | Architecture decisions, completed work | +| Developer guide | `docs/CONTRIBUTING.md` | How to add pages, blog posts, tabs | +| Lessons learned | `docs/project-lessons-learned/INDEX.md` | Past issues and solutions | +| Deployment runbook | `docs/runbooks/deployment.md` | Deploying to staging/production | +| Dashboard runbook | `docs/runbooks/adding-dashboard.md` | Adding new data dashboards | + +--- + +## Projman Plugin Workflow + +**CRITICAL: Always use the projman plugin for sprint and task management.** + +### When to Use Projman Skills + +| Skill | Trigger | Purpose | +|-------|---------|---------| +| `/projman:sprint-plan` | New sprint or phase implementation | Architecture analysis + Gitea issue creation | +| `/projman:sprint-start` | Beginning implementation work | Load lessons learned (Wiki.js or local), start execution | +| `/projman:sprint-status` | Check progress | Review blockers and completion status | +| `/projman:sprint-close` | Sprint completion | Capture lessons learned (Wiki.js or local backup) | + +### Default Behavior + +When user requests implementation work: + +1. **ALWAYS start with `/projman:sprint-plan`** before writing code +2. Create Gitea issues with proper labels and acceptance criteria +3. Use `/projman:sprint-start` to begin execution with lessons learned +4. Track progress via Gitea issue comments +5. Close sprint with `/projman:sprint-close` to document lessons + +### Gitea Repository + +- **Repo**: `personal-projects/personal-portfolio` +- **Host**: `gitea.hotserv.cloud` +- **SSH**: `ssh://git@hotserv.tailc9b278.ts.net:2222/personal-projects/personal-portfolio.git` +- **Labels**: 18 repository-level labels configured (Type, Priority, Complexity, Effort) + +### MCP Tools Available + +**Gitea**: +- `list_issues`, `get_issue`, `create_issue`, `update_issue`, `add_comment` +- `get_labels`, `suggest_labels` + +**Wiki.js**: +- `search_lessons`, `create_lesson`, `search_pages`, `get_page` + +### Lessons Learned (Backup Method) + +**When Wiki.js is unavailable**, use the local backup in `docs/project-lessons-learned/`: + +**At Sprint Start:** +1. Review `docs/project-lessons-learned/INDEX.md` for relevant past lessons +2. Search lesson files by tags/keywords before implementation +3. Apply prevention strategies from applicable lessons + +**At Sprint Close:** +1. Try Wiki.js `create_lesson` first +2. If Wiki.js fails, create lesson in `docs/project-lessons-learned/` +3. Use naming convention: `{phase-or-sprint}-{short-description}.md` +4. Update `INDEX.md` with new entry +5. Follow the lesson template in INDEX.md + +**Migration:** Once Wiki.js is configured, lessons will be migrated there for better searchability. + +### Issue Structure + +Every Gitea issue should include: +- **Overview**: Brief description +- **Files to Create/Modify**: Explicit paths +- **Acceptance Criteria**: Checkboxes +- **Technical Notes**: Implementation hints +- **Labels**: Listed in body (workaround for label API issues) + +--- + +## Other Available Plugins + +### Code Quality: code-sentinel + +Use for security scanning and refactoring analysis. + +| Command | Purpose | +|---------|---------| +| `/code-sentinel:security-scan` | Full security audit of codebase | +| `/code-sentinel:refactor` | Apply refactoring patterns | +| `/code-sentinel:refactor-dry` | Preview refactoring without applying | + +**When to use:** Before major releases, after adding authentication/data handling code, periodic audits. + +### Documentation: doc-guardian + +Use for documentation drift detection and synchronization. + +| Command | Purpose | +|---------|---------| +| `/doc-guardian:doc-audit` | Scan project for documentation drift | +| `/doc-guardian:doc-sync` | Synchronize pending documentation updates | + +**When to use:** After significant code changes, before releases, when docs feel stale. + +### Pull Requests: pr-review + +Use for comprehensive PR review with multiple analysis perspectives. + +| Command | Purpose | +|---------|---------| +| `/pr-review:initial-setup` | Configure PR review for this project | +| `/pr-review:project-init` | Quick project-level setup | + +**When to use:** Before merging significant PRs to `development` or `main`. + +### Git Workflow: git-flow + +Use for git operations assistance. + +**When to use:** Complex merge scenarios, branch management questions. + +--- + +*Last Updated: January 2026 (Post-Sprint 9)* diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0bd39ba --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024-2025 Leo Miranda + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..fa28c22 --- /dev/null +++ b/Makefile @@ -0,0 +1,192 @@ +.PHONY: setup docker-up docker-down db-init load-data seed-data run test dbt-run dbt-test lint format ci deploy clean help logs run-detached etl-toronto + +# Default target +.DEFAULT_GOAL := help + +# Environment +PYTHON := python3 +PIP := pip +DOCKER_COMPOSE := docker compose + +# Architecture detection for Docker images +ARCH := $(shell uname -m) +ifeq ($(ARCH),aarch64) + POSTGIS_IMAGE := imresamu/postgis:16-3.4 +else ifeq ($(ARCH),arm64) + POSTGIS_IMAGE := imresamu/postgis:16-3.4 +else + POSTGIS_IMAGE := postgis/postgis:16-3.4 +endif +export POSTGIS_IMAGE + +# Colors for output +BLUE := \033[0;34m +GREEN := \033[0;32m +YELLOW := \033[0;33m +NC := \033[0m + +help: ## Show this help message + @echo "Usage: make [target]" + @echo "" + @echo "Targets:" + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf " $(BLUE)%-15s$(NC) %s\n", $$1, $$2}' + +# ============================================================================= +# Setup +# ============================================================================= + +setup: ## Install dependencies, create .env, init pre-commit + @echo "$(GREEN)Installing dependencies...$(NC)" + $(PIP) install -e ".[dev,dbt]" + @echo "$(GREEN)Setting up environment...$(NC)" + @if [ ! -f .env ]; then cp .env.example .env; echo "$(YELLOW)Created .env from .env.example - please update values$(NC)"; fi + @echo "$(GREEN)Installing pre-commit hooks...$(NC)" + pre-commit install + @echo "$(GREEN)Setup complete!$(NC)" + +# ============================================================================= +# Docker +# ============================================================================= + +docker-up: ## Start PostgreSQL + PostGIS containers + @echo "$(GREEN)Starting database containers...$(NC)" + @echo "$(BLUE)Architecture: $(ARCH) -> Using image: $(POSTGIS_IMAGE)$(NC)" + $(DOCKER_COMPOSE) up -d + @echo "$(GREEN)Waiting for database to be ready...$(NC)" + @sleep 3 + @echo "$(GREEN)Database containers started!$(NC)" + +docker-down: ## Stop containers + @echo "$(YELLOW)Stopping containers...$(NC)" + $(DOCKER_COMPOSE) down + +docker-logs: ## View container logs + $(DOCKER_COMPOSE) logs -f + +# ============================================================================= +# Database +# ============================================================================= + +db-init: ## Initialize database schema + @echo "$(GREEN)Initializing database schema...$(NC)" + $(PYTHON) scripts/db/init_schema.py + +db-reset: ## Drop and recreate database (DESTRUCTIVE) + @echo "$(YELLOW)WARNING: This will delete all data!$(NC)" + @read -p "Are you sure? [y/N] " confirm && [ "$$confirm" = "y" ] || exit 1 + $(DOCKER_COMPOSE) down -v + $(DOCKER_COMPOSE) up -d + @sleep 3 + $(MAKE) db-init + +load-data: ## Load Toronto data from APIs, seed dev data, run dbt + @echo "$(GREEN)Loading Toronto neighbourhood data...$(NC)" + $(PYTHON) scripts/data/load_toronto_data.py + @echo "$(GREEN)Seeding development data...$(NC)" + $(PYTHON) scripts/data/seed_amenity_data.py + +load-data-only: ## Load Toronto data without running dbt or seeding + @echo "$(GREEN)Loading Toronto data (skip dbt)...$(NC)" + $(PYTHON) scripts/data/load_toronto_data.py --skip-dbt + +seed-data: ## Seed sample development data (amenities, median_age) + @echo "$(GREEN)Seeding development data...$(NC)" + $(PYTHON) scripts/data/seed_amenity_data.py + +# ============================================================================= +# Application +# ============================================================================= + +run: ## Start Dash development server + @echo "$(GREEN)Starting Dash server...$(NC)" + $(PYTHON) -m portfolio_app.app + +# ============================================================================= +# Testing +# ============================================================================= + +test: ## Run pytest + @echo "$(GREEN)Running tests...$(NC)" + pytest + +test-cov: ## Run pytest with coverage + @echo "$(GREEN)Running tests with coverage...$(NC)" + pytest --cov=portfolio_app --cov-report=html --cov-report=term + +# ============================================================================= +# dbt +# ============================================================================= + +dbt-run: ## Run dbt models + @echo "$(GREEN)Running dbt models...$(NC)" + cd dbt && dbt run --profiles-dir . + +dbt-test: ## Run dbt tests + @echo "$(GREEN)Running dbt tests...$(NC)" + cd dbt && dbt test --profiles-dir . + +dbt-docs: ## Generate dbt documentation + @echo "$(GREEN)Generating dbt docs...$(NC)" + cd dbt && dbt docs generate --profiles-dir . && dbt docs serve --profiles-dir . + +# ============================================================================= +# Code Quality +# ============================================================================= + +lint: ## Run ruff linter + @echo "$(GREEN)Running linter...$(NC)" + ruff check . + +format: ## Run ruff formatter + @echo "$(GREEN)Formatting code...$(NC)" + ruff format . + ruff check --fix . + +typecheck: ## Run mypy type checker + @echo "$(GREEN)Running type checker...$(NC)" + mypy portfolio_app + +ci: ## Run all checks (lint, typecheck, test) + @echo "$(GREEN)Running CI checks...$(NC)" + $(MAKE) lint + $(MAKE) typecheck + $(MAKE) test + @echo "$(GREEN)All checks passed!$(NC)" + +# ============================================================================= +# Operations +# ============================================================================= + +logs: ## Follow docker compose logs (usage: make logs or make logs SERVICE=postgres) + @./scripts/logs.sh $(SERVICE) + +run-detached: ## Start containers and wait for health check + @./scripts/run-detached.sh + +etl-toronto: ## Run Toronto ETL pipeline (usage: make etl-toronto MODE=--full) + @./scripts/etl/toronto.sh $(MODE) + +# ============================================================================= +# Deployment +# ============================================================================= + +deploy: ## Deploy to production + @echo "$(YELLOW)Deployment not yet configured$(NC)" + @echo "TODO: Add deployment script" + +# ============================================================================= +# Cleanup +# ============================================================================= + +clean: ## Remove build artifacts and caches + @echo "$(YELLOW)Cleaning up...$(NC)" + rm -rf build/ + rm -rf dist/ + rm -rf *.egg-info/ + rm -rf .pytest_cache/ + rm -rf .ruff_cache/ + rm -rf .mypy_cache/ + rm -rf htmlcov/ + rm -rf .coverage + find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true + @echo "$(GREEN)Clean complete!$(NC)" diff --git a/README.md b/README.md index 9f0372b..1f156b7 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,197 @@ -# personal-portfolio +# Analytics Portfolio +[![CI](https://gitea.hotserv.cloud/lmiranda/personal-portfolio/actions/workflows/ci.yml/badge.svg)](https://gitea.hotserv.cloud/lmiranda/personal-portfolio/actions) + +**Live Demo:** [leodata.science](https://leodata.science) + +A personal portfolio website showcasing data engineering and visualization capabilities, featuring an interactive Toronto Neighbourhood Dashboard. + +## Live Pages + +| Route | Page | Description | +|-------|------|-------------| +| `/` | Home | Bio landing page | +| `/about` | About | Background and experience | +| `/projects` | Projects | Portfolio project showcase | +| `/resume` | Resume | Professional CV | +| `/contact` | Contact | Contact form | +| `/blog` | Blog | Technical articles | +| `/blog/{slug}` | Article | Individual blog posts | +| `/toronto` | Toronto Dashboard | Neighbourhood analysis (5 tabs) | +| `/toronto/methodology` | Methodology | Dashboard data sources and methods | +| `/health` | Health | API health check endpoint | + +## Toronto Neighbourhood Dashboard + +An interactive choropleth dashboard analyzing Toronto's 158 official neighbourhoods across five dimensions: + +- **Overview**: Composite livability scores, income vs safety scatter +- **Housing**: Affordability index, rent trends, dwelling types +- **Safety**: Crime rates, breakdowns by type, trend analysis +- **Demographics**: Income distribution, age pyramids, population density +- **Amenities**: Parks, schools, transit accessibility + +**Data Sources**: +- City of Toronto Open Data Portal (neighbourhoods, census profiles, amenities) +- Toronto Police Service (crime statistics) +- CMHC Rental Market Survey (rental data by zone) + +## Architecture + +```mermaid +flowchart LR + subgraph Sources + A1[City of Toronto API] + A2[Toronto Police API] + A3[CMHC Data] + end + + subgraph ETL + B1[Parsers] + B2[Loaders] + end + + subgraph Database + C1[(PostgreSQL/PostGIS)] + C2[dbt Models] + end + + subgraph Application + D1[Dash App] + D2[Plotly Figures] + end + + A1 & A2 & A3 --> B1 --> B2 --> C1 --> C2 --> D1 --> D2 +``` + +**Pipeline Stages:** +- **Sources**: External APIs and data files (City of Toronto, Toronto Police, CMHC) +- **ETL**: Python parsers extract and validate data; loaders persist to database +- **Database**: PostgreSQL with PostGIS for geospatial; dbt transforms raw → staging → marts +- **Application**: Dash serves interactive dashboards with Plotly visualizations + +For detailed database schema, see [docs/DATABASE_SCHEMA.md](docs/DATABASE_SCHEMA.md). + +## Quick Start + +```bash +# Clone and setup +git clone https://gitea.hotserv.cloud/lmiranda/personal-portfolio.git +cd personal-portfolio + +# Install dependencies and configure environment +make setup + +# Start database +make docker-up + +# Initialize database schema +make db-init + +# Run development server +make run +``` + +Visit `http://localhost:8050` to view the portfolio. + +## Project Structure + +``` +portfolio_app/ +├── app.py # Dash app factory +├── config.py # Pydantic settings +├── pages/ +│ ├── home.py # Bio landing (/) +│ ├── about.py # About page +│ ├── contact.py # Contact form +│ ├── projects.py # Project showcase +│ ├── resume.py # Resume/CV +│ ├── blog/ # Blog system +│ │ ├── index.py # Article listing +│ │ └── article.py # Article renderer +│ └── toronto/ # Toronto dashboard +│ ├── dashboard.py # Main layout with tabs +│ ├── methodology.py # Data documentation +│ ├── tabs/ # Tab layouts (5) +│ └── callbacks/ # Interaction logic +├── components/ # Shared UI components +├── figures/ # Plotly figure factories +├── content/ +│ └── blog/ # Markdown blog articles +├── toronto/ # Toronto data logic +│ ├── parsers/ # API data extraction +│ ├── loaders/ # Database operations +│ ├── schemas/ # Pydantic models +│ └── models/ # SQLAlchemy ORM +└── errors/ # Exception handling + +dbt/ +├── models/ +│ ├── staging/ # 1:1 source tables +│ ├── intermediate/ # Business logic +│ └── marts/ # Analytical tables + +notebooks/ # Data documentation (15 notebooks) +├── overview/ # Overview tab visualizations +├── housing/ # Housing tab visualizations +├── safety/ # Safety tab visualizations +├── demographics/ # Demographics tab visualizations +└── amenities/ # Amenities tab visualizations + +docs/ +├── PROJECT_REFERENCE.md # Architecture reference +├── CONTRIBUTING.md # Developer guide +└── project-lessons-learned/ +``` + +## Tech Stack + +| Layer | Technology | +|-------|------------| +| Database | PostgreSQL 16 + PostGIS | +| Validation | Pydantic 2.x | +| ORM | SQLAlchemy 2.x | +| Transformation | dbt-postgres | +| Data Processing | Pandas, GeoPandas | +| Visualization | Dash + Plotly | +| UI Components | dash-mantine-components | +| Testing | pytest | +| Python | 3.11+ | + +## Development + +```bash +make test # Run pytest +make lint # Run ruff linter +make format # Format code +make ci # Run all checks +make dbt-run # Run dbt models +make dbt-test # Run dbt tests +``` + +## Environment Variables + +Copy `.env.example` to `.env` and configure: + +```bash +DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio +POSTGRES_USER=portfolio +POSTGRES_PASSWORD= +POSTGRES_DB=portfolio +DASH_DEBUG=true +SECRET_KEY= +``` + +## Documentation + +- **For developers**: See `docs/CONTRIBUTING.md` for setup and contribution guidelines +- **For Claude Code**: See `CLAUDE.md` for AI assistant context +- **Architecture**: See `docs/PROJECT_REFERENCE.md` for technical details + +## License + +MIT + +## Author + +Leo Miranda diff --git a/data/raw/cmhc/rmr-toronto-2021-en.xlsx b/data/raw/cmhc/rmr-toronto-2021-en.xlsx new file mode 100644 index 0000000..b52323a Binary files /dev/null and b/data/raw/cmhc/rmr-toronto-2021-en.xlsx differ diff --git a/data/raw/cmhc/rmr-toronto-2022-en.xlsx 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diff --git a/data/toronto/reference/.gitkeep b/data/toronto/reference/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt/dbt_project.yml b/dbt/dbt_project.yml new file mode 100644 index 0000000..473d75c --- /dev/null +++ b/dbt/dbt_project.yml @@ -0,0 +1,28 @@ +name: 'toronto_housing' +version: '1.0.0' +config-version: 2 + +profile: 'toronto_housing' + +model-paths: ["models"] +analysis-paths: ["analyses"] +test-paths: ["tests"] +seed-paths: ["seeds"] +macro-paths: ["macros"] +snapshot-paths: ["snapshots"] + +clean-targets: + - "target" + - "dbt_packages" + +models: + toronto_housing: + staging: + +materialized: view + +schema: staging + intermediate: + +materialized: view + +schema: intermediate + marts: + +materialized: table + +schema: marts diff --git a/dbt/macros/.gitkeep b/dbt/macros/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt/models/intermediate/.gitkeep b/dbt/models/intermediate/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt/models/intermediate/_intermediate.yml b/dbt/models/intermediate/_intermediate.yml new file mode 100644 index 0000000..908da4a --- /dev/null +++ b/dbt/models/intermediate/_intermediate.yml @@ -0,0 +1,87 @@ +version: 2 + +models: + - name: int_rentals__annual + description: "Rental data enriched with time and zone dimensions" + columns: + - name: rental_id + tests: + - unique + - not_null + - name: zone_code + tests: + - not_null + + - name: int_neighbourhood__demographics + description: "Combined census demographics with neighbourhood attributes" + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: census_year + description: "Census year" + tests: + - not_null + - name: income_quintile + description: "Income quintile (1-5, city-wide)" + + - name: int_neighbourhood__housing + description: "Housing indicators combining census and rental data" + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: year + description: "Reference year" + - name: rent_to_income_pct + description: "Rent as percentage of median income" + - name: is_affordable + description: "Boolean: rent <= 30% of income" + + - name: int_neighbourhood__crime_summary + description: "Aggregated crime with year-over-year trends" + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: year + description: "Statistics year" + tests: + - not_null + - name: crime_rate_per_100k + description: "Total crime rate per 100K population" + - name: yoy_change_pct + description: "Year-over-year change percentage" + + - name: int_neighbourhood__amenity_scores + description: "Normalized amenities per capita and per area" + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: year + description: "Reference year" + - name: total_amenities_per_1000 + description: "Total amenities per 1000 population" + - name: amenities_per_sqkm + description: "Total amenities per square km" + + - name: int_rentals__neighbourhood_allocated + description: "CMHC rental data allocated to neighbourhoods via area weights" + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: year + description: "Survey year" + tests: + - not_null + - name: avg_rent_2bed + description: "Weighted average 2-bedroom rent" + - name: vacancy_rate + description: "Weighted average vacancy rate" diff --git a/dbt/models/intermediate/int_census__toronto_cma.sql b/dbt/models/intermediate/int_census__toronto_cma.sql new file mode 100644 index 0000000..37560f4 --- /dev/null +++ b/dbt/models/intermediate/int_census__toronto_cma.sql @@ -0,0 +1,60 @@ +-- Intermediate: Toronto CMA census statistics by year +-- Provides city-wide averages for metrics not available at neighbourhood level +-- Used when neighbourhood-level data is unavailable (e.g., median household income) +-- Grain: One row per year + +with years as ( + select * from {{ ref('int_year_spine') }} +), + +census as ( + select * from {{ ref('stg_toronto__census') }} +), + +-- Census data is only available for 2016 and 2021 +-- Map each analysis year to the appropriate census year +year_to_census as ( + select + y.year, + case + when y.year <= 2018 then 2016 + else 2021 + end as census_year + from years y +), + +-- Toronto CMA median household income from Statistics Canada +-- Source: Census Profile Table 98-316-X2021001 +-- 2016: $65,829 (from Census Profile) +-- 2021: $84,000 (from Census Profile) +cma_income as ( + select 2016 as census_year, 65829 as median_household_income union all + select 2021 as census_year, 84000 as median_household_income +), + +-- City-wide aggregates from loaded neighbourhood data +city_aggregates as ( + select + census_year, + sum(population) as total_population, + avg(population_density) as avg_population_density, + avg(unemployment_rate) as avg_unemployment_rate + from census + where population is not null + group by census_year +), + +final as ( + select + y.year, + y.census_year, + ci.median_household_income, + ca.total_population, + ca.avg_population_density, + ca.avg_unemployment_rate + from year_to_census y + left join cma_income ci on y.census_year = ci.census_year + left join city_aggregates ca on y.census_year = ca.census_year +) + +select * from final diff --git a/dbt/models/intermediate/int_neighbourhood__amenity_scores.sql b/dbt/models/intermediate/int_neighbourhood__amenity_scores.sql new file mode 100644 index 0000000..a5d828e --- /dev/null +++ b/dbt/models/intermediate/int_neighbourhood__amenity_scores.sql @@ -0,0 +1,79 @@ +-- Intermediate: Normalized amenities per 1000 population +-- Pivots amenity types and calculates per-capita metrics +-- Grain: One row per neighbourhood per year + +with neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +amenities as ( + select * from {{ ref('stg_toronto__amenities') }} +), + +-- Aggregate amenity types +amenities_by_year as ( + select + neighbourhood_id, + amenity_year as year, + sum(case when amenity_type = 'Parks' then amenity_count else 0 end) as parks_count, + sum(case when amenity_type = 'Schools' then amenity_count else 0 end) as schools_count, + sum(case when amenity_type = 'Transit Stops' then amenity_count else 0 end) as transit_count, + sum(case when amenity_type = 'Libraries' then amenity_count else 0 end) as libraries_count, + sum(case when amenity_type = 'Community Centres' then amenity_count else 0 end) as community_centres_count, + sum(case when amenity_type = 'Recreation' then amenity_count else 0 end) as recreation_count, + sum(amenity_count) as total_amenities + from amenities + group by neighbourhood_id, amenity_year +), + +amenity_scores as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + n.population, + n.land_area_sqkm, + + coalesce(a.year, 2021) as year, + + -- Raw counts + a.parks_count, + a.schools_count, + a.transit_count, + a.libraries_count, + a.community_centres_count, + a.recreation_count, + a.total_amenities, + + -- Per 1000 population + case when n.population > 0 + then round(a.parks_count::numeric / n.population * 1000, 3) + else null + end as parks_per_1000, + + case when n.population > 0 + then round(a.schools_count::numeric / n.population * 1000, 3) + else null + end as schools_per_1000, + + case when n.population > 0 + then round(a.transit_count::numeric / n.population * 1000, 3) + else null + end as transit_per_1000, + + case when n.population > 0 + then round(a.total_amenities::numeric / n.population * 1000, 3) + else null + end as total_amenities_per_1000, + + -- Per square km + case when n.land_area_sqkm > 0 + then round(a.total_amenities::numeric / n.land_area_sqkm, 2) + else null + end as amenities_per_sqkm + + from neighbourhoods n + left join amenities_by_year a on n.neighbourhood_id = a.neighbourhood_id +) + +select * from amenity_scores diff --git a/dbt/models/intermediate/int_neighbourhood__crime_summary.sql b/dbt/models/intermediate/int_neighbourhood__crime_summary.sql new file mode 100644 index 0000000..cf5c3c8 --- /dev/null +++ b/dbt/models/intermediate/int_neighbourhood__crime_summary.sql @@ -0,0 +1,83 @@ +-- Intermediate: Aggregated crime by neighbourhood with YoY change +-- Pivots crime types and calculates year-over-year trends +-- Grain: One row per neighbourhood per year + +with neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +crime as ( + select * from {{ ref('stg_toronto__crime') }} +), + +-- Aggregate crime types +crime_by_year as ( + select + neighbourhood_id, + crime_year as year, + sum(incident_count) as total_incidents, + sum(case when crime_type = 'Assault' then incident_count else 0 end) as assault_count, + sum(case when crime_type = 'Auto Theft' then incident_count else 0 end) as auto_theft_count, + sum(case when crime_type = 'Break and Enter' then incident_count else 0 end) as break_enter_count, + sum(case when crime_type = 'Robbery' then incident_count else 0 end) as robbery_count, + sum(case when crime_type = 'Theft Over' then incident_count else 0 end) as theft_over_count, + sum(case when crime_type = 'Homicide' then incident_count else 0 end) as homicide_count, + avg(rate_per_100k) as avg_rate_per_100k + from crime + group by neighbourhood_id, crime_year +), + +-- Add year-over-year changes +with_yoy as ( + select + c.*, + lag(c.total_incidents, 1) over ( + partition by c.neighbourhood_id + order by c.year + ) as prev_year_incidents, + round( + (c.total_incidents - lag(c.total_incidents, 1) over ( + partition by c.neighbourhood_id + order by c.year + ))::numeric / + nullif(lag(c.total_incidents, 1) over ( + partition by c.neighbourhood_id + order by c.year + ), 0) * 100, + 2 + ) as yoy_change_pct + from crime_by_year c +), + +crime_summary as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + n.population, + + w.year, + w.total_incidents, + w.assault_count, + w.auto_theft_count, + w.break_enter_count, + w.robbery_count, + w.theft_over_count, + w.homicide_count, + w.yoy_change_pct, + + -- Crime rate per 100K population (use source data avg, or calculate if population available) + coalesce( + w.avg_rate_per_100k, + case + when n.population > 0 + then round(w.total_incidents::numeric / n.population * 100000, 2) + else null + end + ) as crime_rate_per_100k + + from neighbourhoods n + inner join with_yoy w on n.neighbourhood_id = w.neighbourhood_id +) + +select * from crime_summary diff --git a/dbt/models/intermediate/int_neighbourhood__demographics.sql b/dbt/models/intermediate/int_neighbourhood__demographics.sql new file mode 100644 index 0000000..1791c3f --- /dev/null +++ b/dbt/models/intermediate/int_neighbourhood__demographics.sql @@ -0,0 +1,45 @@ +-- Intermediate: Combined census demographics by neighbourhood +-- Joins neighbourhoods with census data for demographic analysis +-- Grain: One row per neighbourhood per census year + +with neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +census as ( + select * from {{ ref('stg_toronto__census') }} +), + +demographics as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + n.land_area_sqkm, + + -- Use census_year from census data, or fall back to dim_neighbourhood's year + coalesce(c.census_year, n.census_year, 2021) as census_year, + c.population, + c.population_density, + c.median_household_income, + c.average_household_income, + c.median_age, + c.unemployment_rate, + c.pct_bachelors_or_higher as education_bachelors_pct, + c.average_dwelling_value, + + -- Tenure mix + c.pct_owner_occupied, + c.pct_renter_occupied, + + -- Income quintile (city-wide comparison) + ntile(5) over ( + partition by c.census_year + order by c.median_household_income + ) as income_quintile + + from neighbourhoods n + left join census c on n.neighbourhood_id = c.neighbourhood_id +) + +select * from demographics diff --git a/dbt/models/intermediate/int_neighbourhood__housing.sql b/dbt/models/intermediate/int_neighbourhood__housing.sql new file mode 100644 index 0000000..f7c0d59 --- /dev/null +++ b/dbt/models/intermediate/int_neighbourhood__housing.sql @@ -0,0 +1,56 @@ +-- Intermediate: Housing indicators by neighbourhood +-- Combines census housing data with allocated CMHC rental data +-- Grain: One row per neighbourhood per year + +with neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +census as ( + select * from {{ ref('stg_toronto__census') }} +), + +allocated_rentals as ( + select * from {{ ref('int_rentals__neighbourhood_allocated') }} +), + +housing as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + + coalesce(r.year, c.census_year, 2021) as year, + + -- Census housing metrics + c.pct_owner_occupied, + c.pct_renter_occupied, + c.average_dwelling_value, + c.median_household_income, + + -- Allocated rental metrics (weighted average from CMHC zones) + r.avg_rent_2bed, + r.vacancy_rate, + + -- Affordability calculations + case + when c.median_household_income > 0 and r.avg_rent_2bed > 0 + then round((r.avg_rent_2bed * 12 / c.median_household_income) * 100, 2) + else null + end as rent_to_income_pct, + + -- Affordability threshold (30% of income) + case + when c.median_household_income > 0 and r.avg_rent_2bed > 0 + then r.avg_rent_2bed * 12 <= c.median_household_income * 0.30 + else null + end as is_affordable + + from neighbourhoods n + left join census c on n.neighbourhood_id = c.neighbourhood_id + left join allocated_rentals r + on n.neighbourhood_id = r.neighbourhood_id + and r.year = c.census_year +) + +select * from housing diff --git a/dbt/models/intermediate/int_rentals__annual.sql b/dbt/models/intermediate/int_rentals__annual.sql new file mode 100644 index 0000000..cd9fc87 --- /dev/null +++ b/dbt/models/intermediate/int_rentals__annual.sql @@ -0,0 +1,57 @@ +-- Intermediate: Annual rental data enriched with dimensions +-- Joins rentals with time and zone dimensions for analysis + +with rentals as ( + select * from {{ ref('stg_cmhc__rentals') }} +), + +time_dim as ( + select * from {{ ref('stg_dimensions__time') }} +), + +zone_dim as ( + select * from {{ ref('stg_dimensions__cmhc_zones') }} +), + +enriched as ( + select + r.rental_id, + + -- Time attributes + t.date_key, + t.full_date, + t.year, + t.month, + t.quarter, + + -- Zone attributes + z.zone_key, + z.zone_code, + z.zone_name, + + -- Bedroom type + r.bedroom_type, + + -- Metrics + r.rental_universe, + r.avg_rent, + r.median_rent, + r.vacancy_rate, + r.availability_rate, + r.turnover_rate, + r.year_over_year_rent_change, + r.reliability_code, + + -- Calculated metrics + case + when r.rental_universe > 0 and r.vacancy_rate is not null + then round(r.rental_universe * (r.vacancy_rate / 100), 0) + else null + end as vacant_units_estimate + + from rentals r + inner join time_dim t on r.date_key = t.date_key + inner join zone_dim z on r.zone_key = z.zone_key +) + +select * from enriched diff --git a/dbt/models/intermediate/int_rentals__neighbourhood_allocated.sql b/dbt/models/intermediate/int_rentals__neighbourhood_allocated.sql new file mode 100644 index 0000000..aa04534 --- /dev/null +++ b/dbt/models/intermediate/int_rentals__neighbourhood_allocated.sql @@ -0,0 +1,73 @@ +-- Intermediate: CMHC rentals allocated to neighbourhoods via area weights +-- Disaggregates zone-level rental data to neighbourhood level +-- Grain: One row per neighbourhood per year + +with crosswalk as ( + select * from {{ ref('stg_cmhc__zone_crosswalk') }} +), + +rentals as ( + select * from {{ ref('int_rentals__annual') }} +), + +neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +-- Allocate rental metrics to neighbourhoods using area weights +allocated as ( + select + c.neighbourhood_id, + r.year, + r.bedroom_type, + + -- Weighted average rent (using area weight) + sum(r.avg_rent * c.area_weight) as weighted_avg_rent, + sum(r.median_rent * c.area_weight) as weighted_median_rent, + sum(c.area_weight) as total_weight, + + -- Weighted vacancy rate + sum(r.vacancy_rate * c.area_weight) / nullif(sum(c.area_weight), 0) as vacancy_rate, + + -- Weighted rental universe + sum(r.rental_universe * c.area_weight) as rental_units_estimate + + from crosswalk c + inner join rentals r on c.cmhc_zone_code = r.zone_code + group by c.neighbourhood_id, r.year, r.bedroom_type +), + +-- Pivot to get 2-bedroom as primary metric +pivoted as ( + select + neighbourhood_id, + year, + max(case when bedroom_type = 'Two Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_2bed, + max(case when bedroom_type = 'One Bedroom' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_1bed, + max(case when bedroom_type = 'Bachelor' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_bachelor, + max(case when bedroom_type = 'Three Bedroom +' then weighted_avg_rent / nullif(total_weight, 0) end) as avg_rent_3bed, + avg(vacancy_rate) as vacancy_rate, + sum(rental_units_estimate) as total_rental_units + from allocated + group by neighbourhood_id, year +), + +final as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + + p.year, + round(p.avg_rent_bachelor::numeric, 2) as avg_rent_bachelor, + round(p.avg_rent_1bed::numeric, 2) as avg_rent_1bed, + round(p.avg_rent_2bed::numeric, 2) as avg_rent_2bed, + round(p.avg_rent_3bed::numeric, 2) as avg_rent_3bed, + round(p.vacancy_rate::numeric, 2) as vacancy_rate, + round(p.total_rental_units::numeric, 0) as total_rental_units + + from neighbourhoods n + inner join pivoted p on n.neighbourhood_id = p.neighbourhood_id +) + +select * from final diff --git a/dbt/models/intermediate/int_rentals__toronto_cma.sql b/dbt/models/intermediate/int_rentals__toronto_cma.sql new file mode 100644 index 0000000..66cc944 --- /dev/null +++ b/dbt/models/intermediate/int_rentals__toronto_cma.sql @@ -0,0 +1,25 @@ +-- Intermediate: Toronto CMA rental metrics by year +-- Aggregates rental data to city-wide averages by year +-- Source: StatCan CMHC data at CMA level +-- Grain: One row per year + +with rentals as ( + select * from {{ ref('stg_cmhc__rentals') }} +), + +-- Pivot bedroom types to columns +yearly_rentals as ( + select + year, + max(case when bedroom_type = 'bachelor' then avg_rent end) as avg_rent_bachelor, + max(case when bedroom_type = '1bed' then avg_rent end) as avg_rent_1bed, + max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_2bed, + max(case when bedroom_type = '3bed' then avg_rent end) as avg_rent_3bed, + -- Use 2-bedroom as standard reference + max(case when bedroom_type = '2bed' then avg_rent end) as avg_rent_standard, + max(vacancy_rate) as vacancy_rate + from rentals + group by year +) + +select * from yearly_rentals diff --git a/dbt/models/intermediate/int_year_spine.sql b/dbt/models/intermediate/int_year_spine.sql new file mode 100644 index 0000000..3760e1e --- /dev/null +++ b/dbt/models/intermediate/int_year_spine.sql @@ -0,0 +1,11 @@ +-- Intermediate: Year spine for analysis +-- Creates a row for each year from 2014-2025 +-- Used to drive time-series analysis across all data sources + +with years as ( + -- Generate years from available data sources + -- Crime data: 2014-2024, Rentals: 2019-2025 + select generate_series(2014, 2025) as year +) + +select year from years diff --git a/dbt/models/marts/.gitkeep b/dbt/models/marts/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt/models/marts/_marts.yml b/dbt/models/marts/_marts.yml new file mode 100644 index 0000000..37170ca --- /dev/null +++ b/dbt/models/marts/_marts.yml @@ -0,0 +1,135 @@ +version: 2 + +models: + - name: mart_toronto_rentals + description: "Final mart for Toronto rental market analysis by zone and time" + columns: + - name: rental_id + description: "Unique rental record identifier" + tests: + - unique + - not_null + + - name: mart_neighbourhood_overview + description: "Neighbourhood overview with composite livability score" + meta: + dashboard_tab: Overview + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry for mapping" + - name: livability_score + description: "Composite score: safety (30%), affordability (40%), amenities (30%)" + - name: safety_score + description: "Safety component score (0-100)" + - name: affordability_score + description: "Affordability component score (0-100)" + - name: amenity_score + description: "Amenity component score (0-100)" + + - name: mart_neighbourhood_housing + description: "Housing and affordability metrics by neighbourhood" + meta: + dashboard_tab: Housing + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry for mapping" + - name: rent_to_income_pct + description: "Rent as percentage of median income" + - name: affordability_index + description: "100 = city average affordability" + - name: rent_yoy_change_pct + description: "Year-over-year rent change" + + - name: mart_neighbourhood_safety + description: "Crime rates and safety metrics by neighbourhood" + meta: + dashboard_tab: Safety + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry for mapping" + - name: crime_rate_per_100k + description: "Total crime rate per 100K population" + - name: crime_index + description: "100 = city average crime rate" + - name: safety_tier + description: "Safety tier (1=safest, 5=highest crime)" + tests: + - accepted_values: + arguments: + values: [1, 2, 3, 4, 5] + + - name: mart_neighbourhood_demographics + description: "Demographics and income metrics by neighbourhood" + meta: + dashboard_tab: Demographics + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry for mapping" + - name: median_household_income + description: "Median household income" + - name: income_index + description: "100 = city average income" + - name: income_quintile + description: "Income quintile (1-5)" + tests: + - accepted_values: + arguments: + values: [1, 2, 3, 4, 5] + + - name: mart_neighbourhood_amenities + description: "Amenity access metrics by neighbourhood" + meta: + dashboard_tab: Amenities + columns: + - name: neighbourhood_id + description: "Neighbourhood identifier" + tests: + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry for mapping" + - name: total_amenities_per_1000 + description: "Total amenities per 1000 population" + - name: amenity_index + description: "100 = city average amenities" + - name: amenity_tier + description: "Amenity tier (1=best, 5=lowest)" + tests: + - accepted_values: + arguments: + values: [1, 2, 3, 4, 5] diff --git a/dbt/models/marts/mart_neighbourhood_amenities.sql b/dbt/models/marts/mart_neighbourhood_amenities.sql new file mode 100644 index 0000000..76f92f9 --- /dev/null +++ b/dbt/models/marts/mart_neighbourhood_amenities.sql @@ -0,0 +1,89 @@ +-- Mart: Neighbourhood Amenities Analysis +-- Dashboard Tab: Amenities +-- Grain: One row per neighbourhood per year + +with amenities as ( + select * from {{ ref('int_neighbourhood__amenity_scores') }} +), + +-- City-wide averages for comparison +city_avg as ( + select + year, + avg(parks_per_1000) as city_avg_parks, + avg(schools_per_1000) as city_avg_schools, + avg(transit_per_1000) as city_avg_transit, + avg(total_amenities_per_1000) as city_avg_total_amenities + from amenities + group by year +), + +final as ( + select + a.neighbourhood_id, + a.neighbourhood_name, + a.geometry, + a.population, + a.land_area_sqkm, + a.year, + + -- Raw counts + a.parks_count, + a.schools_count, + a.transit_count, + a.libraries_count, + a.community_centres_count, + a.recreation_count, + a.total_amenities, + + -- Per 1000 population + a.parks_per_1000, + a.schools_per_1000, + a.transit_per_1000, + a.total_amenities_per_1000, + + -- Per square km + a.amenities_per_sqkm, + + -- City averages + round(ca.city_avg_parks::numeric, 3) as city_avg_parks_per_1000, + round(ca.city_avg_schools::numeric, 3) as city_avg_schools_per_1000, + round(ca.city_avg_transit::numeric, 3) as city_avg_transit_per_1000, + + -- Amenity index (100 = city average) + case + when ca.city_avg_total_amenities > 0 + then round(a.total_amenities_per_1000 / ca.city_avg_total_amenities * 100, 1) + else null + end as amenity_index, + + -- Category indices + case + when ca.city_avg_parks > 0 + then round(a.parks_per_1000 / ca.city_avg_parks * 100, 1) + else null + end as parks_index, + + case + when ca.city_avg_schools > 0 + then round(a.schools_per_1000 / ca.city_avg_schools * 100, 1) + else null + end as schools_index, + + case + when ca.city_avg_transit > 0 + then round(a.transit_per_1000 / ca.city_avg_transit * 100, 1) + else null + end as transit_index, + + -- Amenity tier (1 = best, 5 = lowest) + ntile(5) over ( + partition by a.year + order by a.total_amenities_per_1000 desc + ) as amenity_tier + + from amenities a + left join city_avg ca on a.year = ca.year +) + +select * from final diff --git a/dbt/models/marts/mart_neighbourhood_demographics.sql b/dbt/models/marts/mart_neighbourhood_demographics.sql new file mode 100644 index 0000000..9a58e92 --- /dev/null +++ b/dbt/models/marts/mart_neighbourhood_demographics.sql @@ -0,0 +1,81 @@ +-- Mart: Neighbourhood Demographics Analysis +-- Dashboard Tab: Demographics +-- Grain: One row per neighbourhood per census year + +with demographics as ( + select * from {{ ref('int_neighbourhood__demographics') }} +), + +-- City-wide averages for comparison +city_avg as ( + select + census_year, + avg(median_household_income) as city_avg_income, + avg(median_age) as city_avg_age, + avg(unemployment_rate) as city_avg_unemployment, + avg(education_bachelors_pct) as city_avg_education, + avg(population_density) as city_avg_density + from demographics + group by census_year +), + +final as ( + select + d.neighbourhood_id, + d.neighbourhood_name, + d.geometry, + d.census_year as year, + + -- Population + d.population, + d.land_area_sqkm, + d.population_density, + + -- Income + d.median_household_income, + d.average_household_income, + d.income_quintile, + + -- Income index (100 = city average) + case + when ca.city_avg_income > 0 + then round(d.median_household_income / ca.city_avg_income * 100, 1) + else null + end as income_index, + + -- Demographics + d.median_age, + d.unemployment_rate, + d.education_bachelors_pct, + + -- Age index (100 = city average) + case + when ca.city_avg_age > 0 + then round(d.median_age / ca.city_avg_age * 100, 1) + else null + end as age_index, + + -- Housing tenure + d.pct_owner_occupied, + d.pct_renter_occupied, + d.average_dwelling_value, + + -- Diversity index (using tenure mix as proxy - higher rental = more diverse typically) + round( + 1 - ( + power(d.pct_owner_occupied / 100, 2) + + power(d.pct_renter_occupied / 100, 2) + ), + 3 + ) * 100 as tenure_diversity_index, + + -- City comparisons + round(ca.city_avg_income::numeric, 2) as city_avg_income, + round(ca.city_avg_age::numeric, 1) as city_avg_age, + round(ca.city_avg_unemployment::numeric, 2) as city_avg_unemployment + + from demographics d + left join city_avg ca on d.census_year = ca.census_year +) + +select * from final diff --git a/dbt/models/marts/mart_neighbourhood_housing.sql b/dbt/models/marts/mart_neighbourhood_housing.sql new file mode 100644 index 0000000..6b047ee --- /dev/null +++ b/dbt/models/marts/mart_neighbourhood_housing.sql @@ -0,0 +1,93 @@ +-- Mart: Neighbourhood Housing Analysis +-- Dashboard Tab: Housing +-- Grain: One row per neighbourhood per year + +with housing as ( + select * from {{ ref('int_neighbourhood__housing') }} +), + +rentals as ( + select * from {{ ref('int_rentals__neighbourhood_allocated') }} +), + +demographics as ( + select * from {{ ref('int_neighbourhood__demographics') }} +), + +-- Add year-over-year rent changes +with_yoy as ( + select + h.*, + r.avg_rent_bachelor, + r.avg_rent_1bed, + r.avg_rent_3bed, + r.total_rental_units, + d.income_quintile, + + -- Previous year rent for YoY calculation + lag(h.avg_rent_2bed, 1) over ( + partition by h.neighbourhood_id + order by h.year + ) as prev_year_rent_2bed + + from housing h + left join rentals r + on h.neighbourhood_id = r.neighbourhood_id + and h.year = r.year + left join demographics d + on h.neighbourhood_id = d.neighbourhood_id + and h.year = d.census_year +), + +final as ( + select + neighbourhood_id, + neighbourhood_name, + geometry, + year, + + -- Tenure mix + pct_owner_occupied, + pct_renter_occupied, + + -- Housing values + average_dwelling_value, + median_household_income, + + -- Rental metrics + avg_rent_bachelor, + avg_rent_1bed, + avg_rent_2bed, + avg_rent_3bed, + vacancy_rate, + total_rental_units, + + -- Affordability + rent_to_income_pct, + is_affordable, + + -- Affordability index (100 = city average) + round( + rent_to_income_pct / nullif( + avg(rent_to_income_pct) over (partition by year), + 0 + ) * 100, + 1 + ) as affordability_index, + + -- Year-over-year rent change + case + when prev_year_rent_2bed > 0 + then round( + (avg_rent_2bed - prev_year_rent_2bed) / prev_year_rent_2bed * 100, + 2 + ) + else null + end as rent_yoy_change_pct, + + income_quintile + + from with_yoy +) + +select * from final diff --git a/dbt/models/marts/mart_neighbourhood_overview.sql b/dbt/models/marts/mart_neighbourhood_overview.sql new file mode 100644 index 0000000..5a9c256 --- /dev/null +++ b/dbt/models/marts/mart_neighbourhood_overview.sql @@ -0,0 +1,152 @@ +-- Mart: Neighbourhood Overview with Composite Livability Score +-- Dashboard Tab: Overview +-- Grain: One row per neighbourhood per year +-- Time spine: Years 2014-2025 (driven by crime/rental data availability) + +with years as ( + select * from {{ ref('int_year_spine') }} +), + +neighbourhoods as ( + select * from {{ ref('stg_toronto__neighbourhoods') }} +), + +-- Create base: all neighbourhoods × all years +neighbourhood_years as ( + select + n.neighbourhood_id, + n.neighbourhood_name, + n.geometry, + y.year + from neighbourhoods n + cross join years y +), + +-- Census data (available for 2016, 2021) +-- For each year, use the most recent census data available +census as ( + select * from {{ ref('stg_toronto__census') }} +), + +census_mapped as ( + select + ny.neighbourhood_id, + ny.year, + c.population, + c.unemployment_rate, + c.pct_bachelors_or_higher as education_bachelors_pct + from neighbourhood_years ny + left join census c on ny.neighbourhood_id = c.neighbourhood_id + -- Use census year <= analysis year, prefer most recent + and c.census_year = ( + select max(c2.census_year) + from {{ ref('stg_toronto__census') }} c2 + where c2.neighbourhood_id = ny.neighbourhood_id + and c2.census_year <= ny.year + ) +), + +-- CMA-level census data (for income - not available at neighbourhood level) +cma_census as ( + select * from {{ ref('int_census__toronto_cma') }} +), + +-- Crime data (2014-2024) +crime as ( + select * from {{ ref('int_neighbourhood__crime_summary') }} +), + +-- Rentals (2019-2025) - CMA level applied to all neighbourhoods +rentals as ( + select * from {{ ref('int_rentals__toronto_cma') }} +), + +-- Compute scores +scored as ( + select + ny.neighbourhood_id, + ny.neighbourhood_name, + ny.geometry, + ny.year, + cm.population, + -- Use CMA-level income (neighbourhood-level not available in Toronto Open Data) + cma.median_household_income, + + -- Safety score: inverse of crime rate (higher = safer) + case + when cr.crime_rate_per_100k is not null + then 100 - percent_rank() over ( + partition by ny.year + order by cr.crime_rate_per_100k + ) * 100 + else null + end as safety_score, + + -- Affordability score: inverse of rent-to-income ratio + -- Using CMA-level income since neighbourhood-level not available + case + when cma.median_household_income > 0 and r.avg_rent_standard > 0 + then 100 - percent_rank() over ( + partition by ny.year + order by (r.avg_rent_standard * 12 / cma.median_household_income) + ) * 100 + else null + end as affordability_score, + + -- Raw metrics + cr.crime_rate_per_100k, + case + when cma.median_household_income > 0 and r.avg_rent_standard > 0 + then round((r.avg_rent_standard * 12 / cma.median_household_income) * 100, 2) + else null + end as rent_to_income_pct, + r.avg_rent_standard as avg_rent_2bed, + r.vacancy_rate + + from neighbourhood_years ny + left join census_mapped cm + on ny.neighbourhood_id = cm.neighbourhood_id + and ny.year = cm.year + left join cma_census cma + on ny.year = cma.year + left join crime cr + on ny.neighbourhood_id = cr.neighbourhood_id + and ny.year = cr.year + left join rentals r + on ny.year = r.year +), + +final as ( + select + neighbourhood_id, + neighbourhood_name, + geometry, + year, + population, + median_household_income, + + -- Component scores (0-100) + round(safety_score::numeric, 1) as safety_score, + round(affordability_score::numeric, 1) as affordability_score, + -- Amenity score not available at this level, use placeholder + 50.0 as amenity_score, + + -- Composite livability score: safety (40%), affordability (40%), amenities (20%) + round( + (coalesce(safety_score, 50) * 0.40 + + coalesce(affordability_score, 50) * 0.40 + + 50 * 0.20)::numeric, + 1 + ) as livability_score, + + -- Raw metrics + crime_rate_per_100k, + rent_to_income_pct, + avg_rent_2bed, + vacancy_rate, + null::numeric as total_amenities_per_1000 + + from scored +) + +select * from final diff --git a/dbt/models/marts/mart_neighbourhood_safety.sql b/dbt/models/marts/mart_neighbourhood_safety.sql new file mode 100644 index 0000000..fbd0d2c --- /dev/null +++ b/dbt/models/marts/mart_neighbourhood_safety.sql @@ -0,0 +1,78 @@ +-- Mart: Neighbourhood Safety Analysis +-- Dashboard Tab: Safety +-- Grain: One row per neighbourhood per year + +with crime as ( + select * from {{ ref('int_neighbourhood__crime_summary') }} +), + +-- City-wide averages for comparison +city_avg as ( + select + year, + avg(crime_rate_per_100k) as city_avg_crime_rate, + avg(assault_count) as city_avg_assault, + avg(auto_theft_count) as city_avg_auto_theft, + avg(break_enter_count) as city_avg_break_enter + from crime + group by year +), + +final as ( + select + c.neighbourhood_id, + c.neighbourhood_name, + c.geometry, + c.population, + c.year, + + -- Total crime + c.total_incidents, + c.crime_rate_per_100k, + c.yoy_change_pct as crime_yoy_change_pct, + + -- Crime breakdown + c.assault_count, + c.auto_theft_count, + c.break_enter_count, + c.robbery_count, + c.theft_over_count, + c.homicide_count, + + -- Per 100K rates by type + case when c.population > 0 + then round(c.assault_count::numeric / c.population * 100000, 2) + else null + end as assault_rate_per_100k, + + case when c.population > 0 + then round(c.auto_theft_count::numeric / c.population * 100000, 2) + else null + end as auto_theft_rate_per_100k, + + case when c.population > 0 + then round(c.break_enter_count::numeric / c.population * 100000, 2) + else null + end as break_enter_rate_per_100k, + + -- Comparison to city average + round(ca.city_avg_crime_rate::numeric, 2) as city_avg_crime_rate, + + -- Crime index (100 = city average) + case + when ca.city_avg_crime_rate > 0 + then round(c.crime_rate_per_100k / ca.city_avg_crime_rate * 100, 1) + else null + end as crime_index, + + -- Safety tier based on crime rate percentile + ntile(5) over ( + partition by c.year + order by c.crime_rate_per_100k desc + ) as safety_tier + + from crime c + left join city_avg ca on c.year = ca.year +) + +select * from final diff --git a/dbt/models/marts/mart_toronto_rentals.sql b/dbt/models/marts/mart_toronto_rentals.sql new file mode 100644 index 0000000..1785933 --- /dev/null +++ b/dbt/models/marts/mart_toronto_rentals.sql @@ -0,0 +1,64 @@ +-- Mart: Toronto Rental Market Analysis +-- Final analytical table for rental market visualization +-- Grain: One row per zone per bedroom type per survey year + +with rentals as ( + select * from {{ ref('int_rentals__annual') }} +), + +-- Add year-over-year calculations +with_yoy as ( + select + r.*, + + -- Previous year values + lag(r.avg_rent, 1) over ( + partition by r.zone_code, r.bedroom_type + order by r.year + ) as avg_rent_prev_year, + + lag(r.vacancy_rate, 1) over ( + partition by r.zone_code, r.bedroom_type + order by r.year + ) as vacancy_rate_prev_year + + from rentals r +), + +final as ( + select + rental_id, + date_key, + full_date, + year, + quarter, + zone_key, + zone_code, + zone_name, + bedroom_type, + rental_universe, + avg_rent, + median_rent, + vacancy_rate, + availability_rate, + turnover_rate, + year_over_year_rent_change, + reliability_code, + vacant_units_estimate, + + -- Calculated year-over-year (if not provided) + coalesce( + year_over_year_rent_change, + case + when avg_rent_prev_year > 0 + then round(((avg_rent - avg_rent_prev_year) / avg_rent_prev_year) * 100, 2) + else null + end + ) as rent_change_pct, + + vacancy_rate - vacancy_rate_prev_year as vacancy_rate_change + + from with_yoy +) + +select * from final diff --git a/dbt/models/staging/.gitkeep b/dbt/models/staging/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dbt/models/staging/_sources.yml b/dbt/models/staging/_sources.yml new file mode 100644 index 0000000..74f4888 --- /dev/null +++ b/dbt/models/staging/_sources.yml @@ -0,0 +1,99 @@ +version: 2 + +sources: + - name: toronto_housing + description: "Toronto housing data loaded from CMHC and City of Toronto sources" + database: portfolio + schema: public + tables: + - name: fact_rentals + description: "CMHC annual rental survey data by zone and bedroom type" + columns: + - name: id + description: "Primary key" + - name: date_key + description: "Foreign key to dim_time" + - name: zone_key + description: "Foreign key to dim_cmhc_zone" + + - name: dim_time + description: "Time dimension (monthly grain)" + columns: + - name: date_key + description: "Primary key (YYYYMMDD format)" + + - name: dim_cmhc_zone + description: "CMHC zone dimension with geometry" + columns: + - name: zone_key + description: "Primary key" + - name: zone_code + description: "CMHC zone code" + + - name: dim_neighbourhood + description: "City of Toronto neighbourhoods (158 official boundaries)" + columns: + - name: neighbourhood_id + description: "Primary key" + + - name: dim_policy_event + description: "Housing policy events for annotation" + columns: + - name: event_id + description: "Primary key" + + - name: fact_census + description: "Census demographics by neighbourhood and year" + columns: + - name: id + description: "Primary key" + - name: neighbourhood_id + description: "Foreign key to dim_neighbourhood" + - name: census_year + description: "Census year (2016, 2021, etc.)" + - name: population + description: "Total population" + - name: median_household_income + description: "Median household income" + + - name: fact_crime + description: "Crime statistics by neighbourhood, year, and type" + columns: + - name: id + description: "Primary key" + - name: neighbourhood_id + description: "Foreign key to dim_neighbourhood" + - name: year + description: "Statistics year" + - name: crime_type + description: "Type of crime" + - name: count + description: "Number of incidents" + - name: rate_per_100k + description: "Rate per 100,000 population" + + - name: fact_amenities + description: "Amenity counts by neighbourhood and type" + columns: + - name: id + description: "Primary key" + - name: neighbourhood_id + description: "Foreign key to dim_neighbourhood" + - name: amenity_type + description: "Type of amenity (parks, schools, transit)" + - name: count + description: "Number of amenities" + - name: year + description: "Reference year" + + - name: bridge_cmhc_neighbourhood + description: "CMHC zone to neighbourhood mapping with area weights" + columns: + - name: id + description: "Primary key" + - name: cmhc_zone_code + description: "CMHC zone code" + - name: neighbourhood_id + description: "Neighbourhood ID" + - name: weight + description: "Proportional area weight (0-1)" diff --git a/dbt/models/staging/_staging.yml b/dbt/models/staging/_staging.yml new file mode 100644 index 0000000..5d0dbeb --- /dev/null +++ b/dbt/models/staging/_staging.yml @@ -0,0 +1,129 @@ +version: 2 + +models: + - name: stg_cmhc__rentals + description: "Staged CMHC rental market data from fact_rentals" + columns: + - name: rental_id + description: "Unique identifier for rental record" + tests: + - unique + - not_null + - name: date_key + description: "Date dimension key (YYYYMMDD)" + tests: + - not_null + - name: zone_key + description: "CMHC zone dimension key" + tests: + - not_null + + - name: stg_dimensions__time + description: "Staged time dimension" + columns: + - name: date_key + description: "Date dimension key (YYYYMMDD)" + tests: + - unique + - not_null + + - name: stg_dimensions__cmhc_zones + description: "Staged CMHC zone dimension" + columns: + - name: zone_key + description: "Zone dimension key" + tests: + - unique + - not_null + - name: zone_code + description: "CMHC zone code" + tests: + - unique + - not_null + + - name: stg_toronto__neighbourhoods + description: "Staged Toronto neighbourhood dimension (158 official boundaries)" + columns: + - name: neighbourhood_id + description: "Neighbourhood primary key" + tests: + - unique + - not_null + - name: neighbourhood_name + description: "Official neighbourhood name" + tests: + - not_null + - name: geometry + description: "PostGIS geometry (POLYGON)" + + - name: stg_toronto__census + description: "Staged census demographics by neighbourhood" + columns: + - name: census_id + description: "Census record identifier" + tests: + - unique + - not_null + - name: neighbourhood_id + description: "Neighbourhood foreign key" + tests: + - not_null + - name: census_year + description: "Census year (2016, 2021)" + tests: + - not_null + + - name: stg_toronto__crime + description: "Staged crime statistics by neighbourhood" + columns: + - name: crime_id + description: "Crime record identifier" + tests: + - unique + - not_null + - name: neighbourhood_id + description: "Neighbourhood foreign key" + tests: + - not_null + - name: crime_type + description: "Type of crime" + tests: + - not_null + + - name: stg_toronto__amenities + description: "Staged amenity counts by neighbourhood" + columns: + - name: amenity_id + description: "Amenity record identifier" + tests: + - unique + - not_null + - name: neighbourhood_id + description: "Neighbourhood foreign key" + tests: + - not_null + - name: amenity_type + description: "Type of amenity" + tests: + - not_null + + - name: stg_cmhc__zone_crosswalk + description: "Staged CMHC zone to neighbourhood crosswalk with area weights" + columns: + - name: crosswalk_id + description: "Crosswalk record identifier" + tests: + - unique + - not_null + - name: cmhc_zone_code + description: "CMHC zone code" + tests: + - not_null + - name: neighbourhood_id + description: "Neighbourhood foreign key" + tests: + - not_null + - name: area_weight + description: "Proportional area weight (0-1)" + tests: + - not_null diff --git a/dbt/models/staging/stg_cmhc__rentals.sql b/dbt/models/staging/stg_cmhc__rentals.sql new file mode 100644 index 0000000..d2d181e --- /dev/null +++ b/dbt/models/staging/stg_cmhc__rentals.sql @@ -0,0 +1,31 @@ +-- Staged CMHC rental market survey data +-- Source: fact_rentals table loaded from CMHC/StatCan +-- Grain: One row per zone per bedroom type per survey year + +with source as ( + select + f.*, + t.year as survey_year + from {{ source('toronto_housing', 'fact_rentals') }} f + join {{ source('toronto_housing', 'dim_time') }} t on f.date_key = t.date_key +), + +staged as ( + select + id as rental_id, + date_key, + zone_key, + survey_year as year, + bedroom_type, + universe as rental_universe, + avg_rent, + median_rent, + vacancy_rate, + availability_rate, + turnover_rate, + rent_change_pct as year_over_year_rent_change, + reliability_code + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_cmhc__zone_crosswalk.sql b/dbt/models/staging/stg_cmhc__zone_crosswalk.sql new file mode 100644 index 0000000..d54148e --- /dev/null +++ b/dbt/models/staging/stg_cmhc__zone_crosswalk.sql @@ -0,0 +1,18 @@ +-- Staged CMHC zone to neighbourhood crosswalk +-- Source: bridge_cmhc_neighbourhood table +-- Grain: One row per zone-neighbourhood intersection + +with source as ( + select * from {{ source('toronto_housing', 'bridge_cmhc_neighbourhood') }} +), + +staged as ( + select + id as crosswalk_id, + cmhc_zone_code, + neighbourhood_id, + weight as area_weight + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_dimensions__cmhc_zones.sql b/dbt/models/staging/stg_dimensions__cmhc_zones.sql new file mode 100644 index 0000000..6ef3344 --- /dev/null +++ b/dbt/models/staging/stg_dimensions__cmhc_zones.sql @@ -0,0 +1,18 @@ +-- Staged CMHC zone dimension +-- Source: dim_cmhc_zone table +-- Grain: One row per zone + +with source as ( + select * from {{ source('toronto_housing', 'dim_cmhc_zone') }} +), + +staged as ( + select + zone_key, + zone_code, + zone_name, + geometry + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_dimensions__time.sql b/dbt/models/staging/stg_dimensions__time.sql new file mode 100644 index 0000000..c693af9 --- /dev/null +++ b/dbt/models/staging/stg_dimensions__time.sql @@ -0,0 +1,21 @@ +-- Staged time dimension +-- Source: dim_time table +-- Grain: One row per month + +with source as ( + select * from {{ source('toronto_housing', 'dim_time') }} +), + +staged as ( + select + date_key, + full_date, + year, + month, + quarter, + month_name, + is_month_start + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_toronto__amenities.sql b/dbt/models/staging/stg_toronto__amenities.sql new file mode 100644 index 0000000..c891692 --- /dev/null +++ b/dbt/models/staging/stg_toronto__amenities.sql @@ -0,0 +1,19 @@ +-- Staged amenity counts by neighbourhood +-- Source: fact_amenities table +-- Grain: One row per neighbourhood per amenity type per year + +with source as ( + select * from {{ source('toronto_housing', 'fact_amenities') }} +), + +staged as ( + select + id as amenity_id, + neighbourhood_id, + amenity_type, + count as amenity_count, + year as amenity_year + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_toronto__census.sql b/dbt/models/staging/stg_toronto__census.sql new file mode 100644 index 0000000..66bea60 --- /dev/null +++ b/dbt/models/staging/stg_toronto__census.sql @@ -0,0 +1,27 @@ +-- Staged census demographics by neighbourhood +-- Source: fact_census table +-- Grain: One row per neighbourhood per census year + +with source as ( + select * from {{ source('toronto_housing', 'fact_census') }} +), + +staged as ( + select + id as census_id, + neighbourhood_id, + census_year, + population, + population_density, + median_household_income, + average_household_income, + unemployment_rate, + pct_bachelors_or_higher, + pct_owner_occupied, + pct_renter_occupied, + median_age, + average_dwelling_value + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_toronto__crime.sql b/dbt/models/staging/stg_toronto__crime.sql new file mode 100644 index 0000000..1ebde64 --- /dev/null +++ b/dbt/models/staging/stg_toronto__crime.sql @@ -0,0 +1,20 @@ +-- Staged crime statistics by neighbourhood +-- Source: fact_crime table +-- Grain: One row per neighbourhood per year per crime type + +with source as ( + select * from {{ source('toronto_housing', 'fact_crime') }} +), + +staged as ( + select + id as crime_id, + neighbourhood_id, + year as crime_year, + crime_type, + count as incident_count, + rate_per_100k + from source +) + +select * from staged diff --git a/dbt/models/staging/stg_toronto__neighbourhoods.sql b/dbt/models/staging/stg_toronto__neighbourhoods.sql new file mode 100644 index 0000000..90d7f0b --- /dev/null +++ b/dbt/models/staging/stg_toronto__neighbourhoods.sql @@ -0,0 +1,25 @@ +-- Staged Toronto neighbourhood dimension +-- Source: dim_neighbourhood table +-- Grain: One row per neighbourhood (158 total) + +with source as ( + select * from {{ source('toronto_housing', 'dim_neighbourhood') }} +), + +staged as ( + select + neighbourhood_id, + name as neighbourhood_name, + geometry, + population, + land_area_sqkm, + pop_density_per_sqkm, + pct_bachelors_or_higher, + median_household_income, + pct_owner_occupied, + pct_renter_occupied, + census_year + from source +) + +select * from staged diff --git a/dbt/package-lock.yml b/dbt/package-lock.yml new file mode 100644 index 0000000..28a757f --- /dev/null +++ b/dbt/package-lock.yml @@ -0,0 +1,11 @@ +packages: + - name: dbt_utils + package: dbt-labs/dbt_utils + version: 1.3.3 + - name: dbt_expectations + package: calogica/dbt_expectations + version: 0.10.4 + - name: dbt_date + package: calogica/dbt_date + version: 0.10.1 +sha1_hash: 51a51ab489f7b302c8745ae3c3781271816b01be diff --git a/dbt/packages.yml b/dbt/packages.yml new file mode 100644 index 0000000..52f4efb --- /dev/null +++ b/dbt/packages.yml @@ -0,0 +1,5 @@ +packages: + - package: dbt-labs/dbt_utils + version: ">=1.0.0" + - package: calogica/dbt_expectations + version: ">=0.10.0" diff --git a/dbt/profiles.yml b/dbt/profiles.yml new file mode 100644 index 0000000..360fbc2 --- /dev/null +++ b/dbt/profiles.yml @@ -0,0 +1,21 @@ +toronto_housing: + target: dev + outputs: + dev: + type: postgres + host: localhost + user: portfolio + password: "{{ env_var('POSTGRES_PASSWORD') }}" + port: 5432 + dbname: portfolio + schema: public + threads: 4 + prod: + type: postgres + host: "{{ env_var('POSTGRES_HOST') }}" + user: "{{ env_var('POSTGRES_USER') }}" + password: "{{ env_var('POSTGRES_PASSWORD') }}" + port: 5432 + dbname: portfolio + schema: public + threads: 4 diff --git a/dbt/tests/.gitkeep b/dbt/tests/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..e598c2f --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,22 @@ +services: + db: + image: ${POSTGIS_IMAGE:-postgis/postgis:16-3.4} + container_name: portfolio-db + restart: unless-stopped + ports: + - "5432:5432" + environment: + POSTGRES_USER: ${POSTGRES_USER:-portfolio} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-portfolio_dev} + POSTGRES_DB: ${POSTGRES_DB:-portfolio} + volumes: + - postgres_data:/var/lib/postgresql/data + - ./scripts/db/init-postgis.sql:/docker-entrypoint-initdb.d/init-postgis.sql:ro + healthcheck: + test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-portfolio} -d ${POSTGRES_DB:-portfolio}"] + interval: 10s + timeout: 5s + retries: 5 + +volumes: + postgres_data: diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md new file mode 100644 index 0000000..cfe3507 --- /dev/null +++ b/docs/CONTRIBUTING.md @@ -0,0 +1,492 @@ +# Developer Guide + +Instructions for contributing to the Analytics Portfolio project. + +--- + +## Table of Contents + +1. [Development Setup](#development-setup) +2. [Adding a Blog Post](#adding-a-blog-post) +3. [Adding a New Page](#adding-a-new-page) +4. [Adding a Dashboard Tab](#adding-a-dashboard-tab) +5. [Creating Figure Factories](#creating-figure-factories) +6. [Branch Workflow](#branch-workflow) +7. [Code Standards](#code-standards) + +--- + +## Development Setup + +### Prerequisites + +- Python 3.11+ (via pyenv) +- Docker and Docker Compose +- Git + +### Initial Setup + +```bash +# Clone repository +git clone https://gitea.hotserv.cloud/lmiranda/personal-portfolio.git +cd personal-portfolio + +# Run setup (creates venv, installs deps, copies .env.example) +make setup + +# Start PostgreSQL + PostGIS +make docker-up + +# Initialize database +make db-init + +# Start development server +make run +``` + +The app runs at `http://localhost:8050`. + +### Useful Commands + +```bash +make test # Run tests +make test-cov # Run tests with coverage +make lint # Check code style +make format # Auto-format code +make typecheck # Run mypy type checker +make ci # Run all checks (lint, typecheck, test) +make dbt-run # Run dbt transformations +make dbt-test # Run dbt tests +``` + +--- + +## Adding a Blog Post + +Blog posts are Markdown files with YAML frontmatter, stored in `portfolio_app/content/blog/`. + +### Step 1: Create the Markdown File + +Create a new file in `portfolio_app/content/blog/`: + +```bash +touch portfolio_app/content/blog/your-article-slug.md +``` + +The filename becomes the URL slug: `/blog/your-article-slug` + +### Step 2: Add Frontmatter + +Every blog post requires YAML frontmatter at the top: + +```markdown +--- +title: "Your Article Title" +date: "2026-01-17" +description: "A brief description for the article card (1-2 sentences)" +tags: + - data-engineering + - python + - lessons-learned +status: published +--- + +Your article content starts here... +``` + +**Required fields:** + +| Field | Description | +|-------|-------------| +| `title` | Article title (displayed on cards and page) | +| `date` | Publication date in `YYYY-MM-DD` format | +| `description` | Short summary for article listing cards | +| `tags` | List of tags (displayed as badges) | +| `status` | `published` or `draft` (drafts are hidden from listing) | + +### Step 3: Write Content + +Use standard Markdown: + +```markdown +## Section Heading + +Regular paragraph text. + +### Subsection + +- Bullet points +- Another point + +```python +# Code blocks with syntax highlighting +def example(): + return "Hello" +``` + +**Bold text** and *italic text*. + +> Blockquotes for callouts +``` + +### Step 4: Test Locally + +```bash +make run +``` + +Visit `http://localhost:8050/blog` to see the article listing. +Visit `http://localhost:8050/blog/your-article-slug` for the full article. + +### Example: Complete Blog Post + +```markdown +--- +title: "Building ETL Pipelines with Python" +date: "2026-01-17" +description: "Lessons from building production data pipelines at scale" +tags: + - python + - etl + - data-engineering +status: published +--- + +When I started building data pipelines, I made every mistake possible... + +## The Problem + +Most tutorials show toy examples. Real pipelines are different. + +### Error Handling + +```python +def safe_transform(df: pd.DataFrame) -> pd.DataFrame: + try: + return df.apply(transform_row, axis=1) + except ValueError as e: + logger.error(f"Transform failed: {e}") + raise +``` + +## Conclusion + +Ship something that works, then iterate. +``` + +--- + +## Adding a New Page + +Pages use Dash's automatic routing based on file location in `portfolio_app/pages/`. + +### Step 1: Create the Page File + +```bash +touch portfolio_app/pages/your_page.py +``` + +### Step 2: Register the Page + +Every page must call `dash.register_page()`: + +```python +"""Your page description.""" + +import dash +import dash_mantine_components as dmc + +dash.register_page( + __name__, + path="/your-page", # URL path + name="Your Page", # Display name (for nav) + title="Your Page Title" # Browser tab title +) + + +def layout() -> dmc.Container: + """Page layout function.""" + return dmc.Container( + dmc.Stack( + [ + dmc.Title("Your Page", order=1), + dmc.Text("Page content here."), + ], + gap="lg", + ), + size="md", + py="xl", + ) +``` + +### Step 3: Page with Dynamic Content + +For pages with URL parameters: + +```python +# pages/blog/article.py +dash.register_page( + __name__, + path_template="/blog/", # Dynamic parameter + name="Article", +) + + +def layout(slug: str = "") -> dmc.Container: + """Layout receives URL parameters as arguments.""" + article = get_article(slug) + if not article: + return dmc.Text("Article not found") + + return dmc.Container( + dmc.Title(article["meta"]["title"]), + # ... + ) +``` + +### Step 4: Add Navigation (Optional) + +To add the page to the sidebar, edit `portfolio_app/components/sidebar.py`: + +```python +# For main pages (Home, About, Blog, etc.) +NAV_ITEMS_MAIN = [ + {"path": "/", "icon": "tabler:home", "label": "Home"}, + {"path": "/your-page", "icon": "tabler:star", "label": "Your Page"}, + # ... +] + +# For project/dashboard pages +NAV_ITEMS_PROJECTS = [ + {"path": "/projects", "icon": "tabler:folder", "label": "Projects"}, + {"path": "/your-dashboard", "icon": "tabler:chart-bar", "label": "Your Dashboard"}, + # ... +] +``` + +The sidebar uses icon buttons with tooltips. Each item needs `path`, `icon` (Tabler icon name), and `label` (tooltip text). + +### URL Routing Summary + +| File Location | URL | +|---------------|-----| +| `pages/home.py` | `/` (if `path="/"`) | +| `pages/about.py` | `/about` | +| `pages/blog/index.py` | `/blog` | +| `pages/blog/article.py` | `/blog/` | +| `pages/toronto/dashboard.py` | `/toronto` | + +--- + +## Adding a Dashboard Tab + +Dashboard tabs are in `portfolio_app/pages/toronto/tabs/`. + +### Step 1: Create Tab Layout + +```python +# pages/toronto/tabs/your_tab.py +"""Your tab description.""" + +import dash_mantine_components as dmc + +from portfolio_app.figures.choropleth import create_choropleth +from portfolio_app.toronto.demo_data import get_demo_data + + +def create_your_tab_layout() -> dmc.Stack: + """Create the tab layout.""" + data = get_demo_data() + + return dmc.Stack( + [ + dmc.Grid( + [ + dmc.GridCol( + # Map on left + create_choropleth(data, "your_metric"), + span=8, + ), + dmc.GridCol( + # KPI cards on right + create_kpi_cards(data), + span=4, + ), + ], + ), + # Charts below + create_supporting_charts(data), + ], + gap="lg", + ) +``` + +### Step 2: Register in Dashboard + +Edit `pages/toronto/dashboard.py` to add the tab: + +```python +from portfolio_app.pages.toronto.tabs.your_tab import create_your_tab_layout + +# In the tabs list: +dmc.TabsTab("Your Tab", value="your-tab"), + +# In the panels: +dmc.TabsPanel(create_your_tab_layout(), value="your-tab"), +``` + +--- + +## Creating Figure Factories + +Figure factories are in `portfolio_app/figures/`. They create reusable Plotly figures. + +### Pattern + +```python +# figures/your_chart.py +"""Your chart type factory.""" + +import plotly.express as px +import plotly.graph_objects as go +import pandas as pd + + +def create_your_chart( + df: pd.DataFrame, + x_col: str, + y_col: str, + title: str = "", +) -> go.Figure: + """Create a your_chart figure. + + Args: + df: DataFrame with data. + x_col: Column for x-axis. + y_col: Column for y-axis. + title: Optional chart title. + + Returns: + Configured Plotly figure. + """ + fig = px.bar(df, x=x_col, y=y_col, title=title) + + fig.update_layout( + template="plotly_white", + margin=dict(l=40, r=40, t=40, b=40), + ) + + return fig +``` + +### Export from `__init__.py` + +```python +# figures/__init__.py +from .your_chart import create_your_chart + +__all__ = [ + "create_your_chart", + # ... +] +``` + +--- + +## Branch Workflow + +``` +main (production) + ↑ +staging (pre-production) + ↑ +development (integration) + ↑ +feature/XX-description (your work) +``` + +### Creating a Feature Branch + +```bash +# Start from development +git checkout development +git pull origin development + +# Create feature branch +git checkout -b feature/10-add-new-page + +# Work, commit, push +git add . +git commit -m "feat: Add new page" +git push -u origin feature/10-add-new-page +``` + +### Merging + +```bash +# Merge into development +git checkout development +git merge feature/10-add-new-page +git push origin development + +# Delete feature branch +git branch -d feature/10-add-new-page +git push origin --delete feature/10-add-new-page +``` + +**Rules:** +- Never commit directly to `main` or `staging` +- Never delete `development` +- Feature branches are temporary + +--- + +## Code Standards + +### Type Hints + +Use Python 3.10+ style: + +```python +def process(items: list[str], config: dict[str, int] | None = None) -> bool: + ... +``` + +### Imports + +| Context | Style | +|---------|-------| +| Same directory | `from .module import X` | +| Sibling directory | `from ..schemas.model import Y` | +| External packages | `import pandas as pd` | + +### Formatting + +```bash +make format # Runs ruff formatter +make lint # Checks style +``` + +### Docstrings + +Google style, only for non-obvious functions: + +```python +def calculate_score(values: list[float], weights: list[float]) -> float: + """Calculate weighted score. + + Args: + values: Raw metric values. + weights: Weight for each metric. + + Returns: + Weighted average score. + """ + ... +``` + +--- + +## Questions? + +Check `CLAUDE.md` for AI assistant context and architectural decisions. diff --git a/docs/DATABASE_SCHEMA.md b/docs/DATABASE_SCHEMA.md new file mode 100644 index 0000000..4ecf5f3 --- /dev/null +++ b/docs/DATABASE_SCHEMA.md @@ -0,0 +1,313 @@ +# Database Schema + +This document describes the PostgreSQL/PostGIS database schema for the Toronto Neighbourhood Dashboard. + +## Entity Relationship Diagram + +```mermaid +erDiagram + dim_time { + int date_key PK + date full_date UK + int year + int month + int quarter + string month_name + bool is_month_start + } + + dim_cmhc_zone { + int zone_key PK + string zone_code UK + string zone_name + geometry geometry + } + + dim_neighbourhood { + int neighbourhood_id PK + string name + geometry geometry + int population + numeric land_area_sqkm + numeric pop_density_per_sqkm + numeric pct_bachelors_or_higher + numeric median_household_income + numeric pct_owner_occupied + numeric pct_renter_occupied + int census_year + } + + dim_policy_event { + int event_id PK + date event_date + date effective_date + string level + string category + string title + text description + string expected_direction + string source_url + string confidence + } + + fact_rentals { + int id PK + int date_key FK + int zone_key FK + string bedroom_type + int universe + numeric avg_rent + numeric median_rent + numeric vacancy_rate + numeric availability_rate + numeric turnover_rate + numeric rent_change_pct + string reliability_code + } + + fact_census { + int id PK + int neighbourhood_id FK + int census_year + int population + numeric population_density + numeric median_household_income + numeric average_household_income + numeric unemployment_rate + numeric pct_bachelors_or_higher + numeric pct_owner_occupied + numeric pct_renter_occupied + numeric median_age + numeric average_dwelling_value + } + + fact_crime { + int id PK + int neighbourhood_id FK + int year + string crime_type + int count + numeric rate_per_100k + } + + fact_amenities { + int id PK + int neighbourhood_id FK + string amenity_type + int count + int year + } + + bridge_cmhc_neighbourhood { + int id PK + string cmhc_zone_code FK + int neighbourhood_id FK + numeric weight + } + + dim_time ||--o{ fact_rentals : "date_key" + dim_cmhc_zone ||--o{ fact_rentals : "zone_key" + dim_neighbourhood ||--o{ fact_census : "neighbourhood_id" + dim_neighbourhood ||--o{ fact_crime : "neighbourhood_id" + dim_neighbourhood ||--o{ fact_amenities : "neighbourhood_id" + dim_cmhc_zone ||--o{ bridge_cmhc_neighbourhood : "zone_code" + dim_neighbourhood ||--o{ bridge_cmhc_neighbourhood : "neighbourhood_id" +``` + +## Schema Layers + +### Raw Schema + +Raw data is loaded directly from external sources without transformation: + +| Table | Source | Description | +|-------|--------|-------------| +| `raw.neighbourhoods` | City of Toronto API | GeoJSON neighbourhood boundaries | +| `raw.census_profiles` | City of Toronto API | Census profile data | +| `raw.crime_data` | Toronto Police API | Crime statistics by neighbourhood | +| `raw.cmhc_rentals` | CMHC Data Files | Rental market survey data | + +### Staging Schema (dbt) + +Staging models provide 1:1 cleaned representations of source data: + +| Model | Source Table | Purpose | +|-------|-------------|---------| +| `stg_toronto__neighbourhoods` | raw.neighbourhoods | Cleaned boundaries with standardized names | +| `stg_toronto__census` | raw.census_profiles | Typed census metrics | +| `stg_cmhc__rentals` | raw.cmhc_rentals | Validated rental data | +| `stg_toronto__crime` | raw.crime_data | Standardized crime categories | +| `stg_toronto__amenities` | raw.amenities | Typed amenity counts | +| `stg_dimensions__time` | generated | Time dimension | +| `stg_dimensions__cmhc_zones` | raw.cmhc_zones | CMHC zone boundaries | +| `stg_cmhc__zone_crosswalk` | raw.crosswalk | Zone-neighbourhood mapping | + +### Marts Schema (dbt) + +Analytical tables ready for dashboard consumption: + +| Model | Grain | Purpose | +|-------|-------|---------| +| `mart_neighbourhood_overview` | neighbourhood | Composite livability scores | +| `mart_neighbourhood_housing` | neighbourhood | Housing and rent metrics | +| `mart_neighbourhood_safety` | neighbourhood × year | Crime rate calculations | +| `mart_neighbourhood_demographics` | neighbourhood | Income, age, population metrics | +| `mart_neighbourhood_amenities` | neighbourhood | Amenity accessibility scores | +| `mart_toronto_rentals` | zone × month | Time-series rental analysis | + +## Table Details + +### Dimension Tables + +#### dim_time +Time dimension for date-based analysis. Grain: one row per month. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| date_key | INTEGER | PK | Surrogate key (YYYYMM format) | +| full_date | DATE | UNIQUE, NOT NULL | First day of month | +| year | INTEGER | NOT NULL | Calendar year | +| month | INTEGER | NOT NULL | Month number (1-12) | +| quarter | INTEGER | NOT NULL | Quarter (1-4) | +| month_name | VARCHAR(20) | NOT NULL | Month name | +| is_month_start | BOOLEAN | DEFAULT TRUE | Always true (monthly grain) | + +#### dim_cmhc_zone +CMHC rental market zones (~20 zones covering Toronto). + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| zone_key | INTEGER | PK, AUTO | Surrogate key | +| zone_code | VARCHAR(10) | UNIQUE, NOT NULL | CMHC zone identifier | +| zone_name | VARCHAR(100) | NOT NULL | Zone display name | +| geometry | GEOMETRY(POLYGON) | SRID 4326 | PostGIS zone boundary | + +#### dim_neighbourhood +Toronto's 158 official neighbourhoods. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| neighbourhood_id | INTEGER | PK | City-assigned ID | +| name | VARCHAR(100) | NOT NULL | Neighbourhood name | +| geometry | GEOMETRY(POLYGON) | SRID 4326 | PostGIS boundary | +| population | INTEGER | | Total population | +| land_area_sqkm | NUMERIC(10,4) | | Area in km² | +| pop_density_per_sqkm | NUMERIC(10,2) | | Population density | +| pct_bachelors_or_higher | NUMERIC(5,2) | | Education rate | +| median_household_income | NUMERIC(12,2) | | Median income | +| pct_owner_occupied | NUMERIC(5,2) | | Owner occupancy rate | +| pct_renter_occupied | NUMERIC(5,2) | | Renter occupancy rate | +| census_year | INTEGER | DEFAULT 2021 | Census reference year | + +#### dim_policy_event +Policy events for time-series annotation (rent control, interest rates, etc.). + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| event_id | INTEGER | PK, AUTO | Surrogate key | +| event_date | DATE | NOT NULL | Announcement date | +| effective_date | DATE | | Implementation date | +| level | VARCHAR(20) | NOT NULL | federal/provincial/municipal | +| category | VARCHAR(20) | NOT NULL | monetary/tax/regulatory/supply/economic | +| title | VARCHAR(200) | NOT NULL | Event title | +| description | TEXT | | Detailed description | +| expected_direction | VARCHAR(10) | NOT NULL | bearish/bullish/neutral | +| source_url | VARCHAR(500) | | Reference link | +| confidence | VARCHAR(10) | DEFAULT 'medium' | high/medium/low | + +### Fact Tables + +#### fact_rentals +CMHC rental market survey data. Grain: zone × bedroom type × survey date. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| id | INTEGER | PK, AUTO | Surrogate key | +| date_key | INTEGER | FK → dim_time | Survey date reference | +| zone_key | INTEGER | FK → dim_cmhc_zone | CMHC zone reference | +| bedroom_type | VARCHAR(20) | NOT NULL | bachelor/1-bed/2-bed/3+bed/total | +| universe | INTEGER | | Total rental units | +| avg_rent | NUMERIC(10,2) | | Average rent | +| median_rent | NUMERIC(10,2) | | Median rent | +| vacancy_rate | NUMERIC(5,2) | | Vacancy percentage | +| availability_rate | NUMERIC(5,2) | | Availability percentage | +| turnover_rate | NUMERIC(5,2) | | Turnover percentage | +| rent_change_pct | NUMERIC(5,2) | | Year-over-year change | +| reliability_code | VARCHAR(2) | | CMHC data quality code | + +#### fact_census +Census statistics. Grain: neighbourhood × census year. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| id | INTEGER | PK, AUTO | Surrogate key | +| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference | +| census_year | INTEGER | NOT NULL | 2016, 2021, etc. | +| population | INTEGER | | Total population | +| population_density | NUMERIC(10,2) | | People per km² | +| median_household_income | NUMERIC(12,2) | | Median income | +| average_household_income | NUMERIC(12,2) | | Average income | +| unemployment_rate | NUMERIC(5,2) | | Unemployment % | +| pct_bachelors_or_higher | NUMERIC(5,2) | | Education rate | +| pct_owner_occupied | NUMERIC(5,2) | | Owner rate | +| pct_renter_occupied | NUMERIC(5,2) | | Renter rate | +| median_age | NUMERIC(5,2) | | Median resident age | +| average_dwelling_value | NUMERIC(12,2) | | Average home value | + +#### fact_crime +Crime statistics. Grain: neighbourhood × year × crime type. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| id | INTEGER | PK, AUTO | Surrogate key | +| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference | +| year | INTEGER | NOT NULL | Calendar year | +| crime_type | VARCHAR(50) | NOT NULL | Crime category | +| count | INTEGER | NOT NULL | Number of incidents | +| rate_per_100k | NUMERIC(10,2) | | Rate per 100k population | + +#### fact_amenities +Amenity counts. Grain: neighbourhood × amenity type × year. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| id | INTEGER | PK, AUTO | Surrogate key | +| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference | +| amenity_type | VARCHAR(50) | NOT NULL | parks/schools/transit/etc. | +| count | INTEGER | NOT NULL | Number of amenities | +| year | INTEGER | NOT NULL | Reference year | + +### Bridge Tables + +#### bridge_cmhc_neighbourhood +Maps CMHC zones to neighbourhoods with area-based weights for data disaggregation. + +| Column | Type | Constraints | Description | +|--------|------|-------------|-------------| +| id | INTEGER | PK, AUTO | Surrogate key | +| cmhc_zone_code | VARCHAR(10) | FK → dim_cmhc_zone | Zone reference | +| neighbourhood_id | INTEGER | FK → dim_neighbourhood | Neighbourhood reference | +| weight | NUMERIC(5,4) | NOT NULL | Proportional weight (0-1) | + +## Indexes + +| Table | Index | Columns | Purpose | +|-------|-------|---------|---------| +| fact_rentals | ix_fact_rentals_date_zone | date_key, zone_key | Time-series queries | +| fact_census | ix_fact_census_neighbourhood_year | neighbourhood_id, census_year | Census lookups | +| fact_crime | ix_fact_crime_neighbourhood_year | neighbourhood_id, year | Crime trends | +| fact_crime | ix_fact_crime_type | crime_type | Crime filtering | +| fact_amenities | ix_fact_amenities_neighbourhood_year | neighbourhood_id, year | Amenity queries | +| fact_amenities | ix_fact_amenities_type | amenity_type | Amenity filtering | +| bridge_cmhc_neighbourhood | ix_bridge_cmhc_zone | cmhc_zone_code | Zone lookups | +| bridge_cmhc_neighbourhood | ix_bridge_neighbourhood | neighbourhood_id | Neighbourhood lookups | + +## PostGIS Extensions + +The database requires PostGIS for geospatial operations: + +```sql +CREATE EXTENSION IF NOT EXISTS postgis; +``` + +All geometry columns use SRID 4326 (WGS84) for compatibility with web mapping libraries. diff --git a/docs/PROJECT_REFERENCE.md b/docs/PROJECT_REFERENCE.md new file mode 100644 index 0000000..d9bec1b --- /dev/null +++ b/docs/PROJECT_REFERENCE.md @@ -0,0 +1,297 @@ +# Portfolio Project Reference + +**Project**: Analytics Portfolio +**Owner**: Leo Miranda +**Status**: Sprint 9 Complete (Dashboard Implementation Done) +**Last Updated**: January 2026 + +--- + +## Project Overview + +Personal portfolio website with an interactive Toronto Neighbourhood Dashboard demonstrating data engineering, visualization, and analytics capabilities. + +| Component | Description | Status | +|-----------|-------------|--------| +| Portfolio Website | Bio, About, Projects, Resume, Contact, Blog | Complete | +| Toronto Dashboard | 5-tab neighbourhood analysis | Complete | +| Data Pipeline | dbt models, figure factories | Complete | +| Deployment | Production deployment | Pending | + +--- + +## Completed Work + +### Sprint 1-6: Foundation +- Repository setup, Docker, PostgreSQL + PostGIS +- Bio landing page implementation +- Initial data model design + +### Sprint 7: Navigation & Theme +- Sidebar navigation +- Dark/light theme toggle +- dash-mantine-components integration + +### Sprint 8: Portfolio Website +- About, Contact, Projects, Resume pages +- Blog system with Markdown/frontmatter +- Health endpoint + +### Sprint 9: Neighbourhood Dashboard Transition +- Phase 1: Deleted legacy TRREB code +- Phase 2: Documentation cleanup +- Phase 3: New neighbourhood-centric data model +- Phase 4: dbt model restructuring +- Phase 5: 5-tab dashboard implementation +- Phase 6: 15 documentation notebooks +- Phase 7: Final documentation review + +--- + +## Application Architecture + +### URL Routes + +| URL | Page | File | +|-----|------|------| +| `/` | Home | `pages/home.py` | +| `/about` | About | `pages/about.py` | +| `/contact` | Contact | `pages/contact.py` | +| `/projects` | Projects | `pages/projects.py` | +| `/resume` | Resume | `pages/resume.py` | +| `/blog` | Blog listing | `pages/blog/index.py` | +| `/blog/{slug}` | Article | `pages/blog/article.py` | +| `/toronto` | Dashboard | `pages/toronto/dashboard.py` | +| `/toronto/methodology` | Methodology | `pages/toronto/methodology.py` | +| `/health` | Health check | `pages/health.py` | + +### Directory Structure + +``` +portfolio_app/ +├── app.py # Dash app factory +├── config.py # Pydantic BaseSettings +├── assets/ # CSS, images +├── callbacks/ # Global callbacks (sidebar, theme) +├── components/ # Shared UI components +├── content/blog/ # Markdown blog articles +├── errors/ # Exception handling +├── figures/ # Plotly figure factories +├── pages/ +│ ├── home.py +│ ├── about.py +│ ├── contact.py +│ ├── projects.py +│ ├── resume.py +│ ├── health.py +│ ├── blog/ +│ │ ├── index.py +│ │ └── article.py +│ └── toronto/ +│ ├── dashboard.py +│ ├── methodology.py +│ ├── tabs/ # 5 tab layouts +│ └── callbacks/ # Dashboard interactions (map_callbacks, chart_callbacks, selection_callbacks) +├── toronto/ # Data logic +│ ├── parsers/ # API extraction (geo, toronto_open_data, toronto_police, cmhc) +│ ├── loaders/ # Database operations (base, cmhc, cmhc_crosswalk) +│ ├── schemas/ # Pydantic models +│ ├── models/ # SQLAlchemy ORM +│ ├── services/ # Query functions (neighbourhood_service, geometry_service) +│ └── demo_data.py # Sample data +└── utils/ + └── markdown_loader.py # Blog article loading +``` + +--- + +## Toronto Dashboard + +### Data Sources + +| Source | Data | Format | +|--------|------|--------| +| City of Toronto Open Data | Neighbourhoods (158), Census profiles, Parks, Schools, Childcare, TTC | GeoJSON, CSV, API | +| Toronto Police Service | Crime rates, MCI, Shootings | CSV, API | +| CMHC | Rental Market Survey | CSV | + +### Geographic Model + +``` +City of Toronto Neighbourhoods (158) ← Primary analysis unit +CMHC Zones (~20) ← Rental data (Census Tract aligned) +``` + +### Dashboard Tabs + +| Tab | Choropleth Metric | Supporting Charts | +|-----|-------------------|-------------------| +| Overview | Livability score | Top/Bottom 10 bar, Income vs Safety scatter | +| Housing | Affordability index | Rent trend line, Tenure breakdown bar | +| Safety | Crime rate per 100K | Crime breakdown bar, Crime trend line | +| Demographics | Median income | Age distribution, Population density bar | +| Amenities | Amenity index | Amenity radar, Transit accessibility bar | + +### Star Schema + +| Table | Type | Description | +|-------|------|-------------| +| `dim_neighbourhood` | Dimension | 158 neighbourhoods with geometry | +| `dim_time` | Dimension | Date dimension | +| `dim_cmhc_zone` | Dimension | ~20 CMHC zones with geometry | +| `fact_census` | Fact | Census indicators by neighbourhood | +| `fact_crime` | Fact | Crime stats by neighbourhood | +| `fact_rentals` | Fact | Rental data by CMHC zone | +| `fact_amenities` | Fact | Amenity counts by neighbourhood | + +### dbt Layers + +| Layer | Naming | Example | +|-------|--------|---------| +| Staging | `stg_{source}__{entity}` | `stg_toronto__neighbourhoods` | +| Intermediate | `int_{domain}__{transform}` | `int_neighbourhood__demographics` | +| Marts | `mart_{domain}` | `mart_neighbourhood_overview` | + +--- + +## Tech Stack + +| Layer | Technology | Version | +|-------|------------|---------| +| Database | PostgreSQL + PostGIS | 16.x | +| Validation | Pydantic | 2.x | +| ORM | SQLAlchemy | 2.x | +| Transformation | dbt-postgres | 1.7+ | +| Data Processing | Pandas, GeoPandas | Latest | +| Visualization | Dash + Plotly | 2.14+ | +| UI Components | dash-mantine-components | Latest | +| Testing | pytest | 7.0+ | +| Python | 3.11+ | Via pyenv | + +--- + +## Branching Strategy + +| Branch | Purpose | Deploys To | +|--------|---------|------------| +| `main` | Production releases | VPS (production) | +| `staging` | Pre-production testing | VPS (staging) | +| `development` | Active development | Local only | + +**Rules:** +- Feature branches from `development`: `feature/{sprint}-{description}` +- Merge into `development` when complete +- `development` → `staging` → `main` for releases +- Never delete `development` + +--- + +## Code Standards + +### Type Hints (Python 3.10+) + +```python +def process(items: list[str], config: dict[str, int] | None = None) -> bool: + ... +``` + +### Imports + +| Context | Style | +|---------|-------| +| Same directory | `from .module import X` | +| Sibling directory | `from ..schemas.model import Y` | +| External | `import pandas as pd` | + +### Error Handling + +```python +class PortfolioError(Exception): + """Base exception.""" + +class ParseError(PortfolioError): + """Data parsing failed.""" + +class ValidationError(PortfolioError): + """Validation failed.""" + +class LoadError(PortfolioError): + """Database load failed.""" +``` + +--- + +## Environment Variables + +Required in `.env`: + +```bash +DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio +POSTGRES_USER=portfolio +POSTGRES_PASSWORD= +POSTGRES_DB=portfolio +DASH_DEBUG=true +SECRET_KEY= +LOG_LEVEL=INFO +``` + +--- + +## Makefile Targets + +| Target | Purpose | +|--------|---------| +| `setup` | Install deps, create .env, init pre-commit | +| `docker-up` | Start PostgreSQL + PostGIS (auto-detects x86/ARM) | +| `docker-down` | Stop containers | +| `docker-logs` | View container logs | +| `db-init` | Initialize database schema | +| `db-reset` | Drop and recreate database (DESTRUCTIVE) | +| `load-data` | Load Toronto data from APIs, seed dev data | +| `load-data-only` | Load Toronto data without dbt or seeding | +| `seed-data` | Seed sample development data | +| `run` | Start Dash dev server | +| `test` | Run pytest | +| `test-cov` | Run pytest with coverage | +| `lint` | Run ruff linter | +| `format` | Run ruff formatter | +| `typecheck` | Run mypy type checker | +| `ci` | Run all checks (lint, typecheck, test) | +| `dbt-run` | Run dbt models | +| `dbt-test` | Run dbt tests | +| `dbt-docs` | Generate and serve dbt documentation | +| `clean` | Remove build artifacts and caches | + +--- + +## Next Steps + +### Deployment (Sprint 10+) +- [ ] Production Docker configuration +- [ ] CI/CD pipeline +- [ ] HTTPS/SSL setup +- [ ] Domain configuration + +### Data Enhancement +- [ ] Connect to live APIs (currently using demo data) +- [ ] Data refresh automation +- [ ] Historical data loading + +### Future Projects +- Energy Pricing Analysis dashboard (planned) + +--- + +## Related Documents + +| Document | Purpose | +|----------|---------| +| `README.md` | Quick start guide | +| `CLAUDE.md` | AI assistant context | +| `docs/CONTRIBUTING.md` | Developer guide | +| `notebooks/README.md` | Notebook documentation | + +--- + +*Reference Version: 3.0* +*Updated: January 2026* diff --git a/docs/changes/Portfolio-Changes.txt b/docs/changes/Portfolio-Changes.txt new file mode 100644 index 0000000..68c5db9 --- /dev/null +++ b/docs/changes/Portfolio-Changes.txt @@ -0,0 +1,520 @@ +# Leo Miranda — Portfolio Website Blueprint + +Structure, navigation, and complete page content + +--- + +## Site Architecture + +``` +leodata.science +├── Home (Landing) +├── About +├── Projects (Overview + Status) +│ └── [Side Navbar] +│ ├── → Toronto Housing Market Dashboard (live) +│ ├── → US Retail Energy Price Predictor (coming soon) +│ └── → DataFlow Platform (Phase 3) +├── Lab (Bandit Labs / Experiments) +├── Blog +│ └── [Articles] +├── Resume (downloadable + inline) +└── Contact +``` + +--- + +## Navigation Structure + +Primary Nav: Home | Projects | Lab | Blog | About | Resume + +Footer: LinkedIn | GitHub | Email | “Built with Dash & too much coffee” + +--- + +# PAGE CONTENT + +--- + +## 1. HOME (Landing Page) + +### Hero Section + +Headline: + +> I turn messy data into systems that actually work. + +Subhead: + +> Data Engineer & Analytics Specialist. 8 years building pipelines, dashboards, and the infrastructure nobody sees but everyone depends on. Based in Toronto. + +CTA Buttons: + +- View Projects → /projects +- Get In Touch → /contact + +--- + +### Quick Impact Strip (Optional — 3-4 stats) + +| 1B+ | 40% | 5 Years | +|-------------------------------------------------|------------------------------------|-----------------------------| +| Rows processed daily across enterprise platform | Efficiency gain through automation | Building DataFlow from zero | + +--- + +### Featured Project Card + +Toronto Housing Market Dashboard + +> Real-time analytics on Toronto’s housing trends. dbt-powered ETL, Python scraping, Plotly visualization. +> \[View Dashboard\] \[View Repository\] + +--- + +### Brief Intro (2-3 sentences) + +I’m a data engineer who’s spent the last 8 years in the trenches—building the infrastructure that feeds dashboards, automates the boring stuff, and makes data actually usable. Most of my work has been in contact center operations and energy, where I’ve had to be scrappy: one-person data teams, legacy systems, stakeholders who need answers yesterday. + +I like solving real problems, not theoretical ones. + +--- + +## 2. ABOUT PAGE + +### Opening + +I didn’t start in data. I started in project management—CAPM certified, ITIL trained, the whole corporate playbook. Then I realized I liked building systems more than managing timelines, and I was better at automating reports than attending meetings about them. + +That pivot led me to where I am now: 8 years deep in data engineering, analytics, and the messy reality of turning raw information into something people can actually use. + +--- + +### What I Actually Do + +The short version: I build data infrastructure. Pipelines, warehouses, dashboards, automation—the invisible machinery that makes businesses run on data instead of gut feelings. + +The longer version: At Summitt Energy, I’ve been the sole data professional supporting 150+ employees across 9 markets (Canada and US). I inherited nothing—no data warehouse, no reporting infrastructure, no documentation. Over 5 years, I built DataFlow: an enterprise platform processing 1B+ rows, integrating contact center data, CRM systems, and legacy tools that definitely weren’t designed to talk to each other. + +That meant learning to be a generalist. I’ve done ETL pipeline development (Python, SQLAlchemy), dimensional modeling, dashboard design (Power BI, Plotly-Dash), API integration, and more stakeholder management than I’d like to admit. When you’re the only data person, you learn to wear every hat. + +--- + +### How I Think About Data + +I’m not interested in data for data’s sake. The question I always start with: What decision does this help someone make? + +Most of my work has been in operations-heavy environments—contact centers, energy retail, logistics. These aren’t glamorous domains, but they’re where data can have massive impact. A 30% improvement in abandon rate isn’t just a metric; it’s thousands of customers who didn’t hang up frustrated. A 40% reduction in reporting time means managers can actually manage instead of wrestling with spreadsheets. + +I care about outcomes, not technology stacks. + +--- + +### The Technical Stuff (For Those Who Want It) + +Languages: Python (Pandas, SQLAlchemy, FastAPI), SQL (MSSQL, PostgreSQL), R, VBA + +Data Engineering: ETL/ELT pipelines, dimensional modeling (star schema), dbt patterns, batch processing, API integration, web scraping (Selenium) + +Visualization: Plotly/Dash, Power BI, Tableau + +Platforms: Genesys Cloud, Five9, Zoho, Azure DevOps + +Currently Learning: Cloud certification (Azure DP-203), Airflow, Snowflake + +--- + +### Outside Work + +I’m a Brazilian-Canadian based in Toronto. I speak Portuguese (native), English (fluent), and enough Spanish to survive. + +When I’m not staring at SQL, I’m usually: + +- Building automation tools for small businesses through Bandit Labs (my side project) +- Contributing to open source (MCP servers, Claude Code plugins) +- Trying to explain to my kid why Daddy’s job involves “making computers talk to each other” + +--- + +### What I’m Looking For + +I’m currently exploring Senior Data Analyst and Data Engineer roles in the Toronto area (or remote). I’m most interested in: + +- Companies that treat data as infrastructure, not an afterthought +- Teams where I can contribute to architecture decisions, not just execute tickets +- Operations-focused industries (energy, logistics, financial services, contact center tech) + +If that sounds like your team, let’s talk. + +\[Download Resume\] \[Contact Me\] + +--- + +## 3. PROJECTS PAGE + +### Navigation Note + +The Projects page serves as an overview and status hub for all projects. A side navbar provides direct links to live dashboards and repositories. Users land on the overview first, then navigate to specific projects via the sidebar. + +### Intro Text + +These are projects I’ve built—some professional (anonymized where needed), some personal. Each one taught me something. Use the sidebar to jump directly to live dashboards or explore the overviews below. + +--- + +### Project Card: Toronto Housing Market Dashboard + +Type: Personal Project | Status: Live + +The Problem: +Toronto’s housing market moves fast, and most publicly available data is either outdated, behind paywalls, or scattered across dozens of sources. I wanted a single dashboard that tracked trends in real-time. + +What I Built: + +- Data Pipeline: Python scraper pulling listings data, automated on schedule +- Transformation Layer: dbt-based SQL architecture (staging → intermediate → marts) +- Visualization: Interactive Plotly-Dash dashboard with filters by neighborhood, price range, property type +- Infrastructure: PostgreSQL backend, version-controlled in Git + +Tech Stack: Python, dbt, PostgreSQL, Plotly-Dash, GitHub Actions + +What I Learned: +Real estate data is messy as hell. Listings get pulled, prices change, duplicates are everywhere. Building a reliable pipeline meant implementing serious data quality checks and learning to embrace “good enough” over “perfect.” + +\[View Live Dashboard\] \[View Repository (ETL + dbt)\] + +--- + +### Project Card: US Retail Energy Price Predictor + +Type: Personal Project | Status: Coming Soon (Phase 2) + +The Problem: +Retail energy pricing in deregulated US markets is volatile and opaque. Consumers and analysts lack accessible tools to understand pricing trends and forecast where rates are headed. + +What I’m Building: + +- Data Pipeline: Automated ingestion of public pricing data across multiple US markets +- ML Model: Price prediction using time series forecasting (ARIMA, Prophet, or similar) +- Transformation Layer: dbt-based SQL architecture for feature engineering +- Visualization: Interactive dashboard showing historical trends + predictions by state/market + +Tech Stack: Python, Scikit-learn, dbt, PostgreSQL, Plotly-Dash + +Why This Project: +This showcases the ML side of my skillset—something the Toronto Housing dashboard doesn’t cover. It also leverages my domain expertise from 5+ years in retail energy operations. + +\[Coming Soon\] + +--- + +### Project Card: DataFlow Platform (Enterprise Case Study) + +Type: Professional | Status: Deferred (Phase 3 — requires sanitized codebase) + +The Context: +When I joined Summitt Energy, there was no data infrastructure. Reports were manual. Insights were guesswork. I was hired to fix that. + +What I Built (Over 5 Years): + +- v1 (2020): Basic ETL scripts pulling Genesys Cloud data into MSSQL +- v2 (2021): Dimensional model (star schema) with fact/dimension tables +- v3 (2022): Python refactor with SQLAlchemy ORM, batch processing, error handling +- v4 (2023-24): dbt-pattern SQL views (staging → intermediate → marts), FastAPI layer, CLI tools + +Current State: + +- 21 tables, 1B+ rows +- 5,000+ daily transactions processed +- Integrates Genesys Cloud, Zoho CRM, legacy systems +- Feeds Power BI prototypes and production Dash dashboards +- Near-zero reporting errors + +Impact: + +- 40% improvement in reporting efficiency +- 30% reduction in call abandon rate (via KPI framework) +- 50% faster Average Speed to Answer +- 100% callback completion rate + +What I Learned: +Building data infrastructure as a team of one forces brutal prioritization. I learned to ship imperfect solutions fast, iterate based on feedback, and never underestimate how long stakeholder buy-in takes. + +Note: This is proprietary work. A sanitized case study with architecture patterns (no proprietary data) will be published in Phase 3. + +--- + +### Project Card: AI-Assisted Automation (Bandit Labs) + +Type: Consulting/Side Business | Status: Active + +What It Is: +Bandit Labs is my consulting practice focused on automation for small businesses. Most clients don’t need enterprise data platforms—they need someone to eliminate the 4 hours/week they spend manually entering receipts. + +Sample Work: + +- Receipt Processing Automation: OCR pipeline (Tesseract, Google Vision) extracting purchase data from photos, pushing directly to QuickBooks. Eliminated 3-4 hours/week of manual entry for a restaurant client. +- Product Margin Tracker: Plotly-Dash dashboard with real-time profitability insights +- Claude Code Plugins: MCP servers for Gitea, Wiki.js, NetBox integration + +Why I Do This: +Small businesses are underserved by the data/automation industry. Everyone wants to sell them enterprise software they don’t need. I like solving problems at a scale where the impact is immediately visible. + +\[Learn More About Bandit Labs\] + +--- + +## 4. LAB PAGE (Bandit Labs / Experiments) + +### Intro + +This is where I experiment. Some of this becomes client work. Some of it teaches me something and gets abandoned. All of it is real code solving real (or at least real-adjacent) problems. + +--- + +### Bandit Labs — Automation for Small Business + +I started Bandit Labs because I kept meeting small business owners drowning in manual work that should have been automated years ago. Enterprise tools are overkill. Custom development is expensive. There’s a gap in the middle. + +What I Offer: + +- Receipt/invoice processing automation +- Dashboard development (Plotly-Dash) +- Data pipeline setup for non-technical teams +- AI integration for repetitive tasks + +Recent Client Work: + +- Rio Açaí (Restaurant, Gatineau): Receipt OCR → QuickBooks integration. Saved 3-4 hours/week. + +\[Contact for Consulting\] + +--- + +### Open Source / Experiments + +MCP Servers (Model Context Protocol) +I’ve built production-ready MCP servers for: + +- Gitea: Issue management, label operations +- Wiki.js: Documentation access via GraphQL +- NetBox: CMDB integration (DCIM, IPAM, Virtualization) + +These let AI assistants (like Claude) interact with infrastructure tools through natural language. Still experimental, but surprisingly useful for my own workflows. + +Claude Code Plugins + +- projman: AI-guided sprint planning with Gitea/Wiki.js integration +- cmdb-assistant: Conversational infrastructure queries against NetBox +- project-hygiene: Post-task cleanup automation + +\[View on GitHub\] + +--- + +## 5. BLOG PAGE + +### Intro + +I write occasionally about data engineering, automation, and the reality of being a one-person data team. No hot takes, no growth hacking—just things I’ve learned the hard way. + +--- + +### Suggested Initial Articles + +Article 1: “Building a Data Platform as a Team of One”What I learned from 5 years as the sole data professional at a mid-size company + +Outline: + +- The reality of “full stack data” when there’s no one else +- Prioritization frameworks (what to build first when everything is urgent) +- Technical debt vs. shipping something +- Building stakeholder trust without a team to back you up +- What I’d do differently + +--- + +Article 2: “dbt Patterns Without dbt (And Why I Eventually Adopted Them)”How I accidentally implemented analytics engineering best practices before knowing the terminology + +Outline: + +- The problem: SQL spaghetti in production dashboards +- My solution: staging → intermediate → marts view architecture +- Why separation of concerns matters for maintainability +- The day I discovered dbt and realized I’d been doing this manually +- Migration path for legacy SQL codebases + +--- + +Article 3: “The Toronto Housing Market Dashboard: A Data Engineering Postmortem”Building a real-time analytics pipeline for messy, uncooperative data + +Outline: + +- Why I built this (and why public housing data sucks) +- Data sourcing challenges and ethical scraping +- Pipeline architecture decisions +- dbt transformation layer design +- What broke and how I fixed it +- Dashboard design for non-technical users + +--- + +Article 4: “Automating Small Business Operations with OCR and AI”A case study in practical automation for non-enterprise clients + +Outline: + +- The client problem: 4 hours/week on receipt entry +- Why “just use \[enterprise tool\]” doesn’t work for small business +- Building an OCR pipeline with Tesseract and Google Vision +- QuickBooks integration gotchas +- ROI calculation for automation projects + +--- + +Article 5: “What I Wish I Knew Before Building My First ETL Pipeline”Hard-won lessons for junior data engineers + +Outline: + +- Error handling isn’t optional (it’s the whole job) +- Logging is your best friend at 2am +- Why idempotency matters +- The staging table pattern +- Testing data pipelines +- Documentation nobody will read (write it anyway) + +--- + +Article 6: “Predicting US Retail Energy Prices: An ML Project Walkthrough”Building a forecasting model with domain knowledge from 5 years in energy retail + +Outline: + +- Why retail energy pricing is hard to predict (deregulation, seasonality, policy) +- Data sourcing and pipeline architecture +- Feature engineering with dbt +- Model selection (ARIMA vs Prophet vs ensemble) +- Evaluation metrics that matter for price forecasting +- Lessons from applying domain expertise to ML + +--- + +## 6. RESUME PAGE + +### Inline Display + +Show a clean, readable version of the resume directly on the page. Use your tailored Senior Data Analyst version as the base. + +### Download Options + +- \[Download PDF\] +- \[Download DOCX\] +- \[View on LinkedIn\] + +### Optional: Interactive Timeline + +Visual timeline of career progression with expandable sections for each role. More engaging than a wall of text, but only if you have time to build it. + +--- + +## 7. CONTACT PAGE + +### Intro + +I’m currently open to Senior Data Analyst and Data Engineer roles in Toronto (or remote). If you’re working on something interesting and need someone who can build data infrastructure from scratch, I’d like to hear about it. + +For consulting inquiries (automation, dashboards, small business data work), reach out about Bandit Labs. + +--- + +### Contact Form Fields + +- Name +- Email +- Subject (dropdown: Job Opportunity / Consulting Inquiry / Other) +- Message + +--- + +### Direct Contact + +- Email: leobrmi@hotmail.com +- Phone: (416) 859-7936 +- LinkedIn: \[link\] +- GitHub: \[link\] + +--- + +### Location + +Toronto, ON, Canada +Canadian Citizen | Eligible to work in Canada and US + +--- + +## TONE GUIDELINES + +### Do: + +- Be direct and specific +- Use first person naturally +- Include concrete metrics +- Acknowledge constraints and tradeoffs +- Show personality without being performative +- Write like you talk (minus the profanity) + +### Don’t: + +- Use buzzwords without substance (“leveraging synergies”) +- Oversell or inflate +- Write in third person +- Use passive voice excessively +- Sound like a LinkedIn influencer +- Pretend you’re a full team when you’re one person + +--- + +## SEO / DISCOVERABILITY + +### Target Keywords (Organic) + +- Toronto data analyst +- Data engineer portfolio +- Python ETL developer +- dbt analytics engineer +- Contact center analytics + +### Blog Strategy + +Aim for 1-2 posts per month initially. Focus on: + +- Technical tutorials (how I built X) +- Lessons learned (what went wrong and how I fixed it) +- Industry observations (data work in operations-heavy companies) + +--- + +## IMPLEMENTATION PRIORITY + +### Phase 1 (MVP — Get it live) + +1. Home page (hero + brief intro + featured project) +2. About page (full content) +3. Projects page (overview + status cards with navbar links to dashboards) +4. Resume page (inline + download) +5. Contact page (form + direct info) +6. Blog (start with 2-3 articles) + +### Phase 2 (Expand) + +1. Lab page (Bandit Labs + experiments) +2. US Retail Energy Price Predictor (ML project — coming soon) +3. Add more projects as completed + +### Phase 3 (Polish) + +1. DataFlow Platform case study (requires sanitized fork of proprietary codebase) +2. Testimonials (if available from Summitt stakeholders) +3. Interactive elements (timeline, project filters) + +--- + +Last updated: January 2025 \ No newline at end of file diff --git a/docs/project-lessons-learned/INDEX.md b/docs/project-lessons-learned/INDEX.md new file mode 100644 index 0000000..f45f8b2 --- /dev/null +++ b/docs/project-lessons-learned/INDEX.md @@ -0,0 +1,56 @@ +# Project Lessons Learned + +This folder contains lessons learned from sprints and development work. These lessons help prevent repeating mistakes and capture valuable insights. + +**Note:** This is a temporary local backup while Wiki.js integration is being configured. Once Wiki.js is ready, lessons will be migrated there for better searchability. + +--- + +## Lessons Index + +| Date | Sprint/Phase | Title | Tags | +|------|--------------|-------|------| +| 2026-02-01 | Sprint 10 | [Formspree Integration with Dash Callbacks](./sprint-10-formspree-dash-integration.md) | formspree, dash, callbacks, forms, spam-protection, honeypot, ajax | +| 2026-01-17 | Sprint 9 | [Gitea Labels API Requires Org Context](./sprint-9-gitea-labels-user-repos.md) | gitea, mcp, api, labels, projman, configuration | +| 2026-01-17 | Sprint 9 | [Always Read CLAUDE.md Before Asking Questions](./sprint-9-read-claude-md-first.md) | projman, claude-code, context, documentation, workflow | +| 2026-01-17 | Sprint 9-10 | [Graceful Error Handling in Service Layers](./sprint-9-10-graceful-error-handling.md) | python, postgresql, error-handling, dash, graceful-degradation, arm64 | +| 2026-01-17 | Sprint 9-10 | [Modular Callback Structure](./sprint-9-10-modular-callback-structure.md) | dash, callbacks, architecture, python, code-organization | +| 2026-01-17 | Sprint 9-10 | [Figure Factory Pattern](./sprint-9-10-figure-factory-pattern.md) | plotly, dash, design-patterns, python, visualization | +| 2026-01-16 | Phase 4 | [dbt Test Syntax Deprecation](./phase-4-dbt-test-syntax.md) | dbt, testing, yaml, deprecation | + +--- + +## How to Use + +### When Starting a Sprint +1. Review relevant lessons in this folder before implementation +2. Search by tags or keywords to find applicable insights +3. Apply prevention strategies from past lessons + +### When Closing a Sprint +1. Document any significant lessons learned +2. Use the template below +3. Add entry to the index table above + +--- + +## Lesson Template + +```markdown +# [Sprint/Phase] - [Lesson Title] + +## Context +[What were you trying to do?] + +## Problem +[What went wrong or what insight emerged?] + +## Solution +[How did you solve it?] + +## Prevention +[How can this be avoided in future sprints?] + +## Tags +[Comma-separated tags for search] +``` diff --git a/docs/project-lessons-learned/phase-4-dbt-test-syntax.md b/docs/project-lessons-learned/phase-4-dbt-test-syntax.md new file mode 100644 index 0000000..959780a --- /dev/null +++ b/docs/project-lessons-learned/phase-4-dbt-test-syntax.md @@ -0,0 +1,38 @@ +# Phase 4 - dbt Test Syntax Deprecation + +## Context +Implementing dbt mart models with `accepted_values` tests for tier columns (safety_tier, income_quintile, amenity_tier) that should only contain values 1-5. + +## Problem +dbt 1.9+ introduced a deprecation warning for generic test arguments. The old syntax: + +```yaml +tests: + - accepted_values: + values: [1, 2, 3, 4, 5] +``` + +Produces deprecation warnings: +``` +MissingArgumentsPropertyInGenericTestDeprecation: Arguments to generic tests should be nested under the `arguments` property. +``` + +## Solution +Nest test arguments under the `arguments` property: + +```yaml +tests: + - accepted_values: + arguments: + values: [1, 2, 3, 4, 5] +``` + +This applies to all generic tests with arguments, not just `accepted_values`. + +## Prevention +- When writing dbt schema YAML files, always use the `arguments:` nesting for generic tests +- Run `dbt parse --no-partial-parse` to catch all deprecation warnings before they become errors +- Check dbt changelog when upgrading versions for breaking changes to test syntax + +## Tags +dbt, testing, yaml, deprecation, syntax, schema diff --git a/docs/project-lessons-learned/sprint-10-formspree-dash-integration.md b/docs/project-lessons-learned/sprint-10-formspree-dash-integration.md new file mode 100644 index 0000000..64240cd --- /dev/null +++ b/docs/project-lessons-learned/sprint-10-formspree-dash-integration.md @@ -0,0 +1,70 @@ +# Sprint 10 - Formspree Integration with Dash Callbacks + +## Context +Implementing a contact form on a Dash portfolio site that submits to Formspree, a third-party form handling service. + +## Insights + +### Formspree AJAX Submission +Formspree supports AJAX submissions (no page redirect) when you: +1. POST with `Content-Type: application/json` +2. Include `Accept: application/json` header +3. Send form data as JSON body + +This returns a JSON response instead of redirecting to a thank-you page, which is ideal for single-page Dash applications. + +### Dash Multi-Output Callbacks for Forms +When handling form submission with validation and feedback, use a multi-output callback pattern: + +```python +@callback( + Output("feedback-container", "children"), # Success/error alert + Output("submit-button", "loading"), # Button loading state + Output("field-1", "value"), # Clear on success + Output("field-2", "value"), # Clear on success + Output("field-1", "error"), # Field-level errors + Output("field-2", "error"), # Field-level errors + Input("submit-button", "n_clicks"), + State("field-1", "value"), + State("field-2", "value"), + prevent_initial_call=True, +) +``` + +Use `no_update` for outputs you don't want to change (e.g., keep form values on validation error, only clear on success). + +### Honeypot Spam Protection +Simple and effective bot protection without CAPTCHA: +1. Add a hidden text input field (CSS: `position: absolute; left: -9999px`) +2. Set `tabIndex=-1` and `autoComplete="off"` to prevent accidental filling +3. In callback, check if honeypot has value - if yes, it's a bot +4. For bots: return fake success (don't reveal detection) +5. For humans: proceed with real submission + +Formspree also accepts `_gotcha` as a honeypot field name in the JSON payload. + +## Code Pattern + +```python +# Honeypot check - bots fill hidden fields +if honeypot_value: + # Fake success - don't let bots know they were caught + return (_create_success_alert(), False, "", "", None, None) + +# Real submission for humans +response = requests.post( + FORMSPREE_ENDPOINT, + json=form_data, + headers={"Accept": "application/json", "Content-Type": "application/json"}, + timeout=10, +) +``` + +## Prevention/Best Practices +- Always use `timeout` parameter with `requests.post()` to avoid hanging +- Wrap external API calls in try/except for network errors +- Return user-friendly error messages, not technical details +- Use DMC's `required=True` and `error` props for form validation feedback + +## Tags +formspree, dash, callbacks, forms, spam-protection, honeypot, ajax, python, requests, validation diff --git a/docs/project-lessons-learned/sprint-9-10-figure-factory-pattern.md b/docs/project-lessons-learned/sprint-9-10-figure-factory-pattern.md new file mode 100644 index 0000000..b1b501c --- /dev/null +++ b/docs/project-lessons-learned/sprint-9-10-figure-factory-pattern.md @@ -0,0 +1,53 @@ +# Sprint 9-10 - Figure Factory Pattern for Reusable Charts + +## Context +Creating multiple chart types across 5 dashboard tabs, with consistent styling and behavior needed across all visualizations. + +## Problem +Without a standardized approach, each callback would create figures inline with: +- Duplicated styling code (colors, fonts, backgrounds) +- Inconsistent hover templates +- Hard-to-maintain figure creation logic +- No reuse between tabs + +## Solution +Created a `figures/` module with factory functions: + +``` +figures/ +├── __init__.py # Exports all factories +├── choropleth.py # Map visualizations +├── bar_charts.py # ranking_bar, stacked_bar, horizontal_bar +├── scatter.py # scatter_figure, bubble_chart +├── radar.py # radar_figure, comparison_radar +└── demographics.py # age_pyramid, donut_chart +``` + +Factory pattern benefits: +1. **Consistent styling** - dark theme applied once +2. **Type-safe interfaces** - clear parameters for each chart type +3. **Easy testing** - factories can be unit tested with sample data +4. **Reusability** - same factory used across multiple tabs + +Example factory signature: +```python +def create_ranking_bar( + data: list[dict], + name_column: str, + value_column: str, + title: str = "", + top_n: int = 5, + bottom_n: int = 5, + top_color: str = "#4CAF50", + bottom_color: str = "#F44336", +) -> go.Figure: +``` + +## Prevention +- **Create factories early** - before implementing callbacks +- **Design generic interfaces** - factories should work with any data matching the schema +- **Apply styling in one place** - use constants for colors, fonts +- **Test factories independently** - with synthetic data before integration + +## Tags +plotly, dash, design-patterns, python, visualization, reusability, code-organization diff --git a/docs/project-lessons-learned/sprint-9-10-graceful-error-handling.md b/docs/project-lessons-learned/sprint-9-10-graceful-error-handling.md new file mode 100644 index 0000000..0191a56 --- /dev/null +++ b/docs/project-lessons-learned/sprint-9-10-graceful-error-handling.md @@ -0,0 +1,34 @@ +# Sprint 9-10 - Graceful Error Handling in Service Layers + +## Context +Building the Toronto Neighbourhood Dashboard with a service layer that queries PostgreSQL/PostGIS dbt marts to provide data to Dash callbacks. + +## Problem +Initial service layer implementation let database connection errors propagate as unhandled exceptions. When the PostGIS Docker container was unavailable (common on ARM64 systems where the x86_64 image fails), the entire dashboard would crash instead of gracefully degrading. + +## Solution +Wrapped database queries in try/except blocks to return empty DataFrames/lists/dicts when the database is unavailable: + +```python +def _execute_query(sql: str, params: dict | None = None) -> pd.DataFrame: + try: + engine = get_engine() + with engine.connect() as conn: + return pd.read_sql(text(sql), conn, params=params) + except Exception: + return pd.DataFrame() +``` + +This allows: +1. Dashboard to load and display empty states +2. Development/testing without running database +3. Graceful degradation in production + +## Prevention +- **Always design service layers with graceful degradation** - assume external dependencies can fail +- **Return empty collections, not exceptions** - let UI components handle empty states +- **Test without database** - verify the app doesn't crash when DB is unavailable +- **Consider ARM64 compatibility** - PostGIS images may not support all platforms + +## Tags +python, postgresql, service-layer, error-handling, dash, graceful-degradation, arm64 diff --git a/docs/project-lessons-learned/sprint-9-10-modular-callback-structure.md b/docs/project-lessons-learned/sprint-9-10-modular-callback-structure.md new file mode 100644 index 0000000..fd8814a --- /dev/null +++ b/docs/project-lessons-learned/sprint-9-10-modular-callback-structure.md @@ -0,0 +1,45 @@ +# Sprint 9-10 - Modular Callback Structure for Multi-Tab Dashboards + +## Context +Implementing a 5-tab Toronto Neighbourhood Dashboard with multiple callbacks per tab (map updates, chart updates, KPI updates, selection handling). + +## Problem +Initial callback implementation approach would have placed all callbacks in a single file, leading to: +- A monolithic file with 500+ lines +- Difficult-to-navigate code +- Callbacks for different tabs interleaved +- Testing difficulties + +## Solution +Organized callbacks into three focused modules: + +``` +callbacks/ +├── __init__.py # Imports all modules to register callbacks +├── map_callbacks.py # Choropleth updates, map click handling +├── chart_callbacks.py # Supporting chart updates (scatter, trend, donut) +└── selection_callbacks.py # Dropdown population, KPI updates +``` + +Key patterns: +1. **Group by responsibility**, not by tab - all map-related callbacks together +2. **Use noqa comments** for imports that register callbacks as side effects +3. **Share helper functions** (like `_empty_chart()`) within modules + +```python +# callbacks/__init__.py +from . import ( + chart_callbacks, # noqa: F401 + map_callbacks, # noqa: F401 + selection_callbacks, # noqa: F401 +) +``` + +## Prevention +- **Plan callback organization before implementation** - sketch which callbacks go where +- **Group by function, not by feature** - keeps related logic together +- **Keep modules under 400 lines** - split if exceeding +- **Test imports early** - verify callbacks register correctly + +## Tags +dash, callbacks, architecture, python, code-organization, maintainability diff --git a/docs/project-lessons-learned/sprint-9-gitea-labels-user-repos.md b/docs/project-lessons-learned/sprint-9-gitea-labels-user-repos.md new file mode 100644 index 0000000..a2be341 --- /dev/null +++ b/docs/project-lessons-learned/sprint-9-gitea-labels-user-repos.md @@ -0,0 +1,29 @@ +# Sprint 9 - Gitea Labels API Requires Org Context + +## Context +Creating Gitea issues with labels via MCP tools during Sprint 9 planning for the personal-portfolio project. + +## Problem +When calling `create_issue` with a `labels` parameter, received: +``` +404 Client Error: Not Found for url: https://gitea.hotserv.cloud/api/v1/orgs/lmiranda/labels +``` + +The API attempted to fetch labels from an **organization** endpoint, but `lmiranda` is a **user account**, not an organization. + +## Solution +Created issues without the `labels` parameter and documented intended labels in the issue body instead: +```markdown +**Labels:** Type/Feature, Priority/Medium, Complexity/Simple, Efforts/XS, Component/Docs, Tech/Python +``` + +This provides visibility into intended categorization while avoiding the API error. + +## Prevention +- When working with user-owned repos (not org repos), avoid using the `labels` parameter in `create_issue` +- Document labels in issue body as a workaround +- Consider creating a repo-level label set for user repos (Gitea supports this) +- Update projman plugin to handle user vs org repos differently + +## Tags +gitea, mcp, api, labels, projman, configuration diff --git a/docs/project-lessons-learned/sprint-9-read-claude-md-first.md b/docs/project-lessons-learned/sprint-9-read-claude-md-first.md new file mode 100644 index 0000000..c6f8124 --- /dev/null +++ b/docs/project-lessons-learned/sprint-9-read-claude-md-first.md @@ -0,0 +1,30 @@ +# Sprint 9 - Always Read CLAUDE.md Before Asking Questions + +## Context +Starting Sprint 9 planning session with `/projman:sprint-plan` command. + +## Problem +Asked the user "what should I do?" when all the necessary context was already documented in CLAUDE.md: +- Current sprint number and phase +- Implementation plan location +- Remaining phases to complete +- Project conventions and workflows + +This caused user frustration: "why are you asking what to do? cant you see this yourself" + +## Solution +Before asking any questions about what to do: +1. Read `CLAUDE.md` in the project root +2. Check "Project Status" section for current sprint/phase +3. Follow references to implementation plans +4. Review "Projman Plugin Workflow" section for expected behavior + +## Prevention +- **ALWAYS** read CLAUDE.md at the start of any sprint-related command +- Look for "Current Sprint" and "Phase" indicators +- Check for implementation plan references in `docs/changes/` +- Only ask questions if information is genuinely missing from documentation +- The projman plugin expects autonomous behavior based on documented context + +## Tags +projman, claude-code, context, documentation, workflow, sprint-planning diff --git a/docs/runbooks/adding-dashboard.md b/docs/runbooks/adding-dashboard.md new file mode 100644 index 0000000..d02e421 --- /dev/null +++ b/docs/runbooks/adding-dashboard.md @@ -0,0 +1,200 @@ +# Runbook: Adding a New Dashboard + +This runbook describes how to add a new data dashboard to the portfolio application. + +## Prerequisites + +- [ ] Data sources identified and accessible +- [ ] Database schema designed +- [ ] Basic Dash/Plotly familiarity + +## Directory Structure + +Create the following structure under `portfolio_app/`: + +``` +portfolio_app/ +├── pages/ +│ └── {dashboard_name}/ +│ ├── dashboard.py # Main layout with tabs +│ ├── methodology.py # Data sources and methods page +│ ├── tabs/ +│ │ ├── __init__.py +│ │ ├── overview.py # Overview tab layout +│ │ └── ... # Additional tab layouts +│ └── callbacks/ +│ ├── __init__.py +│ └── ... # Callback modules +├── {dashboard_name}/ # Data logic (outside pages/) +│ ├── __init__.py +│ ├── parsers/ # API/CSV extraction +│ │ └── __init__.py +│ ├── loaders/ # Database operations +│ │ └── __init__.py +│ ├── schemas/ # Pydantic models +│ │ └── __init__.py +│ └── models/ # SQLAlchemy ORM +│ └── __init__.py +``` + +## Step-by-Step Checklist + +### 1. Data Layer + +- [ ] Create Pydantic schemas in `{dashboard_name}/schemas/` +- [ ] Create SQLAlchemy models in `{dashboard_name}/models/` +- [ ] Create parsers in `{dashboard_name}/parsers/` +- [ ] Create loaders in `{dashboard_name}/loaders/` +- [ ] Add database migrations if needed + +### 2. dbt Models + +Create dbt models in `dbt/models/`: + +- [ ] `staging/stg_{source}__{entity}.sql` - Raw data cleaning +- [ ] `intermediate/int_{domain}__{transform}.sql` - Business logic +- [ ] `marts/mart_{domain}.sql` - Final analytical tables + +Follow naming conventions: +- Staging: `stg_{source}__{entity}` +- Intermediate: `int_{domain}__{transform}` +- Marts: `mart_{domain}` + +### 3. Visualization Layer + +- [ ] Create figure factories in `figures/` (or reuse existing) +- [ ] Follow the factory pattern: `create_{chart_type}_figure(data, **kwargs)` + +### 4. Dashboard Pages + +#### Main Dashboard (`pages/{dashboard_name}/dashboard.py`) + +```python +import dash +from dash import html, dcc +import dash_mantine_components as dmc + +dash.register_page( + __name__, + path="/{dashboard_name}", + title="{Dashboard Title}", + description="{Description}" +) + +def layout(): + return dmc.Container([ + # Header + dmc.Title("{Dashboard Title}", order=1), + + # Tabs + dmc.Tabs([ + dmc.TabsList([ + dmc.TabsTab("Overview", value="overview"), + # Add more tabs + ]), + dmc.TabsPanel(overview_tab(), value="overview"), + # Add more panels + ], value="overview"), + ]) +``` + +#### Tab Layouts (`pages/{dashboard_name}/tabs/`) + +- [ ] Create one file per tab +- [ ] Export layout function from each + +#### Callbacks (`pages/{dashboard_name}/callbacks/`) + +- [ ] Create callback modules for interactivity +- [ ] Import and register in dashboard.py + +### 5. Navigation + +Add to sidebar in `components/sidebar.py`: + +```python +dmc.NavLink( + label="{Dashboard Name}", + href="/{dashboard_name}", + icon=DashIconify(icon="..."), +) +``` + +### 6. Documentation + +- [ ] Create methodology page (`pages/{dashboard_name}/methodology.py`) +- [ ] Document data sources +- [ ] Document transformation logic +- [ ] Add notebooks to `notebooks/{dashboard_name}/` if needed + +### 7. Testing + +- [ ] Add unit tests for parsers +- [ ] Add unit tests for loaders +- [ ] Add integration tests for callbacks +- [ ] Run `make test` + +### 8. Final Verification + +- [ ] All pages render without errors +- [ ] All callbacks respond correctly +- [ ] Data loads successfully +- [ ] dbt models run cleanly (`make dbt-run`) +- [ ] Linting passes (`make lint`) +- [ ] Tests pass (`make test`) + +## Example: Toronto Dashboard + +Reference implementation: `portfolio_app/pages/toronto/` + +Key files: +- `dashboard.py` - Main layout with 5 tabs +- `tabs/overview.py` - Livability scores, scatter plots +- `callbacks/map_callbacks.py` - Choropleth interactions +- `toronto/models/dimensions.py` - Dimension tables +- `toronto/models/facts.py` - Fact tables + +## Common Patterns + +### Figure Factories + +```python +# figures/choropleth.py +def create_choropleth_figure( + gdf: gpd.GeoDataFrame, + value_column: str, + title: str, + **kwargs +) -> go.Figure: + ... +``` + +### Callbacks + +```python +# callbacks/map_callbacks.py +@callback( + Output("neighbourhood-details", "children"), + Input("choropleth-map", "clickData"), +) +def update_details(click_data): + ... +``` + +### Data Loading + +```python +# {dashboard_name}/loaders/load.py +def load_data(session: Session) -> None: + # Parse from source + records = parse_source_data() + + # Validate with Pydantic + validated = [Schema(**r) for r in records] + + # Load to database + for record in validated: + session.add(Model(**record.model_dump())) + + session.commit() +``` diff --git a/docs/runbooks/deployment.md b/docs/runbooks/deployment.md new file mode 100644 index 0000000..3babddc --- /dev/null +++ b/docs/runbooks/deployment.md @@ -0,0 +1,232 @@ +# Runbook: Deployment + +This runbook covers deployment procedures for the Analytics Portfolio application. + +## Environments + +| Environment | Branch | Server | URL | +|-------------|--------|--------|-----| +| Development | `development` | Local | http://localhost:8050 | +| Staging | `staging` | Homelab (hotserv) | Internal | +| Production | `main` | Bandit Labs VPS | https://leodata.science | + +## CI/CD Pipeline + +### Automatic Deployment + +Deployments are triggered automatically via Gitea Actions: + +1. **Push to `staging`** → Deploys to staging server +2. **Push to `main`** → Deploys to production server + +### Workflow Files + +- `.gitea/workflows/ci.yml` - Runs linting and tests on all branches +- `.gitea/workflows/deploy-staging.yml` - Staging deployment +- `.gitea/workflows/deploy-production.yml` - Production deployment + +### Required Secrets + +Configure these in Gitea repository settings: + +| Secret | Description | +|--------|-------------| +| `STAGING_HOST` | Staging server hostname/IP | +| `STAGING_USER` | SSH username for staging | +| `STAGING_SSH_KEY` | Private key for staging SSH | +| `PROD_HOST` | Production server hostname/IP | +| `PROD_USER` | SSH username for production | +| `PROD_SSH_KEY` | Private key for production SSH | + +## Manual Deployment + +### Prerequisites + +- SSH access to target server +- Repository cloned at `~/apps/personal-portfolio` +- Virtual environment created at `.venv` +- Docker and Docker Compose installed +- PostgreSQL container running + +### Steps + +```bash +# 1. SSH to server +ssh user@server + +# 2. Navigate to app directory +cd ~/apps/personal-portfolio + +# 3. Pull latest changes +git fetch origin {branch} +git reset --hard origin/{branch} + +# 4. Activate virtual environment +source .venv/bin/activate + +# 5. Install dependencies +pip install -r requirements.txt + +# 6. Run database migrations (if any) +# python -m alembic upgrade head + +# 7. Run dbt models +cd dbt && dbt run --profiles-dir . && cd .. + +# 8. Restart application +docker compose down +docker compose up -d + +# 9. Verify health +curl http://localhost:8050/health +``` + +## Rollback Procedure + +### Quick Rollback + +If deployment fails, rollback to previous commit: + +```bash +# 1. Find previous working commit +git log --oneline -10 + +# 2. Reset to that commit +git reset --hard {commit_hash} + +# 3. Restart services +docker compose down +docker compose up -d + +# 4. Verify +curl http://localhost:8050/health +``` + +### Full Rollback (Database) + +If database changes need to be reverted: + +```bash +# 1. Stop application +docker compose down + +# 2. Restore database backup +pg_restore -h localhost -U portfolio -d portfolio backup.dump + +# 3. Revert code +git reset --hard {commit_hash} + +# 4. Run dbt at that version +cd dbt && dbt run --profiles-dir . && cd .. + +# 5. Restart +docker compose up -d +``` + +## Health Checks + +### Application Health + +```bash +curl http://localhost:8050/health +``` + +Expected response: +```json +{"status": "healthy"} +``` + +### Database Health + +```bash +docker compose exec postgres pg_isready -U portfolio +``` + +### Container Status + +```bash +docker compose ps +``` + +## Monitoring + +### View Logs + +```bash +# All services +make logs + +# Specific service +make logs SERVICE=postgres + +# Or directly +docker compose logs -f +``` + +### Check Resource Usage + +```bash +docker stats +``` + +## Troubleshooting + +### Application Won't Start + +1. Check container logs: `docker compose logs app` +2. Verify environment variables: `cat .env` +3. Check database connectivity: `docker compose exec postgres pg_isready` +4. Verify port availability: `lsof -i :8050` + +### Database Connection Errors + +1. Check postgres container: `docker compose ps postgres` +2. Verify DATABASE_URL in `.env` +3. Check postgres logs: `docker compose logs postgres` +4. Test connection: `docker compose exec postgres psql -U portfolio -c '\l'` + +### dbt Failures + +1. Check dbt logs: `cd dbt && dbt debug` +2. Verify profiles.yml: `cat dbt/profiles.yml` +3. Run with verbose output: `dbt run --debug` + +### Out of Memory + +1. Check memory usage: `free -h` +2. Review container limits in docker-compose.yml +3. Consider increasing swap or server resources + +## Backup Procedures + +### Database Backup + +```bash +# Create backup +docker compose exec postgres pg_dump -U portfolio portfolio > backup_$(date +%Y%m%d).sql + +# Compressed backup +docker compose exec postgres pg_dump -U portfolio -Fc portfolio > backup_$(date +%Y%m%d).dump +``` + +### Restore from Backup + +```bash +# From SQL file +docker compose exec -T postgres psql -U portfolio portfolio < backup.sql + +# From dump file +docker compose exec -T postgres pg_restore -U portfolio -d portfolio < backup.dump +``` + +## Deployment Checklist + +Before deploying to production: + +- [ ] All tests pass (`make test`) +- [ ] Linting passes (`make lint`) +- [ ] Staging deployment successful +- [ ] Manual testing on staging complete +- [ ] Database backup taken +- [ ] Rollback plan confirmed +- [ ] Team notified of deployment window diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 0000000..6354a9c --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,69 @@ +# Toronto Neighbourhood Dashboard - Notebooks + +Documentation notebooks for the Toronto Neighbourhood Dashboard visualizations. Each notebook documents how data is queried, transformed, and visualized using the figure factory pattern. + +## Directory Structure + +``` +notebooks/ +├── README.md # This file +├── overview/ # Overview tab visualizations +├── housing/ # Housing tab visualizations +├── safety/ # Safety tab visualizations +├── demographics/ # Demographics tab visualizations +└── amenities/ # Amenities tab visualizations +``` + +## Notebook Template + +Each notebook follows a standard two-section structure: + +### Section 1: Data Reference + +Documents the data pipeline: +- **Source Tables**: List of dbt marts/tables used +- **SQL Query**: The exact query to fetch data +- **Transformation Steps**: Any pandas/python transformations +- **Sample Output**: First 10 rows of the result + +### Section 2: Data Visualization + +Documents the figure creation: +- **Figure Factory**: Import from `portfolio_app.figures` +- **Parameters**: Key configuration options +- **Rendered Output**: The actual visualization + +## Available Figure Factories + +| Factory | Module | Use Case | +|---------|--------|----------| +| `create_choropleth` | `figures.choropleth` | Map visualizations | +| `create_ranking_bar` | `figures.bar_charts` | Top/bottom N rankings | +| `create_stacked_bar` | `figures.bar_charts` | Category breakdowns | +| `create_scatter` | `figures.scatter` | Correlation plots | +| `create_radar` | `figures.radar` | Multi-metric comparisons | +| `create_age_pyramid` | `figures.demographics` | Age distributions | +| `create_time_series` | `figures.time_series` | Trend lines | + +## Usage + +1. Start Jupyter from project root: + ```bash + jupyter notebook notebooks/ + ``` + +2. Ensure database is running: + ```bash + make docker-up + ``` + +3. Each notebook is self-contained - run all cells top to bottom. + +## Notebook Naming Convention + +`{metric}_{chart_type}.ipynb` + +Examples: +- `livability_choropleth.ipynb` +- `crime_trend_line.ipynb` +- `age_pyramid.ipynb` diff --git a/notebooks/amenities/.gitkeep b/notebooks/amenities/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/amenities/amenity_index_choropleth.ipynb b/notebooks/amenities/amenity_index_choropleth.ipynb new file mode 100644 index 0000000..fa157d3 --- /dev/null +++ b/notebooks/amenities/amenity_index_choropleth.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amenity Index Choropleth Map\n", + "\n", + "Displays total amenities per 1,000 residents across Toronto's 158 neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_amenities` | neighbourhood \u00d7 year | amenity_index, total_amenities_per_1000, amenity_tier, geometry |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_id,\n", + " neighbourhood_name,\n", + " geometry,\n", + " year,\n", + " total_amenities_per_1000,\n", + " amenity_index,\n", + " amenity_tier,\n", + " parks_per_1000,\n", + " schools_per_1000,\n", + " transit_per_1000,\n", + " total_amenities,\n", + " population\n", + "FROM public_marts.mart_neighbourhood_amenities\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n", + "ORDER BY total_amenities_per_1000 DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent year\n", + "2. Convert geometry to GeoJSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import json\n", + "\n", + "gdf = gpd.GeoDataFrame(\n", + " df,\n", + " geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n", + " crs='EPSG:4326'\n", + ")\n", + "\n", + "geojson = json.loads(gdf.to_json())\n", + "data = df.drop(columns=['geometry']).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'total_amenities_per_1000', 'amenity_index', 'amenity_tier']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.choropleth import create_choropleth_figure\n", + "\n", + "fig = create_choropleth_figure(\n", + " geojson=geojson,\n", + " data=data,\n", + " location_key='neighbourhood_id',\n", + " color_column='total_amenities_per_1000',\n", + " hover_data=['neighbourhood_name', 'amenity_index', 'parks_per_1000', 'schools_per_1000'],\n", + " color_scale='Greens',\n", + " title='Toronto Amenities per 1,000 Population',\n", + " zoom=10,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Amenity Tier Interpretation\n", + "\n", + "| Tier | Meaning |\n", + "|------|--------|\n", + "| 1 | Best served (top 20%) |\n", + "| 2-4 | Middle tiers |\n", + "| 5 | Underserved (bottom 20%) |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/amenities/amenity_radar.ipynb b/notebooks/amenities/amenity_radar.ipynb new file mode 100644 index 0000000..99327d5 --- /dev/null +++ b/notebooks/amenities/amenity_radar.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amenity Radar Chart\n", + "\n", + "Spider/radar chart comparing amenity categories for selected neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_amenities` | neighbourhood × year | parks_index, schools_index, transit_index |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import pandas as pd\nfrom sqlalchemy import create_engine\nfrom dotenv import load_dotenv\nimport os\n\n# Load .env from project root\nload_dotenv('../../.env')\n\nengine = create_engine(os.environ['DATABASE_URL'])\n\nquery = \"\"\"\nSELECT\n neighbourhood_name,\n parks_index,\n schools_index,\n transit_index,\n amenity_index,\n amenity_tier\nFROM public_marts.mart_neighbourhood_amenities\nWHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\nORDER BY amenity_index DESC\n\"\"\"\n\ndf = pd.read_sql(query, engine)\nprint(f\"Loaded {len(df)} neighbourhoods\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Select top 5 and bottom 5 neighbourhoods by amenity index\n", + "2. Reshape for radar chart format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Select representative neighbourhoods\n", + "top_5 = df.head(5)\n", + "bottom_5 = df.tail(5)\n", + "\n", + "# Prepare radar data\n", + "categories = ['Parks', 'Schools', 'Transit']\n", + "index_columns = ['parks_index', 'schools_index', 'transit_index']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Top 5 Amenity-Rich Neighbourhoods:\")\n", + "display(top_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])\n", + "print(\"\\nBottom 5 Underserved Neighbourhoods:\")\n", + "display(bottom_5[['neighbourhood_name', 'parks_index', 'schools_index', 'transit_index', 'amenity_index']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_radar` from `portfolio_app.figures.radar`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import sys\nsys.path.insert(0, '../..')\n\nfrom portfolio_app.figures.radar import create_comparison_radar\n\n# Compare top neighbourhood vs city average (100)\ntop_hood = top_5.iloc[0]\nmetrics = ['parks_index', 'schools_index', 'transit_index']\n\nfig = create_comparison_radar(\n selected_data=top_hood.to_dict(),\n average_data={'parks_index': 100, 'schools_index': 100, 'transit_index': 100},\n metrics=metrics,\n selected_name=top_hood['neighbourhood_name'],\n average_name='City Average',\n title=f\"Amenity Profile: {top_hood['neighbourhood_name']} vs City Average\",\n)\n\nfig.show()" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index Interpretation\n", + "\n", + "| Value | Meaning |\n", + "|-------|--------|\n", + "| < 100 | Below city average |\n", + "| = 100 | City average |\n", + "| > 100 | Above city average |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/amenities/transit_accessibility_bar.ipynb b/notebooks/amenities/transit_accessibility_bar.ipynb new file mode 100644 index 0000000..126843b --- /dev/null +++ b/notebooks/amenities/transit_accessibility_bar.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transit Accessibility Bar Chart\n", + "\n", + "Shows transit stops per 1,000 residents across Toronto neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_amenities` | neighbourhood \u00d7 year | transit_per_1000, transit_index, transit_count |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " transit_per_1000,\n", + " transit_index,\n", + " transit_count,\n", + " population,\n", + " amenity_tier\n", + "FROM public_marts.mart_neighbourhood_amenities\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_amenities)\n", + " AND transit_per_1000 IS NOT NULL\n", + "ORDER BY transit_per_1000 DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Sort by transit accessibility\n", + "2. Select top 20 for visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = df.head(20).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'transit_per_1000', 'transit_index', 'transit_count']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_horizontal_bar\n", + "\n", + "fig = create_horizontal_bar(\n", + " data=data,\n", + " name_column='neighbourhood_name',\n", + " value_column='transit_per_1000',\n", + " title='Top 20 Neighbourhoods by Transit Accessibility',\n", + " color='#00BCD4',\n", + " value_format='.2f',\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transit Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"City-wide Transit Statistics:\")\n", + "print(f\" Total Transit Stops: {df['transit_count'].sum():,.0f}\")\n", + "print(f\" Average per 1,000 pop: {df['transit_per_1000'].mean():.2f}\")\n", + "print(f\" Median per 1,000 pop: {df['transit_per_1000'].median():.2f}\")\n", + "print(f\" Best Access: {df['transit_per_1000'].max():.2f} per 1,000\")\n", + "print(f\" Worst Access: {df['transit_per_1000'].min():.2f} per 1,000\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/demographics/.gitkeep b/notebooks/demographics/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/demographics/age_distribution.ipynb b/notebooks/demographics/age_distribution.ipynb new file mode 100644 index 0000000..208bb6d --- /dev/null +++ b/notebooks/demographics/age_distribution.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Age Distribution Analysis\n", + "\n", + "Compares median age and age index across Toronto neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | median_age, age_index, city_avg_age |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " median_age,\n", + " age_index,\n", + " city_avg_age,\n", + " population,\n", + " income_quintile,\n", + " pct_renter_occupied\n", + "FROM public_marts.mart_neighbourhood_demographics\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n", + " AND median_age IS NOT NULL\n", + "ORDER BY median_age DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods with age data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent census year\n", + "2. Calculate deviation from city average\n", + "3. Classify as younger/older than average" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "city_avg = df['city_avg_age'].iloc[0]\n", + "df['age_category'] = df['median_age'].apply(\n", + " lambda x: 'Younger' if x < city_avg else 'Older'\n", + ")\n", + "df['age_deviation'] = df['median_age'] - city_avg\n", + "\n", + "data = df.to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"City Average Age: {city_avg:.1f}\")\n", + "print(\"\\nYoungest Neighbourhoods:\")\n", + "display(df.tail(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])\n", + "print(\"\\nOldest Neighbourhoods:\")\n", + "display(df.head(5)[['neighbourhood_name', 'median_age', 'age_index', 'pct_renter_occupied']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_ranking_bar\n", + "\n", + "fig = create_ranking_bar(\n", + " data=data,\n", + " name_column='neighbourhood_name',\n", + " value_column='median_age',\n", + " title='Youngest & Oldest Neighbourhoods (Median Age)',\n", + " top_n=10,\n", + " bottom_n=10,\n", + " color_top='#FF9800', # Orange for older\n", + " color_bottom='#2196F3', # Blue for younger\n", + " value_format='.1f',\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Age vs Income Correlation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Age by income quintile\n", + "print(\"Median Age by Income Quintile:\")\n", + "df.groupby('income_quintile')['median_age'].mean().round(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/demographics/income_choropleth.ipynb b/notebooks/demographics/income_choropleth.ipynb new file mode 100644 index 0000000..8eaef95 --- /dev/null +++ b/notebooks/demographics/income_choropleth.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Median Income Choropleth Map\n", + "\n", + "Displays median household income across Toronto's 158 neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | median_household_income, income_index, income_quintile, geometry |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_id,\n", + " neighbourhood_name,\n", + " geometry,\n", + " year,\n", + " median_household_income,\n", + " income_index,\n", + " income_quintile,\n", + " population,\n", + " unemployment_rate\n", + "FROM public_marts.mart_neighbourhood_demographics\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n", + "ORDER BY median_household_income DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent census year\n", + "2. Convert geometry to GeoJSON\n", + "3. Scale income to thousands for readability" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import json\n", + "\n", + "df['income_thousands'] = df['median_household_income'] / 1000\n", + "\n", + "gdf = gpd.GeoDataFrame(\n", + " df,\n", + " geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n", + " crs='EPSG:4326'\n", + ")\n", + "\n", + "geojson = json.loads(gdf.to_json())\n", + "data = df.drop(columns=['geometry']).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'median_household_income', 'income_index', 'income_quintile']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.choropleth import create_choropleth_figure\n", + "\n", + "fig = create_choropleth_figure(\n", + " geojson=geojson,\n", + " data=data,\n", + " location_key='neighbourhood_id',\n", + " color_column='median_household_income',\n", + " hover_data=['neighbourhood_name', 'income_index', 'income_quintile'],\n", + " color_scale='Viridis',\n", + " title='Toronto Median Household Income by Neighbourhood',\n", + " zoom=10,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Income Quintile Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.groupby('income_quintile')['median_household_income'].agg(['count', 'mean', 'min', 'max']).round(0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/demographics/population_density_bar.ipynb b/notebooks/demographics/population_density_bar.ipynb new file mode 100644 index 0000000..8c7cd23 --- /dev/null +++ b/notebooks/demographics/population_density_bar.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Population Density Bar Chart\n", + "\n", + "Shows population density (people per sq km) across Toronto neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_demographics` | neighbourhood \u00d7 year | population_density, population, land_area_sqkm |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " population_density,\n", + " population,\n", + " land_area_sqkm,\n", + " median_household_income,\n", + " pct_renter_occupied\n", + "FROM public_marts.mart_neighbourhood_demographics\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_demographics)\n", + " AND population_density IS NOT NULL\n", + "ORDER BY population_density DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Sort by population density\n", + "2. Select top 20 most dense neighbourhoods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = df.head(20).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'population_density', 'population', 'land_area_sqkm']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_horizontal_bar` from `portfolio_app.figures.bar_charts`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_horizontal_bar\n", + "\n", + "fig = create_horizontal_bar(\n", + " data=data,\n", + " name_column='neighbourhood_name',\n", + " value_column='population_density',\n", + " title='Top 20 Most Dense Neighbourhoods',\n", + " color='#9C27B0',\n", + " value_format=',.0f',\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Density Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"City-wide Statistics:\")\n", + "print(f\" Total Population: {df['population'].sum():,.0f}\")\n", + "print(f\" Total Area: {df['land_area_sqkm'].sum():,.1f} sq km\")\n", + "print(f\" Average Density: {df['population_density'].mean():,.0f} per sq km\")\n", + "print(f\" Max Density: {df['population_density'].max():,.0f} per sq km\")\n", + "print(f\" Min Density: {df['population_density'].min():,.0f} per sq km\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/housing/affordability_choropleth.ipynb b/notebooks/housing/affordability_choropleth.ipynb new file mode 100644 index 0000000..ca2f054 --- /dev/null +++ b/notebooks/housing/affordability_choropleth.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Affordability Index Choropleth Map\n", + "\n", + "Displays housing affordability across Toronto's 158 neighbourhoods. Index of 100 = city average." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_id,\n", + " neighbourhood_name,\n", + " geometry,\n", + " year,\n", + " affordability_index,\n", + " rent_to_income_pct,\n", + " avg_rent_2bed,\n", + " median_household_income,\n", + " is_affordable\n", + "FROM public_marts.mart_neighbourhood_housing\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n", + "ORDER BY affordability_index ASC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent year\n", + "2. Convert geometry to GeoJSON\n", + "3. Lower index = more affordable (inverted for visualization clarity)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import json\n", + "\n", + "gdf = gpd.GeoDataFrame(\n", + " df,\n", + " geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n", + " crs='EPSG:4326'\n", + ")\n", + "\n", + "geojson = json.loads(gdf.to_json())\n", + "data = df.drop(columns=['geometry']).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'affordability_index', 'rent_to_income_pct', 'avg_rent_2bed', 'is_affordable']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n", + "\n", + "**Key Parameters:**\n", + "- `color_column`: 'affordability_index'\n", + "- `color_scale`: 'RdYlGn_r' (reversed: green=affordable, red=expensive)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.choropleth import create_choropleth_figure\n", + "\n", + "fig = create_choropleth_figure(\n", + " geojson=geojson,\n", + " data=data,\n", + " location_key='neighbourhood_id',\n", + " color_column='affordability_index',\n", + " hover_data=['neighbourhood_name', 'rent_to_income_pct', 'avg_rent_2bed'],\n", + " color_scale='RdYlGn_r', # Reversed: lower index (affordable) = green\n", + " title='Toronto Housing Affordability Index',\n", + " zoom=10,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index Interpretation\n", + "\n", + "| Index | Meaning |\n", + "|-------|--------|\n", + "| < 100 | More affordable than city average |\n", + "| = 100 | City average affordability |\n", + "| > 100 | Less affordable than city average |\n", + "\n", + "Affordability calculated as: `rent_to_income_pct / city_avg_rent_to_income * 100`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/housing/rent_trend_line.ipynb b/notebooks/housing/rent_trend_line.ipynb new file mode 100644 index 0000000..a658ab1 --- /dev/null +++ b/notebooks/housing/rent_trend_line.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rent Trend Line Chart\n", + "\n", + "Shows 5-year rental price trends across Toronto neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | year, avg_rent_2bed, rent_yoy_change_pct |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "# City-wide average rent by year\n", + "query = \"\"\"\n", + "SELECT\n", + " year,\n", + " AVG(avg_rent_bachelor) as avg_rent_bachelor,\n", + " AVG(avg_rent_1bed) as avg_rent_1bed,\n", + " AVG(avg_rent_2bed) as avg_rent_2bed,\n", + " AVG(avg_rent_3bed) as avg_rent_3bed,\n", + " AVG(rent_yoy_change_pct) as avg_yoy_change\n", + "FROM public_marts.mart_neighbourhood_housing\n", + "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_housing)\n", + "GROUP BY year\n", + "ORDER BY year\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} years of rent data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Aggregate rent by year (city-wide average)\n", + "2. Convert year to datetime for proper x-axis\n", + "3. Reshape for multi-line chart by bedroom type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create date column from year\n", + "df['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n", + "\n", + "# Melt for multi-line chart\n", + "df_melted = df.melt(\n", + " id_vars=['year', 'date'],\n", + " value_vars=['avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed'],\n", + " var_name='bedroom_type',\n", + " value_name='avg_rent'\n", + ")\n", + "\n", + "# Clean labels\n", + "df_melted['bedroom_type'] = df_melted['bedroom_type'].map({\n", + " 'avg_rent_bachelor': 'Bachelor',\n", + " 'avg_rent_1bed': '1 Bedroom',\n", + " 'avg_rent_2bed': '2 Bedroom',\n", + " 'avg_rent_3bed': '3 Bedroom'\n", + "})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['year', 'avg_rent_bachelor', 'avg_rent_1bed', 'avg_rent_2bed', 'avg_rent_3bed', 'avg_yoy_change']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_price_time_series` from `portfolio_app.figures.time_series`.\n", + "\n", + "**Key Parameters:**\n", + "- `date_column`: 'date'\n", + "- `price_column`: 'avg_rent'\n", + "- `group_column`: 'bedroom_type' (for multi-line)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.time_series import create_price_time_series\n", + "\n", + "data = df_melted.to_dict('records')\n", + "\n", + "fig = create_price_time_series(\n", + " data=data,\n", + " date_column='date',\n", + " price_column='avg_rent',\n", + " group_column='bedroom_type',\n", + " title='Toronto Average Rent Trend (5 Years)',\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### YoY Change Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show year-over-year changes\n", + "print(\"Year-over-Year Rent Change (%)\")\n", + "df[['year', 'avg_yoy_change']].dropna()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/housing/tenure_breakdown_bar.ipynb b/notebooks/housing/tenure_breakdown_bar.ipynb new file mode 100644 index 0000000..413f6b8 --- /dev/null +++ b/notebooks/housing/tenure_breakdown_bar.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Housing Tenure Breakdown Bar Chart\n", + "\n", + "Shows the distribution of owner-occupied vs renter-occupied dwellings across neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_housing` | neighbourhood \u00d7 year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " pct_owner_occupied,\n", + " pct_renter_occupied,\n", + " income_quintile,\n", + " total_rental_units,\n", + " average_dwelling_value\n", + "FROM public_marts.mart_neighbourhood_housing\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n", + " AND pct_owner_occupied IS NOT NULL\n", + "ORDER BY pct_renter_occupied DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods with tenure data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent year with tenure data\n", + "2. Melt owner/renter columns for stacked bar\n", + "3. Sort by renter percentage (highest first)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare for stacked bar\n", + "df_stacked = df.melt(\n", + " id_vars=['neighbourhood_name', 'income_quintile'],\n", + " value_vars=['pct_owner_occupied', 'pct_renter_occupied'],\n", + " var_name='tenure_type',\n", + " value_name='percentage'\n", + ")\n", + "\n", + "df_stacked['tenure_type'] = df_stacked['tenure_type'].map({\n", + " 'pct_owner_occupied': 'Owner',\n", + " 'pct_renter_occupied': 'Renter'\n", + "})\n", + "\n", + "data = df_stacked.to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Highest Renter Neighbourhoods:\")\n", + "df[['neighbourhood_name', 'pct_renter_occupied', 'pct_owner_occupied', 'income_quintile']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`.\n", + "\n", + "**Key Parameters:**\n", + "- `x_column`: 'neighbourhood_name'\n", + "- `value_column`: 'percentage'\n", + "- `category_column`: 'tenure_type'\n", + "- `show_percentages`: True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_stacked_bar\n", + "\n", + "# Show top 20 by renter percentage\n", + "top_20_names = df.head(20)['neighbourhood_name'].tolist()\n", + "data_filtered = [d for d in data if d['neighbourhood_name'] in top_20_names]\n", + "\n", + "fig = create_stacked_bar(\n", + " data=data_filtered,\n", + " x_column='neighbourhood_name',\n", + " value_column='percentage',\n", + " category_column='tenure_type',\n", + " title='Housing Tenure Mix - Top 20 Renter Neighbourhoods',\n", + " color_map={'Owner': '#4CAF50', 'Renter': '#2196F3'},\n", + " show_percentages=True,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### City-Wide Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# City-wide averages\n", + "print(f\"City Average Owner-Occupied: {df['pct_owner_occupied'].mean():.1f}%\")\n", + "print(f\"City Average Renter-Occupied: {df['pct_renter_occupied'].mean():.1f}%\")\n", + "\n", + "# By income quintile\n", + "print(\"\\nTenure by Income Quintile:\")\n", + "df.groupby('income_quintile')[['pct_owner_occupied', 'pct_renter_occupied']].mean().round(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/overview/income_safety_scatter.ipynb b/notebooks/overview/income_safety_scatter.ipynb new file mode 100644 index 0000000..da41fb2 --- /dev/null +++ b/notebooks/overview/income_safety_scatter.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Income vs Safety Scatter Plot\n", + "\n", + "Explores the correlation between median household income and safety score across Toronto neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_overview` | neighbourhood \u00d7 year | neighbourhood_name, median_household_income, safety_score, population |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " median_household_income,\n", + " safety_score,\n", + " population,\n", + " livability_score,\n", + " crime_rate_per_100k\n", + "FROM public_marts.mart_neighbourhood_overview\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n", + " AND median_household_income IS NOT NULL\n", + " AND safety_score IS NOT NULL\n", + "ORDER BY median_household_income DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods with income and safety data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter out null values for income and safety\n", + "2. Optionally scale income to thousands for readability\n", + "3. Pass to scatter figure factory with optional trendline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale income to thousands for better axis readability\n", + "df['income_thousands'] = df['median_household_income'] / 1000\n", + "\n", + "# Prepare data for figure factory\n", + "data = df.to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'median_household_income', 'safety_score', 'crime_rate_per_100k']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_scatter_figure` from `portfolio_app.figures.scatter`.\n", + "\n", + "**Key Parameters:**\n", + "- `x_column`: 'income_thousands' (median household income in $K)\n", + "- `y_column`: 'safety_score' (0-100 percentile rank)\n", + "- `name_column`: 'neighbourhood_name' (hover label)\n", + "- `size_column`: 'population' (optional, bubble size)\n", + "- `trendline`: True (adds OLS regression line)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.scatter import create_scatter_figure\n", + "\n", + "fig = create_scatter_figure(\n", + " data=data,\n", + " x_column='income_thousands',\n", + " y_column='safety_score',\n", + " name_column='neighbourhood_name',\n", + " size_column='population',\n", + " title='Income vs Safety by Neighbourhood',\n", + " x_title='Median Household Income ($K)',\n", + " y_title='Safety Score (0-100)',\n", + " trendline=True,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "This scatter plot reveals the relationship between income and safety:\n", + "\n", + "- **Positive correlation**: Higher income neighbourhoods tend to have higher safety scores\n", + "- **Bubble size**: Represents population (larger = more people)\n", + "- **Trendline**: Orange dashed line shows the overall trend\n", + "- **Outliers**: Neighbourhoods far from the trendline are interesting cases\n", + " - Above line: Safer than income would predict\n", + " - Below line: Less safe than income would predict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate correlation coefficient\n", + "correlation = df['median_household_income'].corr(df['safety_score'])\n", + "print(f\"Correlation coefficient (Income vs Safety): {correlation:.3f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/overview/livability_choropleth.ipynb b/notebooks/overview/livability_choropleth.ipynb new file mode 100644 index 0000000..580bc59 --- /dev/null +++ b/notebooks/overview/livability_choropleth.ipynb @@ -0,0 +1,157 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Livability Score Choropleth Map\n", + "\n", + "Displays neighbourhood livability scores on an interactive map of Toronto's 158 neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_overview` | neighbourhood × year | livability_score, safety_score, affordability_score, amenity_score, geometry |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import pandas as pd\nfrom sqlalchemy import create_engine\nfrom dotenv import load_dotenv\nimport os\n\n# Load .env from project root\nload_dotenv('../../.env')\n\nengine = create_engine(os.environ['DATABASE_URL'])\n\nquery = \"\"\"\nSELECT\n neighbourhood_id,\n neighbourhood_name,\n geometry,\n year,\n livability_score,\n safety_score,\n affordability_score,\n amenity_score,\n population,\n median_household_income\nFROM public_marts.mart_neighbourhood_overview\nWHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\nORDER BY livability_score DESC\n\"\"\"\n\ndf = pd.read_sql(query, engine)\nprint(f\"Loaded {len(df)} neighbourhoods\")" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent year of data\n", + "2. Extract GeoJSON from PostGIS geometry column\n", + "3. Pass to choropleth figure factory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform geometry to GeoJSON\n", + "import geopandas as gpd\n", + "import json\n", + "\n", + "# Convert WKB geometry to GeoDataFrame\n", + "gdf = gpd.GeoDataFrame(\n", + " df,\n", + " geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n", + " crs='EPSG:4326'\n", + ")\n", + "\n", + "# Create GeoJSON FeatureCollection\n", + "geojson = json.loads(gdf.to_json())\n", + "\n", + "# Prepare data for figure factory\n", + "data = df.drop(columns=['geometry']).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'livability_score', 'safety_score', 'affordability_score', 'amenity_score']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n", + "\n", + "**Key Parameters:**\n", + "- `geojson`: GeoJSON FeatureCollection with neighbourhood boundaries\n", + "- `data`: List of dicts with neighbourhood_id and scores\n", + "- `location_key`: 'neighbourhood_id'\n", + "- `color_column`: 'livability_score' (or safety_score, etc.)\n", + "- `color_scale`: 'RdYlGn' (red=low, yellow=mid, green=high)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.choropleth import create_choropleth_figure\n", + "\n", + "fig = create_choropleth_figure(\n", + " geojson=geojson,\n", + " data=data,\n", + " location_key='neighbourhood_id',\n", + " color_column='livability_score',\n", + " hover_data=['neighbourhood_name', 'safety_score', 'affordability_score', 'amenity_score'],\n", + " color_scale='RdYlGn',\n", + " title='Toronto Neighbourhood Livability Score',\n", + " zoom=10,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Score Components\n", + "\n", + "The livability score is a weighted composite:\n", + "\n", + "| Component | Weight | Source |\n", + "|-----------|--------|--------|\n", + "| Safety | 30% | Inverse of crime rate per 100K |\n", + "| Affordability | 40% | Inverse of rent-to-income ratio |\n", + "| Amenities | 30% | Amenities per 1,000 residents |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/overview/top_bottom_10_bar.ipynb b/notebooks/overview/top_bottom_10_bar.ipynb new file mode 100644 index 0000000..6468ea3 --- /dev/null +++ b/notebooks/overview/top_bottom_10_bar.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Top & Bottom 10 Neighbourhoods Bar Chart\n", + "\n", + "Horizontal bar chart showing the highest and lowest scoring neighbourhoods by livability." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_overview` | neighbourhood \u00d7 year | neighbourhood_name, livability_score |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " livability_score,\n", + " safety_score,\n", + " affordability_score,\n", + " amenity_score\n", + "FROM public_marts.mart_neighbourhood_overview\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_overview)\n", + " AND livability_score IS NOT NULL\n", + "ORDER BY livability_score DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods with scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Sort by livability_score descending\n", + "2. Take top 10 and bottom 10\n", + "3. Pass to ranking bar figure factory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The figure factory handles top/bottom selection internally\n", + "# Just prepare as list of dicts\n", + "data = df.to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Top 5:\")\n", + "display(df.head(5))\n", + "print(\"\\nBottom 5:\")\n", + "display(df.tail(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_ranking_bar` from `portfolio_app.figures.bar_charts`.\n", + "\n", + "**Key Parameters:**\n", + "- `data`: List of dicts with all neighbourhoods\n", + "- `name_column`: 'neighbourhood_name'\n", + "- `value_column`: 'livability_score'\n", + "- `top_n`: 10 (green bars)\n", + "- `bottom_n`: 10 (red bars)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_ranking_bar\n", + "\n", + "fig = create_ranking_bar(\n", + " data=data,\n", + " name_column='neighbourhood_name',\n", + " value_column='livability_score',\n", + " title='Top & Bottom 10 Neighbourhoods by Livability',\n", + " top_n=10,\n", + " bottom_n=10,\n", + " color_top='#4CAF50', # Green for top performers\n", + " color_bottom='#F44336', # Red for bottom performers\n", + " value_format='.1f',\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretation\n", + "\n", + "- **Green bars**: Highest livability scores (best combination of safety, affordability, and amenities)\n", + "- **Red bars**: Lowest livability scores (areas that may need targeted investment)\n", + "\n", + "The ranking bar chart provides quick context for which neighbourhoods stand out at either extreme." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/safety/.gitkeep b/notebooks/safety/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/safety/crime_breakdown_bar.ipynb b/notebooks/safety/crime_breakdown_bar.ipynb new file mode 100644 index 0000000..2ff6d43 --- /dev/null +++ b/notebooks/safety/crime_breakdown_bar.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crime Type Breakdown Bar Chart\n", + "\n", + "Stacked bar chart showing crime composition by Major Crime Indicator (MCI) categories." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | assault_count, auto_theft_count, break_enter_count, robbery_count, etc. |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_name,\n", + " assault_count,\n", + " auto_theft_count,\n", + " break_enter_count,\n", + " robbery_count,\n", + " theft_over_count,\n", + " homicide_count,\n", + " total_incidents,\n", + " crime_rate_per_100k\n", + "FROM public_marts.mart_neighbourhood_safety\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n", + "ORDER BY total_incidents DESC\n", + "LIMIT 15\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded top {len(df)} neighbourhoods by crime volume\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Select top 15 neighbourhoods by total incidents\n", + "2. Melt crime type columns into rows\n", + "3. Pass to stacked bar figure factory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_melted = df.melt(\n", + " id_vars=['neighbourhood_name', 'total_incidents'],\n", + " value_vars=['assault_count', 'auto_theft_count', 'break_enter_count', \n", + " 'robbery_count', 'theft_over_count', 'homicide_count'],\n", + " var_name='crime_type',\n", + " value_name='count'\n", + ")\n", + "\n", + "# Clean labels\n", + "df_melted['crime_type'] = df_melted['crime_type'].str.replace('_count', '').str.replace('_', ' ').str.title()\n", + "\n", + "data = df_melted.to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'assault_count', 'auto_theft_count', 'break_enter_count', 'total_incidents']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_stacked_bar` from `portfolio_app.figures.bar_charts`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.bar_charts import create_stacked_bar\n", + "\n", + "fig = create_stacked_bar(\n", + " data=data,\n", + " x_column='neighbourhood_name',\n", + " value_column='count',\n", + " category_column='crime_type',\n", + " title='Crime Type Breakdown - Top 15 Neighbourhoods',\n", + " color_map={\n", + " 'Assault': '#d62728',\n", + " 'Auto Theft': '#ff7f0e',\n", + " 'Break Enter': '#9467bd',\n", + " 'Robbery': '#8c564b',\n", + " 'Theft Over': '#e377c2',\n", + " 'Homicide': '#1f77b4'\n", + " },\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MCI Categories\n", + "\n", + "| Category | Description |\n", + "|----------|------------|\n", + "| Assault | Physical attacks |\n", + "| Auto Theft | Vehicle theft |\n", + "| Break & Enter | Burglary |\n", + "| Robbery | Theft with force/threat |\n", + "| Theft Over | Theft > $5,000 |\n", + "| Homicide | Murder/manslaughter |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/safety/crime_rate_choropleth.ipynb b/notebooks/safety/crime_rate_choropleth.ipynb new file mode 100644 index 0000000..05c3225 --- /dev/null +++ b/notebooks/safety/crime_rate_choropleth.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crime Rate Choropleth Map\n", + "\n", + "Displays crime rates per 100,000 population across Toronto's 158 neighbourhoods." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | crime_rate_per_100k, crime_index, safety_tier, geometry |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " neighbourhood_id,\n", + " neighbourhood_name,\n", + " geometry,\n", + " year,\n", + " crime_rate_per_100k,\n", + " crime_index,\n", + " safety_tier,\n", + " total_incidents,\n", + " population\n", + "FROM public_marts.mart_neighbourhood_safety\n", + "WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_safety)\n", + "ORDER BY crime_rate_per_100k DESC\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} neighbourhoods\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Filter to most recent year\n", + "2. Convert geometry to GeoJSON\n", + "3. Use reversed color scale (green=low crime, red=high crime)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import json\n", + "\n", + "gdf = gpd.GeoDataFrame(\n", + " df,\n", + " geometry=gpd.GeoSeries.from_wkb(df['geometry']),\n", + " crs='EPSG:4326'\n", + ")\n", + "\n", + "geojson = json.loads(gdf.to_json())\n", + "data = df.drop(columns=['geometry']).to_dict('records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['neighbourhood_name', 'crime_rate_per_100k', 'crime_index', 'safety_tier', 'total_incidents']].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_choropleth_figure` from `portfolio_app.figures.choropleth`.\n", + "\n", + "**Key Parameters:**\n", + "- `color_column`: 'crime_rate_per_100k'\n", + "- `color_scale`: 'RdYlGn_r' (red=high crime, green=low crime)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.choropleth import create_choropleth_figure\n", + "\n", + "fig = create_choropleth_figure(\n", + " geojson=geojson,\n", + " data=data,\n", + " location_key='neighbourhood_id',\n", + " color_column='crime_rate_per_100k',\n", + " hover_data=['neighbourhood_name', 'crime_index', 'total_incidents'],\n", + " color_scale='RdYlGn_r',\n", + " title='Toronto Crime Rate per 100,000 Population',\n", + " zoom=10,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Safety Tier Interpretation\n", + "\n", + "| Tier | Meaning |\n", + "|------|--------|\n", + "| 1 | Highest crime (top 20%) |\n", + "| 2-4 | Middle tiers |\n", + "| 5 | Lowest crime (bottom 20%) |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/safety/crime_trend_line.ipynb b/notebooks/safety/crime_trend_line.ipynb new file mode 100644 index 0000000..3e3a3a5 --- /dev/null +++ b/notebooks/safety/crime_trend_line.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crime Trend Line Chart\n", + "\n", + "Shows 5-year crime rate trends across Toronto." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Data Reference\n", + "\n", + "### Source Tables\n", + "\n", + "| Table | Grain | Key Columns |\n", + "|-------|-------|-------------|\n", + "| `mart_neighbourhood_safety` | neighbourhood \u00d7 year | year, crime_rate_per_100k, crime_yoy_change_pct |\n", + "\n", + "### SQL Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "from dotenv import load_dotenv\n", + "import os\n", + "\n", + "# Load .env from project root\n", + "load_dotenv('../../.env')\n", + "\n", + "engine = create_engine(os.environ['DATABASE_URL'])\n", + "\n", + "query = \"\"\"\n", + "SELECT\n", + " year,\n", + " AVG(crime_rate_per_100k) as avg_crime_rate,\n", + " AVG(assault_rate_per_100k) as avg_assault_rate,\n", + " AVG(auto_theft_rate_per_100k) as avg_auto_theft_rate,\n", + " AVG(break_enter_rate_per_100k) as avg_break_enter_rate,\n", + " SUM(total_incidents) as total_city_incidents,\n", + " AVG(crime_yoy_change_pct) as avg_yoy_change\n", + "FROM public_marts.mart_neighbourhood_safety\n", + "WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_safety)\n", + "GROUP BY year\n", + "ORDER BY year\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(query, engine)\n", + "print(f\"Loaded {len(df)} years of crime data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transformation Steps\n", + "\n", + "1. Aggregate by year (city-wide)\n", + "2. Convert year to datetime\n", + "3. Melt for multi-line by crime type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['date'] = pd.to_datetime(df['year'].astype(str) + '-01-01')\n", + "\n", + "# Melt for multi-line\n", + "df_melted = df.melt(\n", + " id_vars=['year', 'date'],\n", + " value_vars=['avg_assault_rate', 'avg_auto_theft_rate', 'avg_break_enter_rate'],\n", + " var_name='crime_type',\n", + " value_name='rate_per_100k'\n", + ")\n", + "\n", + "df_melted['crime_type'] = df_melted['crime_type'].map({\n", + " 'avg_assault_rate': 'Assault',\n", + " 'avg_auto_theft_rate': 'Auto Theft',\n", + " 'avg_break_enter_rate': 'Break & Enter'\n", + "})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['year', 'avg_crime_rate', 'total_city_incidents', 'avg_yoy_change']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Data Visualization\n", + "\n", + "### Figure Factory\n", + "\n", + "Uses `create_price_time_series` (reused for any numeric trend)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "\n", + "from portfolio_app.figures.time_series import create_price_time_series\n", + "\n", + "data = df_melted.to_dict('records')\n", + "\n", + "fig = create_price_time_series(\n", + " data=data,\n", + " date_column='date',\n", + " price_column='rate_per_100k',\n", + " group_column='crime_type',\n", + " title='Toronto Crime Trends by Type (5 Years)',\n", + ")\n", + "\n", + "# Remove dollar sign formatting since this is rate data\n", + "fig.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overall Trend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Total crime rate trend\n", + "total_data = df[['date', 'avg_crime_rate']].rename(columns={'avg_crime_rate': 'total_rate'}).to_dict('records')\n", + "\n", + "fig2 = create_price_time_series(\n", + " data=total_data,\n", + " date_column='date',\n", + " price_column='total_rate',\n", + " title='Toronto Overall Crime Rate Trend',\n", + ")\n", + "fig2.update_layout(yaxis_tickprefix='', yaxis_title='Rate per 100K')\n", + "fig2.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/portfolio_app/__init__.py b/portfolio_app/__init__.py new file mode 100644 index 0000000..a530568 --- /dev/null +++ b/portfolio_app/__init__.py @@ -0,0 +1,3 @@ +"""Analytics Portfolio Application.""" + +__version__ = "0.1.0" diff --git a/portfolio_app/app.py b/portfolio_app/app.py new file mode 100644 index 0000000..ddee1c0 --- /dev/null +++ b/portfolio_app/app.py @@ -0,0 +1,58 @@ +"""Dash application factory with Pages routing.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc, html + +from .components import create_sidebar +from .config import get_settings + + +def create_app() -> dash.Dash: + """Create and configure the Dash application.""" + app = dash.Dash( + __name__, + use_pages=True, + suppress_callback_exceptions=True, + title="Analytics Portfolio", + external_stylesheets=dmc.styles.ALL, + ) + + app.layout = dmc.MantineProvider( + id="mantine-provider", + children=[ + dcc.Location(id="url", refresh=False), + dcc.Store(id="theme-store", storage_type="local", data="dark"), + dcc.Store(id="theme-init-dummy"), # Dummy store for theme init callback + html.Div( + [ + create_sidebar(), + html.Div( + dash.page_container, + className="page-content-wrapper", + ), + ], + ), + ], + theme={ + "primaryColor": "blue", + "fontFamily": "'Inter', sans-serif", + }, + defaultColorScheme="dark", + ) + + # Import callbacks to register them + from . import callbacks # noqa: F401 + + return app + + +def main() -> None: + """Run the development server.""" + settings = get_settings() + app = create_app() + app.run(debug=settings.dash_debug, host="0.0.0.0", port=8050) + + +if __name__ == "__main__": + main() diff --git a/portfolio_app/assets/.gitkeep b/portfolio_app/assets/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/portfolio_app/assets/sidebar.css b/portfolio_app/assets/sidebar.css new file mode 100644 index 0000000..8956497 --- /dev/null +++ b/portfolio_app/assets/sidebar.css @@ -0,0 +1,139 @@ +/* Floating sidebar navigation styles */ + +/* Sidebar container */ +.floating-sidebar { + position: fixed; + left: 16px; + top: 50%; + transform: translateY(-50%); + width: 60px; + padding: 16px 8px; + border-radius: 32px; + z-index: 1000; + display: flex; + flex-direction: column; + align-items: center; + gap: 8px; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); + transition: background-color 0.2s ease; +} + +/* Page content offset to prevent sidebar overlap */ +.page-content-wrapper { + margin-left: 92px; /* sidebar width (60px) + left margin (16px) + gap (16px) */ + min-height: 100vh; +} + +/* Dark theme (default) */ +[data-mantine-color-scheme="dark"] .floating-sidebar { + background-color: #141414; +} + +[data-mantine-color-scheme="dark"] body { + background-color: #000000; +} + +/* Light theme */ +[data-mantine-color-scheme="light"] .floating-sidebar { + background-color: #f0f0f0; +} + +[data-mantine-color-scheme="light"] body { + background-color: #ffffff; +} + +/* Brand initials styling */ +.sidebar-brand { + width: 40px; + height: 40px; + display: flex; + align-items: center; + justify-content: center; + border-radius: 50%; + background-color: var(--mantine-color-blue-filled); + margin-bottom: 4px; + transition: transform 0.2s ease; +} + +.sidebar-brand:hover { + transform: scale(1.05); +} + +.sidebar-brand-link { + font-weight: 700; + font-size: 16px; + color: white; + text-decoration: none; + line-height: 1; +} + +/* Divider between sections */ +.sidebar-divider { + width: 32px; + height: 1px; + background-color: var(--mantine-color-dimmed); + margin: 4px 0; + opacity: 0.3; +} + +/* Active nav icon indicator */ +.nav-icon-active { + background-color: var(--mantine-color-blue-filled) !important; +} + +/* Navigation icon hover effects */ +.floating-sidebar .mantine-ActionIcon-root { + transition: transform 0.15s ease, background-color 0.15s ease; +} + +.floating-sidebar .mantine-ActionIcon-root:hover { + transform: scale(1.1); +} + +/* Ensure links don't have underlines */ +.floating-sidebar a { + text-decoration: none; +} + +/* Theme toggle specific styling */ +#theme-toggle { + transition: transform 0.3s ease; +} + +#theme-toggle:hover { + transform: rotate(15deg) scale(1.1); +} + +/* Responsive adjustments for smaller screens */ +@media (max-width: 768px) { + .floating-sidebar { + left: 8px; + width: 50px; + padding: 12px 6px; + border-radius: 25px; + } + + .page-content-wrapper { + margin-left: 70px; + } + + .sidebar-brand { + width: 34px; + height: 34px; + } + + .sidebar-brand-link { + font-size: 14px; + } +} + +/* Very small screens - hide sidebar, show minimal navigation */ +@media (max-width: 480px) { + .floating-sidebar { + display: none; + } + + .page-content-wrapper { + margin-left: 0; + } +} diff --git a/portfolio_app/callbacks/__init__.py b/portfolio_app/callbacks/__init__.py new file mode 100644 index 0000000..a592365 --- /dev/null +++ b/portfolio_app/callbacks/__init__.py @@ -0,0 +1,5 @@ +"""Application-level callbacks for the portfolio app.""" + +from . import contact, sidebar, theme + +__all__ = ["contact", "sidebar", "theme"] diff --git a/portfolio_app/callbacks/contact.py b/portfolio_app/callbacks/contact.py new file mode 100644 index 0000000..1ccdccd --- /dev/null +++ b/portfolio_app/callbacks/contact.py @@ -0,0 +1,214 @@ +"""Contact form submission callback with Formspree integration.""" + +import re +from typing import Any + +import dash_mantine_components as dmc +import requests +from dash import Input, Output, State, callback, no_update +from dash_iconify import DashIconify + +FORMSPREE_ENDPOINT = "https://formspree.io/f/mqelqzpd" +EMAIL_REGEX = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$") + + +def _validate_form( + name: str | None, email: str | None, message: str | None +) -> str | None: + """Validate form fields and return error message if invalid.""" + if not name or not name.strip(): + return "Please enter your name." + if not email or not email.strip(): + return "Please enter your email address." + if not EMAIL_REGEX.match(email.strip()): + return "Please enter a valid email address." + if not message or not message.strip(): + return "Please enter a message." + return None + + +def _create_success_alert() -> dmc.Alert: + """Create success feedback alert.""" + return dmc.Alert( + "Thank you for your message! I'll get back to you soon.", + title="Message Sent", + color="green", + variant="light", + icon=DashIconify(icon="tabler:check", width=20), + withCloseButton=True, + ) + + +def _create_error_alert(message: str) -> dmc.Alert: + """Create error feedback alert.""" + return dmc.Alert( + message, + title="Error", + color="red", + variant="light", + icon=DashIconify(icon="tabler:alert-circle", width=20), + withCloseButton=True, + ) + + +@callback( # type: ignore[misc] + Output("contact-feedback", "children"), + Output("contact-submit", "loading"), + Output("contact-name", "value"), + Output("contact-email", "value"), + Output("contact-subject", "value"), + Output("contact-message", "value"), + Output("contact-name", "error"), + Output("contact-email", "error"), + Output("contact-message", "error"), + Input("contact-submit", "n_clicks"), + State("contact-name", "value"), + State("contact-email", "value"), + State("contact-subject", "value"), + State("contact-message", "value"), + State("contact-gotcha", "value"), + prevent_initial_call=True, +) +def submit_contact_form( + n_clicks: int | None, + name: str | None, + email: str | None, + subject: str | None, + message: str | None, + gotcha: str | None, +) -> tuple[Any, ...]: + """Submit contact form to Formspree. + + Args: + n_clicks: Button click count. + name: User's name. + email: User's email address. + subject: Message subject (optional). + message: Message content. + gotcha: Honeypot field value (should be empty for real users). + + Returns: + Tuple of (feedback, loading, name, email, subject, message, + name_error, email_error, message_error). + """ + if not n_clicks: + return (no_update,) * 9 + + # Check honeypot - if filled, silently "succeed" (it's a bot) + if gotcha: + return ( + _create_success_alert(), + False, + "", + "", + None, + "", + None, + None, + None, + ) + + # Validate form + validation_error = _validate_form(name, email, message) + if validation_error: + # Determine which field has the error + name_error = "Required" if not name or not name.strip() else None + email_error = None + message_error = "Required" if not message or not message.strip() else None + + if not email or not email.strip(): + email_error = "Required" + elif not EMAIL_REGEX.match(email.strip()): + email_error = "Invalid email format" + + return ( + _create_error_alert(validation_error), + False, + no_update, + no_update, + no_update, + no_update, + name_error, + email_error, + message_error, + ) + + # Prepare form data (validation passed, so name/email/message are not None) + assert name is not None + assert email is not None + assert message is not None + form_data = { + "name": name.strip(), + "email": email.strip(), + "subject": subject or "General Inquiry", + "message": message.strip(), + "_gotcha": "", # Formspree honeypot + } + + # Submit to Formspree + try: + response = requests.post( + FORMSPREE_ENDPOINT, + json=form_data, + headers={ + "Accept": "application/json", + "Content-Type": "application/json", + }, + timeout=10, + ) + + if response.status_code == 200: + # Success - clear form + return ( + _create_success_alert(), + False, + "", + "", + None, + "", + None, + None, + None, + ) + else: + # Formspree returned an error + return ( + _create_error_alert( + "Failed to send message. Please try again or use direct contact." + ), + False, + no_update, + no_update, + no_update, + no_update, + None, + None, + None, + ) + + except requests.exceptions.Timeout: + return ( + _create_error_alert("Request timed out. Please try again."), + False, + no_update, + no_update, + no_update, + no_update, + None, + None, + None, + ) + except requests.exceptions.RequestException: + return ( + _create_error_alert( + "Network error. Please check your connection and try again." + ), + False, + no_update, + no_update, + no_update, + no_update, + None, + None, + None, + ) diff --git a/portfolio_app/callbacks/sidebar.py b/portfolio_app/callbacks/sidebar.py new file mode 100644 index 0000000..b58798e --- /dev/null +++ b/portfolio_app/callbacks/sidebar.py @@ -0,0 +1,25 @@ +"""Sidebar navigation callbacks for active state updates.""" + +from typing import Any + +from dash import Input, Output, callback + +from portfolio_app.components.sidebar import create_sidebar_content + + +@callback( # type: ignore[misc] + Output("floating-sidebar", "children"), + Input("url", "pathname"), + prevent_initial_call=False, +) +def update_sidebar_active_state(pathname: str) -> list[Any]: + """Update sidebar to highlight the current page. + + Args: + pathname: Current URL pathname from dcc.Location. + + Returns: + Updated sidebar content with correct active state. + """ + current_path = pathname or "/" + return create_sidebar_content(current_path=current_path) diff --git a/portfolio_app/callbacks/theme.py b/portfolio_app/callbacks/theme.py new file mode 100644 index 0000000..7ca5cbe --- /dev/null +++ b/portfolio_app/callbacks/theme.py @@ -0,0 +1,38 @@ +"""Theme toggle callbacks using clientside JavaScript.""" + +from dash import Input, Output, State, clientside_callback + +# Toggle theme on button click +# Stores new theme value and updates the DOM attribute +clientside_callback( + """ + function(n_clicks, currentTheme) { + if (n_clicks === undefined || n_clicks === null) { + return window.dash_clientside.no_update; + } + const newTheme = currentTheme === 'dark' ? 'light' : 'dark'; + document.documentElement.setAttribute('data-mantine-color-scheme', newTheme); + return newTheme; + } + """, + Output("theme-store", "data"), + Input("theme-toggle", "n_clicks"), + State("theme-store", "data"), + prevent_initial_call=True, +) + +# Initialize theme from localStorage on page load +# Uses a dummy output since we only need the side effect of setting the DOM attribute +clientside_callback( + """ + function(theme) { + if (theme) { + document.documentElement.setAttribute('data-mantine-color-scheme', theme); + } + return theme; + } + """, + Output("theme-init-dummy", "data"), + Input("theme-store", "data"), + prevent_initial_call=False, +) diff --git a/portfolio_app/components/.gitkeep b/portfolio_app/components/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/portfolio_app/components/__init__.py b/portfolio_app/components/__init__.py new file mode 100644 index 0000000..264dda3 --- /dev/null +++ b/portfolio_app/components/__init__.py @@ -0,0 +1,16 @@ +"""Shared Dash components for the portfolio application.""" + +from .map_controls import create_map_controls, create_metric_selector +from .metric_card import MetricCard, create_metric_cards_row +from .sidebar import create_sidebar +from .time_slider import create_time_slider, create_year_selector + +__all__ = [ + "create_map_controls", + "create_metric_selector", + "create_sidebar", + "create_time_slider", + "create_year_selector", + "MetricCard", + "create_metric_cards_row", +] diff --git a/portfolio_app/components/map_controls.py b/portfolio_app/components/map_controls.py new file mode 100644 index 0000000..5fa6784 --- /dev/null +++ b/portfolio_app/components/map_controls.py @@ -0,0 +1,79 @@ +"""Map control components for choropleth visualizations.""" + +from typing import Any + +import dash_mantine_components as dmc +from dash import html + + +def create_metric_selector( + id_prefix: str, + options: list[dict[str, str]], + default_value: str | None = None, + label: str = "Select Metric", +) -> dmc.Select: + """Create a metric selector dropdown. + + Args: + id_prefix: Prefix for component IDs. + options: List of options with 'label' and 'value' keys. + default_value: Initial selected value. + label: Label text for the selector. + + Returns: + Mantine Select component. + """ + return dmc.Select( + id=f"{id_prefix}-metric-selector", + label=label, + data=options, + value=default_value or (options[0]["value"] if options else None), + style={"width": "200px"}, + ) + + +def create_map_controls( + id_prefix: str, + metric_options: list[dict[str, str]], + default_metric: str | None = None, + show_layer_toggle: bool = True, +) -> dmc.Paper: + """Create a control panel for map visualizations. + + Args: + id_prefix: Prefix for component IDs. + metric_options: Options for metric selector. + default_metric: Default selected metric. + show_layer_toggle: Whether to show layer visibility toggle. + + Returns: + Mantine Paper component containing controls. + """ + controls: list[Any] = [ + create_metric_selector( + id_prefix=id_prefix, + options=metric_options, + default_value=default_metric, + label="Display Metric", + ), + ] + + if show_layer_toggle: + controls.append( + dmc.Switch( + id=f"{id_prefix}-layer-toggle", + label="Show Boundaries", + checked=True, + style={"marginTop": "10px"}, + ) + ) + + return dmc.Paper( + children=[ + dmc.Text("Map Controls", fw=500, size="sm", mb="xs"), + html.Div(controls), + ], + p="md", + radius="sm", + withBorder=True, + ) diff --git a/portfolio_app/components/metric_card.py b/portfolio_app/components/metric_card.py new file mode 100644 index 0000000..42b3d24 --- /dev/null +++ b/portfolio_app/components/metric_card.py @@ -0,0 +1,115 @@ +"""Metric card components for KPI display.""" + +from typing import Any + +import dash_mantine_components as dmc +from dash import dcc + +from portfolio_app.figures.summary_cards import create_metric_card_figure + + +class MetricCard: + """A reusable metric card component.""" + + def __init__( + self, + id_prefix: str, + title: str, + value: float | int | str = 0, + delta: float | None = None, + prefix: str = "", + suffix: str = "", + format_spec: str = ",.0f", + positive_is_good: bool = True, + ): + """Initialize a metric card. + + Args: + id_prefix: Prefix for component IDs. + title: Card title. + value: Main metric value. + delta: Change value for delta indicator. + prefix: Value prefix (e.g., '$'). + suffix: Value suffix. + format_spec: Python format specification. + positive_is_good: Whether positive delta is good. + """ + self.id_prefix = id_prefix + self.title = title + self.value = value + self.delta = delta + self.prefix = prefix + self.suffix = suffix + self.format_spec = format_spec + self.positive_is_good = positive_is_good + + def render(self) -> dmc.Paper: + """Render the metric card component. + + Returns: + Mantine Paper component with embedded graph. + """ + fig = create_metric_card_figure( + value=self.value, + title=self.title, + delta=self.delta, + prefix=self.prefix, + suffix=self.suffix, + format_spec=self.format_spec, + positive_is_good=self.positive_is_good, + ) + + return dmc.Paper( + children=[ + dcc.Graph( + id=f"{self.id_prefix}-graph", + figure=fig, + config={"displayModeBar": False}, + style={"height": "120px"}, + ) + ], + p="xs", + radius="sm", + withBorder=True, + ) + + +def create_metric_cards_row( + metrics: list[dict[str, Any]], + id_prefix: str = "metric", +) -> dmc.SimpleGrid: + """Create a row of metric cards. + + Args: + metrics: List of metric configurations with keys: + - title: Card title + - value: Metric value + - delta: Optional change value + - prefix: Optional value prefix + - suffix: Optional value suffix + - format_spec: Optional format specification + - positive_is_good: Optional delta color logic + id_prefix: Prefix for component IDs. + + Returns: + Mantine SimpleGrid component with metric cards. + """ + cards = [] + for i, metric in enumerate(metrics): + card = MetricCard( + id_prefix=f"{id_prefix}-{i}", + title=metric.get("title", ""), + value=metric.get("value", 0), + delta=metric.get("delta"), + prefix=metric.get("prefix", ""), + suffix=metric.get("suffix", ""), + format_spec=metric.get("format_spec", ",.0f"), + positive_is_good=metric.get("positive_is_good", True), + ) + cards.append(card.render()) + + return dmc.SimpleGrid( + cols={"base": 1, "sm": 2, "md": len(cards)}, + spacing="md", + children=cards, + ) diff --git a/portfolio_app/components/sidebar.py b/portfolio_app/components/sidebar.py new file mode 100644 index 0000000..60494e2 --- /dev/null +++ b/portfolio_app/components/sidebar.py @@ -0,0 +1,214 @@ +"""Floating sidebar navigation component.""" + +import dash_mantine_components as dmc +from dash import dcc, html +from dash_iconify import DashIconify + +# Navigation items configuration - main pages +NAV_ITEMS_MAIN = [ + {"path": "/", "icon": "tabler:home", "label": "Home"}, + {"path": "/about", "icon": "tabler:user", "label": "About"}, + {"path": "/blog", "icon": "tabler:article", "label": "Blog"}, + {"path": "/resume", "icon": "tabler:file-text", "label": "Resume"}, + {"path": "/contact", "icon": "tabler:mail", "label": "Contact"}, +] + +# Navigation items configuration - projects/dashboards (separated) +NAV_ITEMS_PROJECTS = [ + {"path": "/projects", "icon": "tabler:folder", "label": "Projects"}, + {"path": "/toronto", "icon": "tabler:map-2", "label": "Toronto Housing"}, +] + +# External links configuration +EXTERNAL_LINKS = [ + { + "url": "https://github.com/leomiranda", + "icon": "tabler:brand-github", + "label": "GitHub", + }, + { + "url": "https://linkedin.com/in/leobmiranda", + "icon": "tabler:brand-linkedin", + "label": "LinkedIn", + }, +] + + +def create_brand_logo() -> html.Div: + """Create the brand initials logo.""" + return html.Div( + dcc.Link( + "LM", + href="/", + className="sidebar-brand-link", + ), + className="sidebar-brand", + ) + + +def create_nav_icon( + icon: str, + label: str, + path: str, + current_path: str, +) -> dmc.Tooltip: + """Create a navigation icon with tooltip. + + Args: + icon: Iconify icon string. + label: Tooltip label. + path: Navigation path. + current_path: Current page path for active state. + + Returns: + Tooltip-wrapped navigation icon. + """ + is_active = current_path == path or (path != "/" and current_path.startswith(path)) + + return dmc.Tooltip( + dcc.Link( + dmc.ActionIcon( + DashIconify(icon=icon, width=20), + variant="subtle" if not is_active else "filled", + size="lg", + radius="xl", + color="blue" if is_active else "gray", + className="nav-icon-active" if is_active else "", + ), + href=path, + ), + label=label, + position="right", + withArrow=True, + ) + + +def create_theme_toggle(current_theme: str = "dark") -> dmc.Tooltip: + """Create the theme toggle button. + + Args: + current_theme: Current theme ('dark' or 'light'). + + Returns: + Tooltip-wrapped theme toggle icon. + """ + icon = "tabler:sun" if current_theme == "dark" else "tabler:moon" + label = "Switch to light mode" if current_theme == "dark" else "Switch to dark mode" + + return dmc.Tooltip( + dmc.ActionIcon( + DashIconify(icon=icon, width=20, id="theme-toggle-icon"), + id="theme-toggle", + variant="subtle", + size="lg", + radius="xl", + color="gray", + ), + label=label, + position="right", + withArrow=True, + ) + + +def create_external_link(url: str, icon: str, label: str) -> dmc.Tooltip: + """Create an external link icon with tooltip. + + Args: + url: External URL. + icon: Iconify icon string. + label: Tooltip label. + + Returns: + Tooltip-wrapped external link icon. + """ + return dmc.Tooltip( + dmc.Anchor( + dmc.ActionIcon( + DashIconify(icon=icon, width=20), + variant="subtle", + size="lg", + radius="xl", + color="gray", + ), + href=url, + target="_blank", + ), + label=label, + position="right", + withArrow=True, + ) + + +def create_sidebar_divider() -> html.Div: + """Create a horizontal divider for the sidebar.""" + return html.Div(className="sidebar-divider") + + +def create_sidebar_content( + current_path: str = "/", current_theme: str = "dark" +) -> list[dmc.Tooltip | html.Div]: + """Create the sidebar content list. + + Args: + current_path: Current page path for active state highlighting. + current_theme: Current theme for toggle icon state. + + Returns: + List of sidebar components. + """ + return [ + # Brand logo + create_brand_logo(), + create_sidebar_divider(), + # Main navigation icons + *[ + create_nav_icon( + icon=item["icon"], + label=item["label"], + path=item["path"], + current_path=current_path, + ) + for item in NAV_ITEMS_MAIN + ], + create_sidebar_divider(), + # Dashboard/Project links + *[ + create_nav_icon( + icon=item["icon"], + label=item["label"], + path=item["path"], + current_path=current_path, + ) + for item in NAV_ITEMS_PROJECTS + ], + create_sidebar_divider(), + # Theme toggle + create_theme_toggle(current_theme), + create_sidebar_divider(), + # External links + *[ + create_external_link( + url=link["url"], + icon=link["icon"], + label=link["label"], + ) + for link in EXTERNAL_LINKS + ], + ] + + +def create_sidebar(current_path: str = "/", current_theme: str = "dark") -> html.Div: + """Create the floating sidebar navigation. + + Args: + current_path: Current page path for active state highlighting. + current_theme: Current theme for toggle icon state. + + Returns: + Complete sidebar component. + """ + return html.Div( + id="floating-sidebar", + className="floating-sidebar", + children=create_sidebar_content(current_path, current_theme), + ) diff --git a/portfolio_app/components/time_slider.py b/portfolio_app/components/time_slider.py new file mode 100644 index 0000000..12752d6 --- /dev/null +++ b/portfolio_app/components/time_slider.py @@ -0,0 +1,135 @@ +"""Time selection components for temporal data filtering.""" + +from datetime import date + +import dash_mantine_components as dmc + + +def create_year_selector( + id_prefix: str, + min_year: int = 2020, + max_year: int | None = None, + default_year: int | None = None, + label: str = "Select Year", +) -> dmc.Select: + """Create a year selector dropdown. + + Args: + id_prefix: Prefix for component IDs. + min_year: Minimum year option. + max_year: Maximum year option (defaults to current year). + default_year: Initial selected year. + label: Label text for the selector. + + Returns: + Mantine Select component. + """ + if max_year is None: + max_year = date.today().year + + if default_year is None: + default_year = max_year + + years = list(range(max_year, min_year - 1, -1)) + options = [{"label": str(year), "value": str(year)} for year in years] + + return dmc.Select( + id=f"{id_prefix}-year-selector", + label=label, + data=options, + value=str(default_year), + style={"width": "120px"}, + ) + + +def create_time_slider( + id_prefix: str, + min_year: int = 2020, + max_year: int | None = None, + default_range: tuple[int, int] | None = None, + label: str = "Time Range", +) -> dmc.Paper: + """Create a time range slider component. + + Args: + id_prefix: Prefix for component IDs. + min_year: Minimum year for the slider. + max_year: Maximum year for the slider. + default_range: Default (start, end) year range. + label: Label text for the slider. + + Returns: + Mantine Paper component containing the slider. + """ + if max_year is None: + max_year = date.today().year + + if default_range is None: + default_range = (min_year, max_year) + + # Create marks for every year + marks = [ + {"value": year, "label": str(year)} for year in range(min_year, max_year + 1) + ] + + return dmc.Paper( + children=[ + dmc.Text(label, fw=500, size="sm", mb="xs"), + dmc.RangeSlider( + id=f"{id_prefix}-time-slider", + min=min_year, + max=max_year, + value=list(default_range), + marks=marks, + step=1, + minRange=1, + style={"marginTop": "20px", "marginBottom": "10px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ) + + +def create_month_selector( + id_prefix: str, + default_month: int | None = None, + label: str = "Select Month", +) -> dmc.Select: + """Create a month selector dropdown. + + Args: + id_prefix: Prefix for component IDs. + default_month: Initial selected month (1-12). + label: Label text for the selector. + + Returns: + Mantine Select component. + """ + months = [ + "January", + "February", + "March", + "April", + "May", + "June", + "July", + "August", + "September", + "October", + "November", + "December", + ] + options = [{"label": month, "value": str(i + 1)} for i, month in enumerate(months)] + + if default_month is None: + default_month = date.today().month + + return dmc.Select( + id=f"{id_prefix}-month-selector", + label=label, + data=options, + value=str(default_month), + style={"width": "140px"}, + ) diff --git a/portfolio_app/config.py b/portfolio_app/config.py new file mode 100644 index 0000000..ebe09f5 --- /dev/null +++ b/portfolio_app/config.py @@ -0,0 +1,34 @@ +"""Application configuration using Pydantic BaseSettings.""" + +from functools import lru_cache + +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): # type: ignore[misc] + """Application settings loaded from environment variables.""" + + model_config = SettingsConfigDict( + env_file=".env", + env_file_encoding="utf-8", + extra="ignore", + ) + + # Database + database_url: str = "postgresql://portfolio:portfolio_dev@localhost:5432/portfolio" + postgres_user: str = "portfolio" + postgres_password: str = "portfolio_dev" + postgres_db: str = "portfolio" + + # Application + dash_debug: bool = True + secret_key: str = "change-me-in-production" + + # Logging + log_level: str = "INFO" + + +@lru_cache +def get_settings() -> Settings: + """Get cached settings instance.""" + return Settings() diff --git a/portfolio_app/content/blog/building-data-platform-team-of-one.md b/portfolio_app/content/blog/building-data-platform-team-of-one.md new file mode 100644 index 0000000..1ba48e6 --- /dev/null +++ b/portfolio_app/content/blog/building-data-platform-team-of-one.md @@ -0,0 +1,111 @@ +--- +title: "Building a Data Platform as a Team of One" +date: "2025-01-15" +description: "What I learned from 5 years as the sole data professional at a mid-size company" +tags: + - data-engineering + - career + - lessons-learned +status: published +--- + +When I joined Summitt Energy in 2019, there was no data infrastructure. No warehouse. No pipelines. No documentation. Just a collection of spreadsheets and a Genesys Cloud instance spitting out CSVs. + +Five years later, I'd built DataFlow: an enterprise platform processing 1B+ rows across 21 tables, feeding dashboards that executives actually opened. Here's what I learned doing it alone. + +## The Reality of "Full Stack Data" + +When you're the only data person, "full stack" isn't a buzzword—it's survival. In a single week, I might: + +- Debug a Python ETL script at 7am because overnight loads failed +- Present quarterly metrics to leadership at 10am +- Design a new dimensional model over lunch +- Write SQL transformations in the afternoon +- Handle ad-hoc "can you pull this data?" requests between meetings + +There's no handoff. No "that's not my job." Everything is your job. + +## Prioritization Frameworks + +The hardest part isn't the technical work—it's deciding what to build first when everything feels urgent. + +### The 80/20 Rule, Applied Ruthlessly + +I asked myself: **What 20% of the data drives 80% of decisions?** + +For a contact center, that turned out to be: +- Call volume by interval +- Abandon rate +- Average handle time +- Service level + +Everything else was nice-to-have. I built those four metrics first, got them bulletproof, then expanded. + +### The "Who's Screaming?" Test + +When multiple stakeholders want different things: +1. Who has executive backing? +2. What's blocking revenue? +3. What's causing visible pain? + +If nobody's screaming, it can probably wait. + +## Technical Debt vs. Shipping + +I rewrote DataFlow three times: + +- **v1 (2020)**: Hacky Python scripts. Worked, barely. +- **v2 (2021)**: Proper dimensional model. Still messy code. +- **v3 (2022)**: SQLAlchemy ORM, proper error handling, logging. +- **v4 (2023)**: dbt-style transformations, FastAPI layer. + +Was v1 embarrassing? Yes. Did it work? Also yes. + +**The lesson**: Ship something that works, then iterate. Perfect is the enemy of done, especially when you're alone. + +## Building Stakeholder Trust + +The technical work is maybe 40% of the job. The rest is politics. + +### Quick Wins First + +Before asking for resources or patience, I delivered: +- Automated a weekly report that took someone 4 hours +- Fixed a dashboard that had been wrong for months +- Built a simple tool that answered a frequent question + +Trust is earned in small deposits. + +### Speak Their Language + +Executives don't care about your star schema. They care about: +- "This will save 10 hours/week" +- "This will catch errors before they hit customers" +- "This will let you see X in real-time" + +Translate technical work into business outcomes. + +## What I'd Do Differently + +1. **Document earlier**. I waited too long. When I finally wrote things down, onboarding became possible. + +2. **Say no more**. Every "yes" to an ad-hoc request is a "no" to infrastructure work. Guard your time. + +3. **Build monitoring first**. I spent too many mornings discovering failures manually. Alerting should be table stakes. + +4. **Version control everything**. Even SQL. Even documentation. If it's not in Git, it doesn't exist. + +## The Upside + +Being a team of one forced me to learn things I'd have specialized away from on a bigger team: +- Data modeling +- Pipeline architecture +- Dashboard design +- Stakeholder management +- System administration + +It's brutal, but it makes you dangerous. You understand the whole stack. + +--- + +*This is part of a series on building data infrastructure at small companies. More posts coming on dimensional modeling, dbt patterns, and surviving legacy systems.* diff --git a/portfolio_app/errors/__init__.py b/portfolio_app/errors/__init__.py new file mode 100644 index 0000000..0de13c8 --- /dev/null +++ b/portfolio_app/errors/__init__.py @@ -0,0 +1,5 @@ +"""Error handling for the portfolio application.""" + +from .exceptions import LoadError, ParseError, PortfolioError, ValidationError + +__all__ = ["PortfolioError", "ParseError", "ValidationError", "LoadError"] diff --git a/portfolio_app/errors/exceptions.py b/portfolio_app/errors/exceptions.py new file mode 100644 index 0000000..ffae689 --- /dev/null +++ b/portfolio_app/errors/exceptions.py @@ -0,0 +1,17 @@ +"""Custom exceptions for the portfolio application.""" + + +class PortfolioError(Exception): + """Base exception for all portfolio errors.""" + + +class ParseError(PortfolioError): + """PDF/CSV parsing failed.""" + + +class ValidationError(PortfolioError): + """Pydantic or business rule validation failed.""" + + +class LoadError(PortfolioError): + """Database load operation failed.""" diff --git a/portfolio_app/figures/.gitkeep b/portfolio_app/figures/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/portfolio_app/figures/__init__.py b/portfolio_app/figures/__init__.py new file mode 100644 index 0000000..fe22939 --- /dev/null +++ b/portfolio_app/figures/__init__.py @@ -0,0 +1,61 @@ +"""Plotly figure factories for data visualization.""" + +from .bar_charts import ( + create_horizontal_bar, + create_ranking_bar, + create_stacked_bar, +) +from .choropleth import ( + create_choropleth_figure, + create_zone_map, +) +from .demographics import ( + create_age_pyramid, + create_donut_chart, + create_income_distribution, +) +from .radar import ( + create_comparison_radar, + create_radar_figure, +) +from .scatter import ( + create_bubble_chart, + create_scatter_figure, +) +from .summary_cards import create_metric_card_figure, create_summary_metrics +from .time_series import ( + add_policy_markers, + create_market_comparison_chart, + create_price_time_series, + create_time_series_with_events, + create_volume_time_series, +) + +__all__ = [ + # Choropleth + "create_choropleth_figure", + "create_zone_map", + # Time series + "create_price_time_series", + "create_volume_time_series", + "create_market_comparison_chart", + "create_time_series_with_events", + "add_policy_markers", + # Summary + "create_metric_card_figure", + "create_summary_metrics", + # Bar charts + "create_ranking_bar", + "create_stacked_bar", + "create_horizontal_bar", + # Scatter plots + "create_scatter_figure", + "create_bubble_chart", + # Radar charts + "create_radar_figure", + "create_comparison_radar", + # Demographics + "create_age_pyramid", + "create_donut_chart", + "create_income_distribution", +] diff --git a/portfolio_app/figures/bar_charts.py b/portfolio_app/figures/bar_charts.py new file mode 100644 index 0000000..692ab45 --- /dev/null +++ b/portfolio_app/figures/bar_charts.py @@ -0,0 +1,238 @@ +"""Bar chart figure factories for dashboard visualizations.""" + +from typing import Any + +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + + +def create_ranking_bar( + data: list[dict[str, Any]], + name_column: str, + value_column: str, + title: str | None = None, + top_n: int = 10, + bottom_n: int = 10, + color_top: str = "#4CAF50", + color_bottom: str = "#F44336", + value_format: str = ",.0f", +) -> go.Figure: + """Create horizontal bar chart showing top and bottom rankings. + + Args: + data: List of data records. + name_column: Column name for labels. + value_column: Column name for values. + title: Optional chart title. + top_n: Number of top items to show. + bottom_n: Number of bottom items to show. + color_top: Color for top performers. + color_bottom: Color for bottom performers. + value_format: Number format string for values. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Rankings") + + df = pd.DataFrame(data).sort_values(value_column, ascending=False) + + # Get top and bottom + top_df = df.head(top_n).copy() + bottom_df = df.tail(bottom_n).copy() + + top_df["group"] = "Top" + bottom_df["group"] = "Bottom" + + # Combine with gap in the middle + combined = pd.concat([top_df, bottom_df]) + combined["color"] = combined["group"].map( + {"Top": color_top, "Bottom": color_bottom} + ) + + fig = go.Figure() + + # Add top bars + fig.add_trace( + go.Bar( + y=top_df[name_column], + x=top_df[value_column], + orientation="h", + marker_color=color_top, + name="Top", + text=top_df[value_column].apply(lambda x: f"{x:{value_format}}"), + textposition="auto", + hovertemplate=f"%{{y}}
{value_column}: %{{x:{value_format}}}", + ) + ) + + # Add bottom bars + fig.add_trace( + go.Bar( + y=bottom_df[name_column], + x=bottom_df[value_column], + orientation="h", + marker_color=color_bottom, + name="Bottom", + text=bottom_df[value_column].apply(lambda x: f"{x:{value_format}}"), + textposition="auto", + hovertemplate=f"%{{y}}
{value_column}: %{{x:{value_format}}}", + ) + ) + + fig.update_layout( + title=title, + barmode="group", + showlegend=True, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02}, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None}, + yaxis={"autorange": "reversed", "title": None}, + margin={"l": 10, "r": 10, "t": 40, "b": 10}, + ) + + return fig + + +def create_stacked_bar( + data: list[dict[str, Any]], + x_column: str, + value_column: str, + category_column: str, + title: str | None = None, + color_map: dict[str, str] | None = None, + show_percentages: bool = False, +) -> go.Figure: + """Create stacked bar chart for breakdown visualizations. + + Args: + data: List of data records. + x_column: Column name for x-axis categories. + value_column: Column name for values. + category_column: Column name for stacking categories. + title: Optional chart title. + color_map: Mapping of category to color. + show_percentages: Whether to normalize to 100%. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Breakdown") + + df = pd.DataFrame(data) + + # Default color scheme + if color_map is None: + categories = df[category_column].unique() + colors = px.colors.qualitative.Set2[: len(categories)] + color_map = dict(zip(categories, colors, strict=False)) + + fig = px.bar( + df, + x=x_column, + y=value_column, + color=category_column, + color_discrete_map=color_map, + barmode="stack", + text=value_column if not show_percentages else None, + ) + + if show_percentages: + fig.update_traces(texttemplate="%{y:.1f}%", textposition="inside") + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None}, + yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None}, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02}, + margin={"l": 10, "r": 10, "t": 60, "b": 10}, + ) + + return fig + + +def create_horizontal_bar( + data: list[dict[str, Any]], + name_column: str, + value_column: str, + title: str | None = None, + color: str = "#2196F3", + value_format: str = ",.0f", + sort: bool = True, +) -> go.Figure: + """Create simple horizontal bar chart. + + Args: + data: List of data records. + name_column: Column name for labels. + value_column: Column name for values. + title: Optional chart title. + color: Bar color. + value_format: Number format string. + sort: Whether to sort by value descending. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Bar Chart") + + df = pd.DataFrame(data) + + if sort: + df = df.sort_values(value_column, ascending=True) + + fig = go.Figure( + go.Bar( + y=df[name_column], + x=df[value_column], + orientation="h", + marker_color=color, + text=df[value_column].apply(lambda x: f"{x:{value_format}}"), + textposition="outside", + hovertemplate=f"%{{y}}
Value: %{{x:{value_format}}}", + ) + ) + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": None}, + yaxis={"title": None}, + margin={"l": 10, "r": 10, "t": 40, "b": 10}, + ) + + return fig + + +def _create_empty_figure(title: str) -> go.Figure: + """Create an empty figure with a message.""" + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"visible": False}, + yaxis={"visible": False}, + ) + return fig diff --git a/portfolio_app/figures/choropleth.py b/portfolio_app/figures/choropleth.py new file mode 100644 index 0000000..a8c8d55 --- /dev/null +++ b/portfolio_app/figures/choropleth.py @@ -0,0 +1,143 @@ +"""Choropleth map figure factory for Toronto housing data.""" + +from typing import Any + +import plotly.express as px +import plotly.graph_objects as go + + +def create_choropleth_figure( + geojson: dict[str, Any] | None, + data: list[dict[str, Any]], + location_key: str, + color_column: str, + hover_data: list[str] | None = None, + color_scale: str = "Blues", + title: str | None = None, + map_style: str = "carto-positron", + center: dict[str, float] | None = None, + zoom: float = 9.5, +) -> go.Figure: + """Create a choropleth map figure. + + Args: + geojson: GeoJSON FeatureCollection for boundaries. + data: List of data records with location keys and values. + location_key: Column name for location identifier. + color_column: Column name for color values. + hover_data: Additional columns to show on hover. + color_scale: Plotly color scale name. + title: Optional chart title. + map_style: Mapbox style (carto-positron, open-street-map, etc.). + center: Map center coordinates {"lat": float, "lon": float}. + zoom: Initial zoom level. + + Returns: + Plotly Figure object. + """ + # Default center to Toronto + if center is None: + center = {"lat": 43.7, "lon": -79.4} + + # Use dark-mode friendly map style by default + if map_style == "carto-positron": + map_style = "carto-darkmatter" + + # If no geojson provided, create a placeholder map + if geojson is None or not data: + fig = go.Figure(go.Scattermapbox()) + fig.update_layout( + mapbox={ + "style": map_style, + "center": center, + "zoom": zoom, + }, + margin={"l": 0, "r": 0, "t": 40, "b": 0}, + title=title or "Toronto Housing Map", + height=500, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + fig.add_annotation( + text="No geometry data available. Complete QGIS digitization to enable map.", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + return fig + + # Create choropleth with data + import pandas as pd + + df = pd.DataFrame(data) + + # Use dark-mode friendly map style + effective_map_style = ( + "carto-darkmatter" if map_style == "carto-positron" else map_style + ) + + fig = px.choropleth_mapbox( + df, + geojson=geojson, + locations=location_key, + featureidkey=f"properties.{location_key}", + color=color_column, + color_continuous_scale=color_scale, + hover_data=hover_data, + mapbox_style=effective_map_style, + center=center, + zoom=zoom, + opacity=0.7, + ) + + fig.update_layout( + margin={"l": 0, "r": 0, "t": 40, "b": 0}, + title=title, + height=500, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + coloraxis_colorbar={ + "title": { + "text": color_column.replace("_", " ").title(), + "font": {"color": "#c9c9c9"}, + }, + "thickness": 15, + "len": 0.7, + "tickfont": {"color": "#c9c9c9"}, + }, + ) + + return fig + + +def create_zone_map( + zones_geojson: dict[str, Any] | None, + rental_data: list[dict[str, Any]], + metric: str = "avg_rent", +) -> go.Figure: + """Create choropleth map for CMHC zones. + + Args: + zones_geojson: GeoJSON for CMHC zone boundaries. + rental_data: Rental statistics by zone. + metric: Metric to display (avg_rent, vacancy_rate, etc.). + + Returns: + Plotly Figure object. + """ + hover_columns = ["zone_name", "avg_rent", "vacancy_rate", "rental_universe"] + + return create_choropleth_figure( + geojson=zones_geojson, + data=rental_data, + location_key="zone_code", + color_column=metric, + hover_data=[c for c in hover_columns if c != metric], + color_scale="Oranges" if "rent" in metric else "Purples", + title="Toronto Rental Market by Zone", + ) diff --git a/portfolio_app/figures/demographics.py b/portfolio_app/figures/demographics.py new file mode 100644 index 0000000..ceced5b --- /dev/null +++ b/portfolio_app/figures/demographics.py @@ -0,0 +1,240 @@ +"""Demographics-specific chart factories.""" + +from typing import Any + +import pandas as pd +import plotly.graph_objects as go + + +def create_age_pyramid( + data: list[dict[str, Any]], + age_groups: list[str], + male_column: str = "male", + female_column: str = "female", + title: str | None = None, +) -> go.Figure: + """Create population pyramid by age and gender. + + Args: + data: List with one record per age group containing male/female counts. + age_groups: List of age group labels in order (youngest to oldest). + male_column: Column name for male population. + female_column: Column name for female population. + title: Optional chart title. + + Returns: + Plotly Figure object. + """ + if not data or not age_groups: + return _create_empty_figure(title or "Age Distribution") + + df = pd.DataFrame(data) + + # Ensure data is ordered by age groups + if "age_group" in df.columns: + df["age_order"] = df["age_group"].apply( + lambda x: age_groups.index(x) if x in age_groups else -1 + ) + df = df.sort_values("age_order") + + male_values = df[male_column].tolist() if male_column in df.columns else [] + female_values = df[female_column].tolist() if female_column in df.columns else [] + + # Make male values negative for pyramid effect + male_values_neg = [-v for v in male_values] + + fig = go.Figure() + + # Male bars (left side, negative values) + fig.add_trace( + go.Bar( + y=age_groups, + x=male_values_neg, + orientation="h", + name="Male", + marker_color="#2196F3", + hovertemplate="%{y}
Male: %{customdata:,}", + customdata=male_values, + ) + ) + + # Female bars (right side, positive values) + fig.add_trace( + go.Bar( + y=age_groups, + x=female_values, + orientation="h", + name="Female", + marker_color="#E91E63", + hovertemplate="%{y}
Female: %{x:,}", + ) + ) + + # Calculate max for symmetric axis + max_val = max(max(male_values, default=0), max(female_values, default=0)) + + fig.update_layout( + title=title, + barmode="overlay", + bargap=0.1, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={ + "title": "Population", + "gridcolor": "rgba(128,128,128,0.2)", + "range": [-max_val * 1.1, max_val * 1.1], + "tickvals": [-max_val, -max_val / 2, 0, max_val / 2, max_val], + "ticktext": [ + f"{max_val:,.0f}", + f"{max_val / 2:,.0f}", + "0", + f"{max_val / 2:,.0f}", + f"{max_val:,.0f}", + ], + }, + yaxis={"title": None, "gridcolor": "rgba(128,128,128,0.2)"}, + legend={"orientation": "h", "yanchor": "bottom", "y": 1.02}, + margin={"l": 10, "r": 10, "t": 60, "b": 10}, + ) + + return fig + + +def create_donut_chart( + data: list[dict[str, Any]], + name_column: str, + value_column: str, + title: str | None = None, + colors: list[str] | None = None, + hole_size: float = 0.4, +) -> go.Figure: + """Create donut chart for percentage breakdowns. + + Args: + data: List of data records with name and value. + name_column: Column name for labels. + value_column: Column name for values. + title: Optional chart title. + colors: List of colors for segments. + hole_size: Size of center hole (0-1). + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Distribution") + + df = pd.DataFrame(data) + + if colors is None: + colors = [ + "#2196F3", + "#4CAF50", + "#FF9800", + "#E91E63", + "#9C27B0", + "#00BCD4", + "#FFC107", + "#795548", + ] + + fig = go.Figure( + go.Pie( + labels=df[name_column], + values=df[value_column], + hole=hole_size, + marker_colors=colors[: len(df)], + textinfo="percent+label", + textposition="outside", + hovertemplate="%{label}
%{value:,} (%{percent})", + ) + ) + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + showlegend=False, + margin={"l": 10, "r": 10, "t": 60, "b": 10}, + ) + + return fig + + +def create_income_distribution( + data: list[dict[str, Any]], + bracket_column: str, + count_column: str, + title: str | None = None, + color: str = "#4CAF50", +) -> go.Figure: + """Create histogram-style bar chart for income distribution. + + Args: + data: List of data records with income brackets and counts. + bracket_column: Column name for income brackets. + count_column: Column name for household counts. + title: Optional chart title. + color: Bar color. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Income Distribution") + + df = pd.DataFrame(data) + + fig = go.Figure( + go.Bar( + x=df[bracket_column], + y=df[count_column], + marker_color=color, + text=df[count_column].apply(lambda x: f"{x:,}"), + textposition="outside", + hovertemplate="%{x}
Households: %{y:,}", + ) + ) + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={ + "title": "Income Bracket", + "gridcolor": "rgba(128,128,128,0.2)", + "tickangle": -45, + }, + yaxis={ + "title": "Households", + "gridcolor": "rgba(128,128,128,0.2)", + }, + margin={"l": 10, "r": 10, "t": 60, "b": 80}, + ) + + return fig + + +def _create_empty_figure(title: str) -> go.Figure: + """Create an empty figure with a message.""" + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"visible": False}, + yaxis={"visible": False}, + ) + return fig diff --git a/portfolio_app/figures/radar.py b/portfolio_app/figures/radar.py new file mode 100644 index 0000000..35f8e75 --- /dev/null +++ b/portfolio_app/figures/radar.py @@ -0,0 +1,166 @@ +"""Radar/spider chart figure factory for multi-metric comparison.""" + +from typing import Any + +import plotly.graph_objects as go + + +def create_radar_figure( + data: list[dict[str, Any]], + metrics: list[str], + name_column: str | None = None, + title: str | None = None, + fill: bool = True, + colors: list[str] | None = None, +) -> go.Figure: + """Create radar/spider chart for multi-axis comparison. + + Each record in data represents one entity (e.g., a neighbourhood) + with values for each metric that will be plotted on a separate axis. + + Args: + data: List of data records, each with values for the metrics. + metrics: List of metric column names to display on radar axes. + name_column: Column name for entity labels. + title: Optional chart title. + fill: Whether to fill the radar polygons. + colors: List of colors for each data series. + + Returns: + Plotly Figure object. + """ + if not data or not metrics: + return _create_empty_figure(title or "Radar Chart") + + # Default colors + if colors is None: + colors = [ + "#2196F3", + "#4CAF50", + "#FF9800", + "#E91E63", + "#9C27B0", + "#00BCD4", + ] + + fig = go.Figure() + + # Format axis labels + axis_labels = [m.replace("_", " ").title() for m in metrics] + + for i, record in enumerate(data): + values = [record.get(m, 0) or 0 for m in metrics] + # Close the radar polygon + values_closed = values + [values[0]] + labels_closed = axis_labels + [axis_labels[0]] + + name = ( + record.get(name_column, f"Series {i + 1}") + if name_column + else f"Series {i + 1}" + ) + color = colors[i % len(colors)] + + fig.add_trace( + go.Scatterpolar( + r=values_closed, + theta=labels_closed, + name=name, + line={"color": color, "width": 2}, + fill="toself" if fill else None, + fillcolor=f"rgba{_hex_to_rgba(color, 0.2)}" if fill else None, + hovertemplate="%{theta}: %{r:.1f}", + ) + ) + + fig.update_layout( + title=title, + polar={ + "radialaxis": { + "visible": True, + "gridcolor": "rgba(128,128,128,0.3)", + "linecolor": "rgba(128,128,128,0.3)", + "tickfont": {"color": "#c9c9c9"}, + }, + "angularaxis": { + "gridcolor": "rgba(128,128,128,0.3)", + "linecolor": "rgba(128,128,128,0.3)", + "tickfont": {"color": "#c9c9c9"}, + }, + "bgcolor": "rgba(0,0,0,0)", + }, + paper_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + showlegend=len(data) > 1, + legend={"orientation": "h", "yanchor": "bottom", "y": -0.2}, + margin={"l": 40, "r": 40, "t": 60, "b": 40}, + ) + + return fig + + +def create_comparison_radar( + selected_data: dict[str, Any], + average_data: dict[str, Any], + metrics: list[str], + selected_name: str = "Selected", + average_name: str = "City Average", + title: str | None = None, +) -> go.Figure: + """Create radar chart comparing a selection to city average. + + Args: + selected_data: Data for the selected entity. + average_data: Data for the city average. + metrics: List of metric column names. + selected_name: Label for selected entity. + average_name: Label for average. + title: Optional chart title. + + Returns: + Plotly Figure object. + """ + if not selected_data or not average_data: + return _create_empty_figure(title or "Comparison") + + data = [ + {**selected_data, "__name__": selected_name}, + {**average_data, "__name__": average_name}, + ] + + return create_radar_figure( + data=data, + metrics=metrics, + name_column="__name__", + title=title, + colors=["#4CAF50", "#9E9E9E"], + ) + + +def _hex_to_rgba(hex_color: str, alpha: float) -> tuple[int, int, int, float]: + """Convert hex color to RGBA tuple.""" + hex_color = hex_color.lstrip("#") + r = int(hex_color[0:2], 16) + g = int(hex_color[2:4], 16) + b = int(hex_color[4:6], 16) + return (r, g, b, alpha) + + +def _create_empty_figure(title: str) -> go.Figure: + """Create an empty figure with a message.""" + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + return fig diff --git a/portfolio_app/figures/scatter.py b/portfolio_app/figures/scatter.py new file mode 100644 index 0000000..1e1c6ef --- /dev/null +++ b/portfolio_app/figures/scatter.py @@ -0,0 +1,184 @@ +"""Scatter plot figure factory for correlation views.""" + +from typing import Any + +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + + +def create_scatter_figure( + data: list[dict[str, Any]], + x_column: str, + y_column: str, + name_column: str | None = None, + size_column: str | None = None, + color_column: str | None = None, + title: str | None = None, + x_title: str | None = None, + y_title: str | None = None, + trendline: bool = False, + color_scale: str = "Blues", +) -> go.Figure: + """Create scatter plot for correlation visualization. + + Args: + data: List of data records. + x_column: Column name for x-axis values. + y_column: Column name for y-axis values. + name_column: Column name for point labels (hover). + size_column: Column name for point sizes. + color_column: Column name for color encoding. + title: Optional chart title. + x_title: X-axis title. + y_title: Y-axis title. + trendline: Whether to add OLS trendline. + color_scale: Plotly color scale for continuous colors. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Scatter Plot") + + df = pd.DataFrame(data) + + # Build hover_data + hover_data = {} + if name_column and name_column in df.columns: + hover_data[name_column] = True + + # Create scatter plot + fig = px.scatter( + df, + x=x_column, + y=y_column, + size=size_column if size_column and size_column in df.columns else None, + color=color_column if color_column and color_column in df.columns else None, + color_continuous_scale=color_scale, + hover_name=name_column, + trendline="ols" if trendline else None, + opacity=0.7, + ) + + # Style the markers + fig.update_traces( + marker={ + "line": {"width": 1, "color": "rgba(255,255,255,0.3)"}, + }, + ) + + # Trendline styling + if trendline: + fig.update_traces( + selector={"mode": "lines"}, + line={"color": "#FF9800", "dash": "dash", "width": 2}, + ) + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={ + "gridcolor": "rgba(128,128,128,0.2)", + "title": x_title or x_column.replace("_", " ").title(), + "zeroline": False, + }, + yaxis={ + "gridcolor": "rgba(128,128,128,0.2)", + "title": y_title or y_column.replace("_", " ").title(), + "zeroline": False, + }, + margin={"l": 10, "r": 10, "t": 40, "b": 10}, + showlegend=color_column is not None, + ) + + return fig + + +def create_bubble_chart( + data: list[dict[str, Any]], + x_column: str, + y_column: str, + size_column: str, + name_column: str | None = None, + color_column: str | None = None, + title: str | None = None, + x_title: str | None = None, + y_title: str | None = None, + size_max: int = 50, +) -> go.Figure: + """Create bubble chart with sized markers. + + Args: + data: List of data records. + x_column: Column name for x-axis values. + y_column: Column name for y-axis values. + size_column: Column name for bubble sizes. + name_column: Column name for labels. + color_column: Column name for colors. + title: Optional chart title. + x_title: X-axis title. + y_title: Y-axis title. + size_max: Maximum marker size in pixels. + + Returns: + Plotly Figure object. + """ + if not data: + return _create_empty_figure(title or "Bubble Chart") + + df = pd.DataFrame(data) + + fig = px.scatter( + df, + x=x_column, + y=y_column, + size=size_column, + color=color_column, + hover_name=name_column, + size_max=size_max, + opacity=0.7, + ) + + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={ + "gridcolor": "rgba(128,128,128,0.2)", + "title": x_title or x_column.replace("_", " ").title(), + }, + yaxis={ + "gridcolor": "rgba(128,128,128,0.2)", + "title": y_title or y_column.replace("_", " ").title(), + }, + margin={"l": 10, "r": 10, "t": 40, "b": 10}, + ) + + return fig + + +def _create_empty_figure(title: str) -> go.Figure: + """Create an empty figure with a message.""" + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + fig.update_layout( + title=title, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"visible": False}, + yaxis={"visible": False}, + ) + return fig diff --git a/portfolio_app/figures/summary_cards.py b/portfolio_app/figures/summary_cards.py new file mode 100644 index 0000000..fd4cbc3 --- /dev/null +++ b/portfolio_app/figures/summary_cards.py @@ -0,0 +1,107 @@ +"""Summary card figure factories for KPI display.""" + +from typing import Any + +import plotly.graph_objects as go + + +def create_metric_card_figure( + value: float | int | str, + title: str, + delta: float | None = None, + delta_suffix: str = "%", + prefix: str = "", + suffix: str = "", + format_spec: str = ",.0f", + positive_is_good: bool = True, +) -> go.Figure: + """Create a KPI indicator figure. + + Args: + value: The main metric value. + title: Card title. + delta: Optional change value (for delta indicator). + delta_suffix: Suffix for delta value (e.g., '%'). + prefix: Prefix for main value (e.g., '$'). + suffix: Suffix for main value. + format_spec: Python format specification for the value. + positive_is_good: Whether positive delta is good (green) or bad (red). + + Returns: + Plotly Figure object. + """ + # Determine numeric value for indicator + if isinstance(value, int | float): + number_value: float | None = float(value) + else: + number_value = None + + fig = go.Figure() + + # Add indicator trace + indicator_config: dict[str, Any] = { + "mode": "number", + "value": number_value if number_value is not None else 0, + "title": {"text": title, "font": {"size": 14}}, + "number": { + "font": {"size": 32}, + "prefix": prefix, + "suffix": suffix, + "valueformat": format_spec, + }, + } + + # Add delta if provided + if delta is not None: + indicator_config["mode"] = "number+delta" + indicator_config["delta"] = { + "reference": number_value - delta if number_value else 0, + "relative": False, + "valueformat": ".1f", + "suffix": delta_suffix, + "increasing": {"color": "green" if positive_is_good else "red"}, + "decreasing": {"color": "red" if positive_is_good else "green"}, + } + + fig.add_trace(go.Indicator(**indicator_config)) + + fig.update_layout( + height=120, + margin={"l": 20, "r": 20, "t": 40, "b": 20}, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font={"family": "Inter, sans-serif", "color": "#c9c9c9"}, + ) + + return fig + + +def create_summary_metrics( + metrics: dict[str, dict[str, Any]], +) -> list[go.Figure]: + """Create multiple metric card figures. + + Args: + metrics: Dictionary of metric configurations. + Key: metric name + Value: dict with 'value', 'title', 'delta' (optional), etc. + + Returns: + List of Plotly Figure objects. + """ + figures = [] + + for metric_config in metrics.values(): + fig = create_metric_card_figure( + value=metric_config.get("value", 0), + title=metric_config.get("title", ""), + delta=metric_config.get("delta"), + delta_suffix=metric_config.get("delta_suffix", "%"), + prefix=metric_config.get("prefix", ""), + suffix=metric_config.get("suffix", ""), + format_spec=metric_config.get("format_spec", ",.0f"), + positive_is_good=metric_config.get("positive_is_good", True), + ) + figures.append(fig) + + return figures diff --git a/portfolio_app/figures/time_series.py b/portfolio_app/figures/time_series.py new file mode 100644 index 0000000..fc46cfc --- /dev/null +++ b/portfolio_app/figures/time_series.py @@ -0,0 +1,386 @@ +"""Time series figure factories for Toronto housing data.""" + +from typing import Any + +import plotly.express as px +import plotly.graph_objects as go + + +def create_price_time_series( + data: list[dict[str, Any]], + date_column: str = "full_date", + price_column: str = "avg_price", + group_column: str | None = None, + title: str = "Average Price Over Time", + show_yoy: bool = True, +) -> go.Figure: + """Create a time series chart for price data. + + Args: + data: List of records with date and price columns. + date_column: Column name for dates. + price_column: Column name for price values. + group_column: Optional column for grouping (e.g., district_code). + title: Chart title. + show_yoy: Whether to show year-over-year change annotations. + + Returns: + Plotly Figure object. + """ + import pandas as pd + + if not data: + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"color": "#888888"}, + ) + fig.update_layout( + title=title, + height=350, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + return fig + + df = pd.DataFrame(data) + df[date_column] = pd.to_datetime(df[date_column]) + + if group_column and group_column in df.columns: + fig = px.line( + df, + x=date_column, + y=price_column, + color=group_column, + title=title, + ) + else: + fig = px.line( + df, + x=date_column, + y=price_column, + title=title, + ) + + fig.update_layout( + height=350, + margin={"l": 40, "r": 20, "t": 50, "b": 40}, + xaxis_title="Date", + yaxis_title=price_column.replace("_", " ").title(), + yaxis_tickprefix="$", + yaxis_tickformat=",", + hovermode="x unified", + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + yaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + ) + + return fig + + +def create_volume_time_series( + data: list[dict[str, Any]], + date_column: str = "full_date", + volume_column: str = "sales_count", + group_column: str | None = None, + title: str = "Sales Volume Over Time", + chart_type: str = "bar", +) -> go.Figure: + """Create a time series chart for volume/count data. + + Args: + data: List of records with date and volume columns. + date_column: Column name for dates. + volume_column: Column name for volume values. + group_column: Optional column for grouping. + title: Chart title. + chart_type: 'bar' or 'line'. + + Returns: + Plotly Figure object. + """ + import pandas as pd + + if not data: + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"color": "#888888"}, + ) + fig.update_layout( + title=title, + height=350, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + return fig + + df = pd.DataFrame(data) + df[date_column] = pd.to_datetime(df[date_column]) + + if chart_type == "bar": + if group_column and group_column in df.columns: + fig = px.bar( + df, + x=date_column, + y=volume_column, + color=group_column, + title=title, + ) + else: + fig = px.bar( + df, + x=date_column, + y=volume_column, + title=title, + ) + else: + if group_column and group_column in df.columns: + fig = px.line( + df, + x=date_column, + y=volume_column, + color=group_column, + title=title, + ) + else: + fig = px.line( + df, + x=date_column, + y=volume_column, + title=title, + ) + + fig.update_layout( + height=350, + margin={"l": 40, "r": 20, "t": 50, "b": 40}, + xaxis_title="Date", + yaxis_title=volume_column.replace("_", " ").title(), + yaxis_tickformat=",", + hovermode="x unified", + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + yaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + ) + + return fig + + +def create_market_comparison_chart( + data: list[dict[str, Any]], + date_column: str = "full_date", + metrics: list[str] | None = None, + title: str = "Market Indicators", +) -> go.Figure: + """Create a multi-metric comparison chart. + + Args: + data: List of records with date and metric columns. + date_column: Column name for dates. + metrics: List of metric columns to display. + title: Chart title. + + Returns: + Plotly Figure object with secondary y-axis. + """ + import pandas as pd + from plotly.subplots import make_subplots + + if not data: + fig = go.Figure() + fig.add_annotation( + text="No data available", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"color": "#888888"}, + ) + fig.update_layout( + title=title, + height=400, + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + return fig + + if metrics is None: + metrics = ["avg_price", "sales_count"] + + df = pd.DataFrame(data) + df[date_column] = pd.to_datetime(df[date_column]) + + fig = make_subplots(specs=[[{"secondary_y": True}]]) + + colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"] + + for i, metric in enumerate(metrics[:4]): + if metric not in df.columns: + continue + + secondary = i > 0 + fig.add_trace( + go.Scatter( + x=df[date_column], + y=df[metric], + name=metric.replace("_", " ").title(), + line={"color": colors[i % len(colors)]}, + ), + secondary_y=secondary, + ) + + fig.update_layout( + title=title, + height=400, + margin={"l": 40, "r": 40, "t": 50, "b": 40}, + hovermode="x unified", + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + yaxis={"gridcolor": "#333333", "linecolor": "#444444"}, + legend={ + "orientation": "h", + "yanchor": "bottom", + "y": 1.02, + "xanchor": "right", + "x": 1, + "font": {"color": "#c9c9c9"}, + }, + ) + + return fig + + +def add_policy_markers( + fig: go.Figure, + policy_events: list[dict[str, Any]], + date_column: str = "event_date", + y_position: float | None = None, +) -> go.Figure: + """Add policy event markers to an existing time series figure. + + Args: + fig: Existing Plotly figure to add markers to. + policy_events: List of policy event dicts with date and metadata. + date_column: Column name for event dates. + y_position: Y position for markers. If None, uses top of chart. + + Returns: + Updated Plotly Figure object with policy markers. + """ + if not policy_events: + return fig + + # Color mapping for policy categories + category_colors = { + "monetary": "#1f77b4", # Blue + "tax": "#2ca02c", # Green + "regulatory": "#ff7f0e", # Orange + "supply": "#9467bd", # Purple + "economic": "#d62728", # Red + } + + # Symbol mapping for expected direction + direction_symbols = { + "bullish": "triangle-up", + "bearish": "triangle-down", + "neutral": "circle", + } + + for event in policy_events: + event_date = event.get(date_column) + category = event.get("category", "economic") + direction = event.get("expected_direction", "neutral") + title = event.get("title", "Policy Event") + level = event.get("level", "federal") + + color = category_colors.get(category, "#666666") + symbol = direction_symbols.get(direction, "circle") + + # Add vertical line for the event + fig.add_vline( + x=event_date, + line_dash="dot", + line_color=color, + opacity=0.5, + annotation_text="", + ) + + # Add marker with hover info + fig.add_trace( + go.Scatter( + x=[event_date], + y=[y_position] if y_position else [None], # type: ignore[list-item] + mode="markers", + marker={ + "symbol": symbol, + "size": 12, + "color": color, + "line": {"width": 1, "color": "white"}, + }, + name=title, + hovertemplate=( + f"{title}
" + f"Date: %{{x}}
" + f"Level: {level.title()}
" + f"Category: {category.title()}
" + f"" + ), + showlegend=False, + ) + ) + + return fig + + +def create_time_series_with_events( + data: list[dict[str, Any]], + policy_events: list[dict[str, Any]], + date_column: str = "full_date", + value_column: str = "avg_price", + title: str = "Price Trend with Policy Events", +) -> go.Figure: + """Create a time series chart with policy event markers. + + Args: + data: Time series data. + policy_events: Policy events to overlay. + date_column: Column name for dates. + value_column: Column name for values. + title: Chart title. + + Returns: + Plotly Figure with time series and policy markers. + """ + # Create base time series + fig = create_price_time_series( + data=data, + date_column=date_column, + price_column=value_column, + title=title, + ) + + # Add policy markers at the top of the chart + if policy_events: + fig = add_policy_markers(fig, policy_events) + + return fig diff --git a/portfolio_app/pages/__init__.py b/portfolio_app/pages/__init__.py new file mode 100644 index 0000000..fb3e412 --- /dev/null +++ b/portfolio_app/pages/__init__.py @@ -0,0 +1 @@ +"""Dash pages.""" diff --git a/portfolio_app/pages/about.py b/portfolio_app/pages/about.py new file mode 100644 index 0000000..7bd13cf --- /dev/null +++ b/portfolio_app/pages/about.py @@ -0,0 +1,248 @@ +"""About page - Professional narrative and background.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc +from dash_iconify import DashIconify + +dash.register_page(__name__, path="/about", name="About") + +# Opening section +OPENING = """I didn't start in data. I started in project management—CAPM certified, ITIL trained, \ +the whole corporate playbook. Then I realized I liked building systems more than managing timelines, \ +and I was better at automating reports than attending meetings about them. + +That pivot led me to where I am now: 8 years deep in data engineering, analytics, and the messy \ +reality of turning raw information into something people can actually use.""" + +# What I Actually Do section +WHAT_I_DO_SHORT = "The short version: I build data infrastructure. Pipelines, warehouses, \ +dashboards, automation—the invisible machinery that makes businesses run on data instead of gut feelings." + +WHAT_I_DO_LONG = """The longer version: At Summitt Energy, I've been the sole data professional \ +supporting 150+ employees across 9 markets (Canada and US). I inherited nothing—no data warehouse, \ +no reporting infrastructure, no documentation. Over 5 years, I built DataFlow: an enterprise \ +platform processing 1B+ rows, integrating contact center data, CRM systems, and legacy tools \ +that definitely weren't designed to talk to each other. + +That meant learning to be a generalist. I've done ETL pipeline development (Python, SQLAlchemy), \ +dimensional modeling, dashboard design (Power BI, Plotly-Dash), API integration, and more \ +stakeholder management than I'd like to admit. When you're the only data person, you learn to wear every hat.""" + +# How I Think About Data +DATA_PHILOSOPHY_INTRO = "I'm not interested in data for data's sake. The question I always \ +start with: What decision does this help someone make?" + +DATA_PHILOSOPHY_DETAIL = """Most of my work has been in operations-heavy environments—contact \ +centers, energy retail, logistics. These aren't glamorous domains, but they're where data can \ +have massive impact. A 30% improvement in abandon rate isn't just a metric; it's thousands of \ +customers who didn't hang up frustrated. A 40% reduction in reporting time means managers can \ +actually manage instead of wrestling with spreadsheets.""" + +DATA_PHILOSOPHY_CLOSE = "I care about outcomes, not technology stacks." + +# Technical skills +TECH_SKILLS = { + "Languages": "Python (Pandas, SQLAlchemy, FastAPI), SQL (MSSQL, PostgreSQL), R, VBA", + "Data Engineering": "ETL/ELT pipelines, dimensional modeling (star schema), dbt patterns, batch processing, API integration, web scraping (Selenium)", + "Visualization": "Plotly/Dash, Power BI, Tableau", + "Platforms": "Genesys Cloud, Five9, Zoho, Azure DevOps", + "Currently Learning": "Cloud certification (Azure DP-203), Airflow, Snowflake", +} + +# Outside Work +OUTSIDE_WORK_INTRO = "I'm a Brazilian-Canadian based in Toronto. I speak Portuguese (native), \ +English (fluent), and enough Spanish to survive." + +OUTSIDE_WORK_ACTIVITIES = [ + "Building automation tools for small businesses through Bandit Labs (my side project)", + "Contributing to open source (MCP servers, Claude Code plugins)", + 'Trying to explain to my kid why Daddy\'s job involves "making computers talk to each other"', +] + +# What I'm Looking For +LOOKING_FOR_INTRO = "I'm currently exploring Senior Data Analyst and Data Engineer roles in \ +the Toronto area (or remote). I'm most interested in:" + +LOOKING_FOR_ITEMS = [ + "Companies that treat data as infrastructure, not an afterthought", + "Teams where I can contribute to architecture decisions, not just execute tickets", + "Operations-focused industries (energy, logistics, financial services, contact center tech)", +] + +LOOKING_FOR_CLOSE = "If that sounds like your team, let's talk." + + +def create_section_title(title: str) -> dmc.Title: + """Create a consistent section title.""" + return dmc.Title(title, order=2, size="h3", mb="sm") + + +def create_opening_section() -> dmc.Paper: + """Create the opening/intro section.""" + paragraphs = OPENING.split("\n\n") + return dmc.Paper( + dmc.Stack( + [dmc.Text(p, size="md") for p in paragraphs], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_what_i_do_section() -> dmc.Paper: + """Create the What I Actually Do section.""" + return dmc.Paper( + dmc.Stack( + [ + create_section_title("What I Actually Do"), + dmc.Text(WHAT_I_DO_SHORT, size="md", fw=500), + dmc.Text(WHAT_I_DO_LONG, size="md"), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_philosophy_section() -> dmc.Paper: + """Create the How I Think About Data section.""" + return dmc.Paper( + dmc.Stack( + [ + create_section_title("How I Think About Data"), + dmc.Text(DATA_PHILOSOPHY_INTRO, size="md", fw=500), + dmc.Text(DATA_PHILOSOPHY_DETAIL, size="md"), + dmc.Text(DATA_PHILOSOPHY_CLOSE, size="md", fw=500, fs="italic"), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_tech_section() -> dmc.Paper: + """Create the Technical Stuff section.""" + return dmc.Paper( + dmc.Stack( + [ + create_section_title("The Technical Stuff"), + dmc.Stack( + [ + dmc.Group( + [ + dmc.Text(category + ":", fw=600, size="sm", w=150), + dmc.Text(skills, size="sm", c="dimmed"), + ], + gap="sm", + align="flex-start", + wrap="nowrap", + ) + for category, skills in TECH_SKILLS.items() + ], + gap="xs", + ), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_outside_work_section() -> dmc.Paper: + """Create the Outside Work section.""" + return dmc.Paper( + dmc.Stack( + [ + create_section_title("Outside Work"), + dmc.Text(OUTSIDE_WORK_INTRO, size="md"), + dmc.Text("When I'm not staring at SQL, I'm usually:", size="md"), + dmc.List( + [ + dmc.ListItem(dmc.Text(item, size="md")) + for item in OUTSIDE_WORK_ACTIVITIES + ], + spacing="xs", + ), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_looking_for_section() -> dmc.Paper: + """Create the What I'm Looking For section.""" + return dmc.Paper( + dmc.Stack( + [ + create_section_title("What I'm Looking For"), + dmc.Text(LOOKING_FOR_INTRO, size="md"), + dmc.List( + [ + dmc.ListItem(dmc.Text(item, size="md")) + for item in LOOKING_FOR_ITEMS + ], + spacing="xs", + ), + dmc.Text(LOOKING_FOR_CLOSE, size="md", fw=500), + dmc.Group( + [ + dcc.Link( + dmc.Button( + "Download Resume", + variant="filled", + leftSection=DashIconify( + icon="tabler:download", width=18 + ), + ), + href="/resume", + ), + dcc.Link( + dmc.Button( + "Contact Me", + variant="outline", + leftSection=DashIconify(icon="tabler:mail", width=18), + ), + href="/contact", + ), + ], + gap="sm", + mt="md", + ), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +layout = dmc.Container( + dmc.Stack( + [ + dmc.Title("About", order=1, ta="center", mb="lg"), + create_opening_section(), + create_what_i_do_section(), + create_philosophy_section(), + create_tech_section(), + create_outside_work_section(), + create_looking_for_section(), + dmc.Space(h=40), + ], + gap="xl", + ), + size="md", + py="xl", +) diff --git a/portfolio_app/pages/blog/__init__.py b/portfolio_app/pages/blog/__init__.py new file mode 100644 index 0000000..9439941 --- /dev/null +++ b/portfolio_app/pages/blog/__init__.py @@ -0,0 +1 @@ +"""Blog pages package.""" diff --git a/portfolio_app/pages/blog/article.py b/portfolio_app/pages/blog/article.py new file mode 100644 index 0000000..179e504 --- /dev/null +++ b/portfolio_app/pages/blog/article.py @@ -0,0 +1,147 @@ +"""Blog article page - Dynamic routing for individual articles.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc, html +from dash_iconify import DashIconify + +from portfolio_app.utils.markdown_loader import get_article + +dash.register_page( + __name__, + path_template="/blog/", + name="Article", +) + + +def create_not_found() -> dmc.Container: + """Create 404 state for missing articles.""" + return dmc.Container( + dmc.Stack( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:file-unknown", width=48), + size=80, + radius="xl", + variant="light", + color="red", + ), + dmc.Title("Article Not Found", order=2), + dmc.Text( + "The article you're looking for doesn't exist or has been moved.", + size="md", + c="dimmed", + ta="center", + ), + dcc.Link( + dmc.Button( + "Back to Blog", + variant="light", + leftSection=DashIconify(icon="tabler:arrow-left", width=18), + ), + href="/blog", + ), + ], + align="center", + gap="md", + py="xl", + ), + size="md", + py="xl", + ) + + +def layout(slug: str = "") -> dmc.Container: + """Generate the article layout dynamically. + + Args: + slug: Article slug from URL path. + """ + if not slug: + return create_not_found() + + article = get_article(slug) + if not article: + return create_not_found() + + meta = article["meta"] + + return dmc.Container( + dmc.Stack( + [ + # Back link + dcc.Link( + dmc.Group( + [ + DashIconify(icon="tabler:arrow-left", width=16), + dmc.Text("Back to Blog", size="sm"), + ], + gap="xs", + ), + href="/blog", + style={"textDecoration": "none"}, + ), + # Article header + dmc.Paper( + dmc.Stack( + [ + dmc.Title(meta["title"], order=1), + dmc.Group( + [ + dmc.Group( + [ + DashIconify( + icon="tabler:calendar", width=16 + ), + dmc.Text( + meta["date"], size="sm", c="dimmed" + ), + ], + gap="xs", + ), + dmc.Group( + [ + dmc.Badge(tag, variant="light", size="sm") + for tag in meta.get("tags", []) + ], + gap="xs", + ), + ], + justify="space-between", + wrap="wrap", + ), + ( + dmc.Text(meta["description"], size="lg", c="dimmed") + if meta.get("description") + else None + ), + ], + gap="sm", + ), + p="xl", + radius="md", + withBorder=True, + ), + # Article content + dmc.Paper( + html.Div( + # Render HTML content from markdown + # Using dangerously_allow_html via dcc.Markdown or html.Div + dcc.Markdown( + article["content"], + className="article-content", + dangerously_allow_html=True, + ), + ), + p="xl", + radius="md", + withBorder=True, + className="article-body", + ), + dmc.Space(h=40), + ], + gap="lg", + ), + size="md", + py="xl", + ) diff --git a/portfolio_app/pages/blog/index.py b/portfolio_app/pages/blog/index.py new file mode 100644 index 0000000..9c46597 --- /dev/null +++ b/portfolio_app/pages/blog/index.py @@ -0,0 +1,113 @@ +"""Blog index page - Article listing.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc +from dash_iconify import DashIconify + +from portfolio_app.utils.markdown_loader import Article, get_all_articles + +dash.register_page(__name__, path="/blog", name="Blog") + +# Page intro +INTRO_TEXT = ( + "I write occasionally about data engineering, automation, and the reality of being " + "a one-person data team. No hot takes, no growth hacking—just things I've learned " + "the hard way." +) + + +def create_article_card(article: Article) -> dmc.Paper: + """Create an article preview card.""" + meta = article["meta"] + return dmc.Paper( + dcc.Link( + dmc.Stack( + [ + dmc.Group( + [ + dmc.Text(meta["title"], fw=600, size="lg"), + dmc.Text(meta["date"], size="sm", c="dimmed"), + ], + justify="space-between", + align="flex-start", + wrap="wrap", + ), + dmc.Text(meta["description"], size="md", c="dimmed", lineClamp=2), + dmc.Group( + [ + dmc.Badge(tag, variant="light", size="sm") + for tag in meta.get("tags", [])[:3] + ], + gap="xs", + ), + ], + gap="sm", + ), + href=f"/blog/{meta['slug']}", + style={"textDecoration": "none", "color": "inherit"}, + ), + p="lg", + radius="md", + withBorder=True, + className="article-card", + ) + + +def create_empty_state() -> dmc.Paper: + """Create empty state when no articles exist.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:article-off", width=48), + size=80, + radius="xl", + variant="light", + color="gray", + ), + dmc.Title("No Articles Yet", order=3), + dmc.Text( + "Articles are coming soon. Check back later!", + size="md", + c="dimmed", + ta="center", + ), + ], + align="center", + gap="md", + py="xl", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def layout() -> dmc.Container: + """Generate the blog index layout dynamically.""" + articles = get_all_articles(include_drafts=False) + + return dmc.Container( + dmc.Stack( + [ + dmc.Title("Blog", order=1, ta="center"), + dmc.Text( + INTRO_TEXT, size="md", c="dimmed", ta="center", maw=600, mx="auto" + ), + dmc.Divider(my="lg"), + ( + dmc.Stack( + [create_article_card(article) for article in articles], + gap="lg", + ) + if articles + else create_empty_state() + ), + dmc.Space(h=40), + ], + gap="lg", + ), + size="md", + py="xl", + ) diff --git a/portfolio_app/pages/contact.py b/portfolio_app/pages/contact.py new file mode 100644 index 0000000..25e60d2 --- /dev/null +++ b/portfolio_app/pages/contact.py @@ -0,0 +1,294 @@ +"""Contact page - Form UI and direct contact information.""" + +import dash +import dash_mantine_components as dmc +from dash import html +from dash_iconify import DashIconify + +dash.register_page(__name__, path="/contact", name="Contact") + +# Contact information +CONTACT_INFO = { + "email": "leobrmi@hotmail.com", + "phone": "(416) 859-7936", + "linkedin": "https://linkedin.com/in/leobmiranda", + "github": "https://github.com/leomiranda", + "location": "Toronto, ON, Canada", +} + +# Page intro text +INTRO_TEXT = ( + "I'm currently open to Senior Data Analyst and Data Engineer roles in Toronto " + "(or remote). If you're working on something interesting and need someone who can " + "build data infrastructure from scratch, I'd like to hear about it." +) + +CONSULTING_TEXT = ( + "For consulting inquiries (automation, dashboards, small business data work), " + "reach out about Bandit Labs." +) + +# Form subject options +SUBJECT_OPTIONS = [ + {"value": "job", "label": "Job Opportunity"}, + {"value": "consulting", "label": "Consulting Inquiry"}, + {"value": "other", "label": "Other"}, +] + + +def create_intro_section() -> dmc.Stack: + """Create the intro text section.""" + return dmc.Stack( + [ + dmc.Title("Get In Touch", order=1, ta="center"), + dmc.Text(INTRO_TEXT, size="md", ta="center", maw=600, mx="auto"), + dmc.Text( + CONSULTING_TEXT, size="md", ta="center", maw=600, mx="auto", c="dimmed" + ), + ], + gap="md", + mb="xl", + ) + + +def create_contact_form() -> dmc.Paper: + """Create the contact form with Formspree integration.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Send a Message", order=2, size="h4"), + # Feedback container for success/error messages + html.Div(id="contact-feedback"), + dmc.TextInput( + id="contact-name", + label="Name", + placeholder="Your name", + leftSection=DashIconify(icon="tabler:user", width=18), + required=True, + ), + dmc.TextInput( + id="contact-email", + label="Email", + placeholder="your.email@example.com", + leftSection=DashIconify(icon="tabler:mail", width=18), + required=True, + ), + dmc.Select( + id="contact-subject", + label="Subject", + placeholder="Select a subject", + data=SUBJECT_OPTIONS, + leftSection=DashIconify(icon="tabler:tag", width=18), + ), + dmc.Textarea( + id="contact-message", + label="Message", + placeholder="Your message...", + minRows=4, + required=True, + ), + # Honeypot field for spam protection (hidden from users) + dmc.TextInput( + id="contact-gotcha", + style={"position": "absolute", "left": "-9999px"}, + tabIndex=-1, + autoComplete="off", + ), + dmc.Button( + "Send Message", + id="contact-submit", + fullWidth=True, + leftSection=DashIconify(icon="tabler:send", width=18), + ), + ], + gap="md", + style={"position": "relative"}, + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_direct_contact() -> dmc.Paper: + """Create the direct contact information section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Direct Contact", order=2, size="h4"), + dmc.Stack( + [ + # Email + dmc.Group( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:mail", width=20), + size="lg", + radius="md", + variant="light", + ), + dmc.Stack( + [ + dmc.Text("Email", size="sm", c="dimmed"), + dmc.Anchor( + CONTACT_INFO["email"], + href=f"mailto:{CONTACT_INFO['email']}", + size="md", + fw=500, + ), + ], + gap=0, + ), + ], + gap="md", + ), + # Phone + dmc.Group( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:phone", width=20), + size="lg", + radius="md", + variant="light", + ), + dmc.Stack( + [ + dmc.Text("Phone", size="sm", c="dimmed"), + dmc.Anchor( + CONTACT_INFO["phone"], + href=f"tel:{CONTACT_INFO['phone'].replace('(', '').replace(')', '').replace(' ', '').replace('-', '')}", + size="md", + fw=500, + ), + ], + gap=0, + ), + ], + gap="md", + ), + # LinkedIn + dmc.Group( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:brand-linkedin", width=20), + size="lg", + radius="md", + variant="light", + color="blue", + ), + dmc.Stack( + [ + dmc.Text("LinkedIn", size="sm", c="dimmed"), + dmc.Anchor( + "linkedin.com/in/leobmiranda", + href=CONTACT_INFO["linkedin"], + target="_blank", + size="md", + fw=500, + ), + ], + gap=0, + ), + ], + gap="md", + ), + # GitHub + dmc.Group( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:brand-github", width=20), + size="lg", + radius="md", + variant="light", + ), + dmc.Stack( + [ + dmc.Text("GitHub", size="sm", c="dimmed"), + dmc.Anchor( + "github.com/leomiranda", + href=CONTACT_INFO["github"], + target="_blank", + size="md", + fw=500, + ), + ], + gap=0, + ), + ], + gap="md", + ), + ], + gap="lg", + ), + ], + gap="lg", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_location_section() -> dmc.Paper: + """Create the location and work eligibility section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Location", order=2, size="h4"), + dmc.Group( + [ + dmc.ThemeIcon( + DashIconify(icon="tabler:map-pin", width=20), + size="lg", + radius="md", + variant="light", + color="red", + ), + dmc.Stack( + [ + dmc.Text(CONTACT_INFO["location"], size="md", fw=500), + dmc.Text( + "Canadian Citizen | Eligible to work in Canada and US", + size="sm", + c="dimmed", + ), + ], + gap=0, + ), + ], + gap="md", + ), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +layout = dmc.Container( + dmc.Stack( + [ + create_intro_section(), + dmc.SimpleGrid( + [ + create_contact_form(), + dmc.Stack( + [ + create_direct_contact(), + create_location_section(), + ], + gap="lg", + ), + ], + cols={"base": 1, "md": 2}, + spacing="xl", + ), + dmc.Space(h=40), + ], + gap="lg", + ), + size="lg", + py="xl", +) diff --git a/portfolio_app/pages/health.py b/portfolio_app/pages/health.py new file mode 100644 index 0000000..119da0f --- /dev/null +++ b/portfolio_app/pages/health.py @@ -0,0 +1,20 @@ +"""Health check endpoint for deployment monitoring.""" + +import dash +from dash import html + +dash.register_page( + __name__, + path="/health", + title="Health Check", +) + + +def layout() -> html.Div: + """Return simple health check response.""" + return html.Div( + [ + html.Pre("status: ok"), + ], + id="health-check", + ) diff --git a/portfolio_app/pages/home.py b/portfolio_app/pages/home.py new file mode 100644 index 0000000..aa6e72a --- /dev/null +++ b/portfolio_app/pages/home.py @@ -0,0 +1,214 @@ +"""Home landing page - Portfolio entry point.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc +from dash_iconify import DashIconify + +dash.register_page(__name__, path="/", name="Home") + +# Hero content from blueprint +HEADLINE = "I turn messy data into systems that actually work." +SUBHEAD = ( + "Data Engineer & Analytics Specialist. 8 years building pipelines, dashboards, " + "and the infrastructure nobody sees but everyone depends on. Based in Toronto." +) + +# Impact metrics +IMPACT_STATS = [ + {"value": "1B+", "label": "Rows processed daily across enterprise platform"}, + {"value": "40%", "label": "Efficiency gain through automation"}, + {"value": "5 Years", "label": "Building DataFlow from zero"}, +] + +# Featured project +FEATURED_PROJECT = { + "title": "Toronto Housing Market Dashboard", + "description": ( + "Real-time analytics on Toronto's housing trends. " + "dbt-powered ETL, Python scraping, Plotly visualization." + ), + "status": "Live", + "dashboard_link": "/toronto", + "repo_link": "https://github.com/leomiranda/personal-portfolio", +} + +# Brief intro +INTRO_TEXT = ( + "I'm a data engineer who's spent the last 8 years in the trenches—building the " + "infrastructure that feeds dashboards, automates the boring stuff, and makes data " + "actually usable. Most of my work has been in contact center operations and energy, " + "where I've had to be scrappy: one-person data teams, legacy systems, stakeholders " + "who need answers yesterday." +) + +INTRO_CLOSING = "I like solving real problems, not theoretical ones." + + +def create_hero_section() -> dmc.Stack: + """Create the hero section with headline, subhead, and CTAs.""" + return dmc.Stack( + [ + dmc.Title( + HEADLINE, + order=1, + ta="center", + size="2.5rem", + ), + dmc.Text( + SUBHEAD, + size="lg", + c="dimmed", + ta="center", + maw=700, + mx="auto", + ), + dmc.Group( + [ + dcc.Link( + dmc.Button( + "View Projects", + size="lg", + variant="filled", + leftSection=DashIconify(icon="tabler:folder", width=20), + ), + href="/projects", + ), + dcc.Link( + dmc.Button( + "Get In Touch", + size="lg", + variant="outline", + leftSection=DashIconify(icon="tabler:mail", width=20), + ), + href="/contact", + ), + ], + justify="center", + gap="md", + mt="md", + ), + ], + gap="md", + py="xl", + ) + + +def create_impact_stat(stat: dict[str, str]) -> dmc.Stack: + """Create a single impact stat.""" + return dmc.Stack( + [ + dmc.Text(stat["value"], fw=700, size="2rem", ta="center"), + dmc.Text(stat["label"], size="sm", c="dimmed", ta="center"), + ], + gap="xs", + align="center", + ) + + +def create_impact_strip() -> dmc.Paper: + """Create the impact statistics strip.""" + return dmc.Paper( + dmc.SimpleGrid( + [create_impact_stat(stat) for stat in IMPACT_STATS], + cols={"base": 1, "sm": 3}, + spacing="xl", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_featured_project() -> dmc.Paper: + """Create the featured project card.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Group( + [ + dmc.Title("Featured Project", order=2, size="h3"), + dmc.Badge( + FEATURED_PROJECT["status"], + color="green", + variant="light", + size="lg", + ), + ], + justify="space-between", + ), + dmc.Title( + FEATURED_PROJECT["title"], + order=3, + size="h4", + ), + dmc.Text( + FEATURED_PROJECT["description"], + size="md", + c="dimmed", + ), + dmc.Group( + [ + dcc.Link( + dmc.Button( + "View Dashboard", + variant="light", + leftSection=DashIconify( + icon="tabler:chart-bar", width=18 + ), + ), + href=FEATURED_PROJECT["dashboard_link"], + ), + dmc.Anchor( + dmc.Button( + "View Repository", + variant="subtle", + leftSection=DashIconify( + icon="tabler:brand-github", width=18 + ), + ), + href=FEATURED_PROJECT["repo_link"], + target="_blank", + ), + ], + gap="sm", + ), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_intro_section() -> dmc.Paper: + """Create the brief intro section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Text(INTRO_TEXT, size="md"), + dmc.Text(INTRO_CLOSING, size="md", fw=500, fs="italic"), + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +layout = dmc.Container( + dmc.Stack( + [ + create_hero_section(), + create_impact_strip(), + create_featured_project(), + create_intro_section(), + dmc.Space(h=40), + ], + gap="xl", + ), + size="md", + py="xl", +) diff --git a/portfolio_app/pages/projects.py b/portfolio_app/pages/projects.py new file mode 100644 index 0000000..edc4dc0 --- /dev/null +++ b/portfolio_app/pages/projects.py @@ -0,0 +1,304 @@ +"""Projects overview page - Hub for all portfolio projects.""" + +from typing import Any + +import dash +import dash_mantine_components as dmc +from dash import dcc +from dash_iconify import DashIconify + +dash.register_page(__name__, path="/projects", name="Projects") + +# Page intro +INTRO_TEXT = ( + "These are projects I've built—some professional (anonymized where needed), " + "some personal. Each one taught me something. Use the sidebar to jump directly " + "to live dashboards or explore the overviews below." +) + +# Project definitions +PROJECTS: list[dict[str, Any]] = [ + { + "title": "Toronto Housing Market Dashboard", + "type": "Personal Project", + "status": "Live", + "status_color": "green", + "problem": ( + "Toronto's housing market moves fast, and most publicly available data " + "is either outdated, behind paywalls, or scattered across dozens of sources. " + "I wanted a single dashboard that tracked trends in real-time." + ), + "built": [ + "Data Pipeline: Python scraper pulling listings data, automated on schedule", + "Transformation Layer: dbt-based SQL architecture (staging -> intermediate -> marts)", + "Visualization: Interactive Plotly-Dash dashboard with filters by neighborhood, price range, property type", + "Infrastructure: PostgreSQL backend, version-controlled in Git", + ], + "tech_stack": "Python, dbt, PostgreSQL, Plotly-Dash, GitHub Actions", + "learned": ( + "Real estate data is messy as hell. Listings get pulled, prices change, " + "duplicates are everywhere. Building a reliable pipeline meant implementing " + 'serious data quality checks and learning to embrace "good enough" over "perfect."' + ), + "dashboard_link": "/toronto", + "repo_link": "https://github.com/leomiranda/personal-portfolio", + }, + { + "title": "US Retail Energy Price Predictor", + "type": "Personal Project", + "status": "Coming Soon", + "status_color": "yellow", + "problem": ( + "Retail energy pricing in deregulated US markets is volatile and opaque. " + "Consumers and analysts lack accessible tools to understand pricing trends " + "and forecast where rates are headed." + ), + "built": [ + "Data Pipeline: Automated ingestion of public pricing data across multiple US markets", + "ML Model: Price prediction using time series forecasting (ARIMA, Prophet, or similar)", + "Transformation Layer: dbt-based SQL architecture for feature engineering", + "Visualization: Interactive dashboard showing historical trends + predictions by state/market", + ], + "tech_stack": "Python, Scikit-learn, dbt, PostgreSQL, Plotly-Dash", + "learned": ( + "This showcases the ML side of my skillset—something the Toronto Housing " + "dashboard doesn't cover. It also leverages my domain expertise from 5+ years " + "in retail energy operations." + ), + "dashboard_link": None, + "repo_link": None, + }, + { + "title": "DataFlow Platform", + "type": "Professional", + "status": "Case Study Pending", + "status_color": "gray", + "problem": ( + "When I joined Summitt Energy, there was no data infrastructure. " + "Reports were manual. Insights were guesswork. I was hired to fix that." + ), + "built": [ + "v1 (2020): Basic ETL scripts pulling Genesys Cloud data into MSSQL", + "v2 (2021): Dimensional model (star schema) with fact/dimension tables", + "v3 (2022): Python refactor with SQLAlchemy ORM, batch processing, error handling", + "v4 (2023-24): dbt-pattern SQL views (staging -> intermediate -> marts), FastAPI layer, CLI tools", + ], + "tech_stack": "Python, SQLAlchemy, FastAPI, MSSQL, Power BI, Genesys Cloud API", + "impact": [ + "21 tables, 1B+ rows", + "5,000+ daily transactions processed", + "40% improvement in reporting efficiency", + "30% reduction in call abandon rate", + "50% faster Average Speed to Answer", + ], + "learned": ( + "Building data infrastructure as a team of one forces brutal prioritization. " + "I learned to ship imperfect solutions fast, iterate based on feedback, " + "and never underestimate how long stakeholder buy-in takes." + ), + "note": "This is proprietary work. A sanitized case study with architecture patterns (no proprietary data) will be published in Phase 3.", + "dashboard_link": None, + "repo_link": None, + }, + { + "title": "AI-Assisted Automation (Bandit Labs)", + "type": "Consulting/Side Business", + "status": "Active", + "status_color": "blue", + "problem": ( + "Small businesses don't need enterprise data platforms—they need someone " + "to eliminate the 4 hours/week they spend manually entering receipts." + ), + "built": [ + "Receipt Processing Automation: OCR pipeline (Tesseract, Google Vision) extracting purchase data from photos", + "Product Margin Tracker: Plotly-Dash dashboard with real-time profitability insights", + "Claude Code Plugins: MCP servers for Gitea, Wiki.js, NetBox integration", + ], + "tech_stack": "Python, Tesseract, Google Vision API, Plotly-Dash, QuickBooks API", + "learned": ( + "Small businesses are underserved by the data/automation industry. " + "Everyone wants to sell them enterprise software they don't need. " + "I like solving problems at a scale where the impact is immediately visible." + ), + "dashboard_link": None, + "repo_link": None, + "external_link": "/lab", + "external_label": "Learn More About Bandit Labs", + }, +] + + +def create_project_card(project: dict[str, Any]) -> dmc.Paper: + """Create a detailed project card.""" + # Build the "What I Built" list + built_items = project.get("built", []) + built_section = ( + dmc.Stack( + [ + dmc.Text("What I Built:", fw=600, size="sm"), + dmc.List( + [dmc.ListItem(dmc.Text(item, size="sm")) for item in built_items], + spacing="xs", + size="sm", + ), + ], + gap="xs", + ) + if built_items + else None + ) + + # Build impact section for DataFlow + impact_items = project.get("impact", []) + impact_section = ( + dmc.Stack( + [ + dmc.Text("Impact:", fw=600, size="sm"), + dmc.Group( + [ + dmc.Badge(item, variant="light", size="sm") + for item in impact_items + ], + gap="xs", + ), + ], + gap="xs", + ) + if impact_items + else None + ) + + # Build action buttons + buttons = [] + if project.get("dashboard_link"): + buttons.append( + dcc.Link( + dmc.Button( + "View Dashboard", + variant="light", + size="sm", + leftSection=DashIconify(icon="tabler:chart-bar", width=16), + ), + href=project["dashboard_link"], + ) + ) + if project.get("repo_link"): + buttons.append( + dmc.Anchor( + dmc.Button( + "View Repository", + variant="subtle", + size="sm", + leftSection=DashIconify(icon="tabler:brand-github", width=16), + ), + href=project["repo_link"], + target="_blank", + ) + ) + if project.get("external_link"): + buttons.append( + dcc.Link( + dmc.Button( + project.get("external_label", "Learn More"), + variant="outline", + size="sm", + leftSection=DashIconify(icon="tabler:arrow-right", width=16), + ), + href=project["external_link"], + ) + ) + + # Handle "Coming Soon" state + if project["status"] == "Coming Soon" and not buttons: + buttons.append( + dmc.Badge("Coming Soon", variant="light", color="yellow", size="lg") + ) + + return dmc.Paper( + dmc.Stack( + [ + # Header + dmc.Group( + [ + dmc.Stack( + [ + dmc.Text(project["title"], fw=600, size="lg"), + dmc.Text(project["type"], size="sm", c="dimmed"), + ], + gap=0, + ), + dmc.Badge( + project["status"], + color=project["status_color"], + variant="light", + size="lg", + ), + ], + justify="space-between", + align="flex-start", + ), + # Problem + dmc.Stack( + [ + dmc.Text("The Problem:", fw=600, size="sm"), + dmc.Text(project["problem"], size="sm", c="dimmed"), + ], + gap="xs", + ), + # What I Built + built_section, + # Impact (if exists) + impact_section, + # Tech Stack + dmc.Group( + [ + dmc.Text("Tech Stack:", fw=600, size="sm"), + dmc.Text(project["tech_stack"], size="sm", c="dimmed"), + ], + gap="xs", + ), + # What I Learned + dmc.Stack( + [ + dmc.Text("What I Learned:", fw=600, size="sm"), + dmc.Text(project["learned"], size="sm", fs="italic"), + ], + gap="xs", + ), + # Note (if exists) + ( + dmc.Alert( + project["note"], + color="gray", + variant="light", + ) + if project.get("note") + else None + ), + # Action buttons + dmc.Group(buttons, gap="sm") if buttons else None, + ], + gap="md", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +layout = dmc.Container( + dmc.Stack( + [ + dmc.Title("Projects", order=1, ta="center"), + dmc.Text( + INTRO_TEXT, size="md", c="dimmed", ta="center", maw=700, mx="auto" + ), + dmc.Divider(my="lg"), + *[create_project_card(project) for project in PROJECTS], + dmc.Space(h=40), + ], + gap="xl", + ), + size="md", + py="xl", +) diff --git a/portfolio_app/pages/resume.py b/portfolio_app/pages/resume.py new file mode 100644 index 0000000..a29cb1e --- /dev/null +++ b/portfolio_app/pages/resume.py @@ -0,0 +1,362 @@ +"""Resume page - Inline display with download options.""" + +from typing import Any + +import dash +import dash_mantine_components as dmc +from dash_iconify import DashIconify + +dash.register_page(__name__, path="/resume", name="Resume") + +# ============================================================================= +# HUMAN TASK: Upload resume content via Gitea +# Replace the placeholder content below with actual resume data. +# You can upload PDF/DOCX files to portfolio_app/assets/resume/ +# ============================================================================= + +# Resume sections - replace with actual content +RESUME_HEADER = { + "name": "Leo Miranda", + "title": "Data Engineer & Analytics Specialist", + "location": "Toronto, ON, Canada", + "email": "leobrmi@hotmail.com", + "phone": "(416) 859-7936", + "linkedin": "linkedin.com/in/leobmiranda", + "github": "github.com/leomiranda", +} + +RESUME_SUMMARY = ( + "Data Engineer with 8 years of experience building enterprise analytics platforms, " + "ETL pipelines, and business intelligence solutions. Proven track record of delivering " + "40% efficiency gains through automation and data infrastructure modernization. " + "Expert in Python, SQL, and dimensional modeling with deep domain expertise in " + "contact center operations and energy retail." +) + +# Experience - placeholder structure +EXPERIENCE = [ + { + "title": "Senior Data Analyst / Data Engineer", + "company": "Summitt Energy", + "location": "Toronto, ON", + "period": "2019 - Present", + "highlights": [ + "Built DataFlow platform from scratch: 21 tables, 1B+ rows, processing 5,000+ daily transactions", + "Achieved 40% improvement in reporting efficiency through automated ETL pipelines", + "Reduced call abandon rate by 30% via KPI framework and real-time dashboards", + "Sole data professional supporting 150+ employees across 9 markets (Canada + US)", + ], + }, + { + "title": "IT Project Coordinator", + "company": "Petrobras", + "location": "Rio de Janeiro, Brazil", + "period": "2015 - 2018", + "highlights": [ + "Coordinated IT infrastructure projects for Fortune 500 energy company", + "Managed vendor relationships and project timelines", + "Developed reporting automation reducing manual effort by 60%", + ], + }, + { + "title": "Project Management Associate", + "company": "Project Management Institute", + "location": "Remote", + "period": "2014 - 2015", + "highlights": [ + "Supported global project management standards development", + "CAPM and ITIL certified during this period", + ], + }, +] + +# Skills - organized by category +SKILLS = { + "Languages": ["Python", "SQL", "R", "VBA"], + "Data Engineering": [ + "ETL/ELT Pipelines", + "Dimensional Modeling", + "dbt", + "SQLAlchemy", + "FastAPI", + ], + "Databases": ["PostgreSQL", "MSSQL", "Redis"], + "Visualization": ["Plotly/Dash", "Power BI", "Tableau"], + "Platforms": ["Genesys Cloud", "Five9", "Zoho CRM", "Azure DevOps"], + "Currently Learning": ["Azure DP-203", "Airflow", "Snowflake"], +} + +# Education +EDUCATION = [ + { + "degree": "Bachelor of Business Administration", + "school": "Universidade Federal do Rio de Janeiro", + "year": "2014", + }, +] + +# Certifications +CERTIFICATIONS = [ + "CAPM (Certified Associate in Project Management)", + "ITIL Foundation", + "Azure DP-203 (In Progress)", +] + + +def create_header_section() -> dmc.Paper: + """Create the resume header with contact info.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title(RESUME_HEADER["name"], order=1, ta="center"), + dmc.Text(RESUME_HEADER["title"], size="xl", c="dimmed", ta="center"), + dmc.Divider(my="sm"), + dmc.Group( + [ + dmc.Group( + [ + DashIconify(icon="tabler:map-pin", width=16), + dmc.Text(RESUME_HEADER["location"], size="sm"), + ], + gap="xs", + ), + dmc.Group( + [ + DashIconify(icon="tabler:mail", width=16), + dmc.Text(RESUME_HEADER["email"], size="sm"), + ], + gap="xs", + ), + dmc.Group( + [ + DashIconify(icon="tabler:phone", width=16), + dmc.Text(RESUME_HEADER["phone"], size="sm"), + ], + gap="xs", + ), + ], + justify="center", + gap="lg", + wrap="wrap", + ), + dmc.Group( + [ + dmc.Anchor( + dmc.Group( + [ + DashIconify(icon="tabler:brand-linkedin", width=16), + dmc.Text("LinkedIn", size="sm"), + ], + gap="xs", + ), + href=f"https://{RESUME_HEADER['linkedin']}", + target="_blank", + ), + dmc.Anchor( + dmc.Group( + [ + DashIconify(icon="tabler:brand-github", width=16), + dmc.Text("GitHub", size="sm"), + ], + gap="xs", + ), + href=f"https://{RESUME_HEADER['github']}", + target="_blank", + ), + ], + justify="center", + gap="lg", + ), + ], + gap="sm", + ), + p="xl", + radius="md", + withBorder=True, + ) + + +def create_download_section() -> dmc.Group: + """Create download buttons for resume files.""" + # Note: Buttons disabled until files are uploaded + return dmc.Group( + [ + dmc.Button( + "Download PDF", + variant="filled", + leftSection=DashIconify(icon="tabler:file-type-pdf", width=18), + disabled=True, # Enable after uploading resume.pdf to assets + ), + dmc.Button( + "Download DOCX", + variant="outline", + leftSection=DashIconify(icon="tabler:file-type-docx", width=18), + disabled=True, # Enable after uploading resume.docx to assets + ), + dmc.Anchor( + dmc.Button( + "View on LinkedIn", + variant="subtle", + leftSection=DashIconify(icon="tabler:brand-linkedin", width=18), + ), + href=f"https://{RESUME_HEADER['linkedin']}", + target="_blank", + ), + ], + justify="center", + gap="md", + ) + + +def create_summary_section() -> dmc.Paper: + """Create the professional summary section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Professional Summary", order=2, size="h4"), + dmc.Text(RESUME_SUMMARY, size="md"), + ], + gap="sm", + ), + p="lg", + radius="md", + withBorder=True, + ) + + +def create_experience_item(exp: dict[str, Any]) -> dmc.Stack: + """Create a single experience entry.""" + return dmc.Stack( + [ + dmc.Group( + [ + dmc.Text(exp["title"], fw=600), + dmc.Text(exp["period"], size="sm", c="dimmed"), + ], + justify="space-between", + ), + dmc.Text(f"{exp['company']} | {exp['location']}", size="sm", c="dimmed"), + dmc.List( + [dmc.ListItem(dmc.Text(h, size="sm")) for h in exp["highlights"]], + spacing="xs", + size="sm", + ), + ], + gap="xs", + ) + + +def create_experience_section() -> dmc.Paper: + """Create the experience section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Experience", order=2, size="h4"), + *[create_experience_item(exp) for exp in EXPERIENCE], + ], + gap="lg", + ), + p="lg", + radius="md", + withBorder=True, + ) + + +def create_skills_section() -> dmc.Paper: + """Create the skills section with badges.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Skills", order=2, size="h4"), + dmc.SimpleGrid( + [ + dmc.Stack( + [ + dmc.Text(category, fw=600, size="sm"), + dmc.Group( + [ + dmc.Badge(skill, variant="light", size="sm") + for skill in skills + ], + gap="xs", + ), + ], + gap="xs", + ) + for category, skills in SKILLS.items() + ], + cols={"base": 1, "sm": 2}, + spacing="md", + ), + ], + gap="md", + ), + p="lg", + radius="md", + withBorder=True, + ) + + +def create_education_section() -> dmc.Paper: + """Create education and certifications section.""" + return dmc.Paper( + dmc.Stack( + [ + dmc.Title("Education & Certifications", order=2, size="h4"), + dmc.Stack( + [ + dmc.Stack( + [ + dmc.Text(edu["degree"], fw=600), + dmc.Text( + f"{edu['school']} | {edu['year']}", + size="sm", + c="dimmed", + ), + ], + gap=0, + ) + for edu in EDUCATION + ], + gap="sm", + ), + dmc.Divider(my="sm"), + dmc.Group( + [ + dmc.Badge(cert, variant="outline", size="md") + for cert in CERTIFICATIONS + ], + gap="xs", + ), + ], + gap="md", + ), + p="lg", + radius="md", + withBorder=True, + ) + + +layout = dmc.Container( + dmc.Stack( + [ + create_header_section(), + create_download_section(), + dmc.Alert( + "Resume files (PDF/DOCX) will be available for download once uploaded. " + "The inline content below is a preview.", + title="Downloads Coming Soon", + color="blue", + variant="light", + ), + create_summary_section(), + create_experience_section(), + create_skills_section(), + create_education_section(), + dmc.Space(h=40), + ], + gap="lg", + ), + size="md", + py="xl", +) diff --git a/portfolio_app/pages/toronto/__init__.py b/portfolio_app/pages/toronto/__init__.py new file mode 100644 index 0000000..53e3907 --- /dev/null +++ b/portfolio_app/pages/toronto/__init__.py @@ -0,0 +1 @@ +"""Toronto Housing Dashboard pages.""" diff --git a/portfolio_app/pages/toronto/callbacks/__init__.py b/portfolio_app/pages/toronto/callbacks/__init__.py new file mode 100644 index 0000000..0fe06c7 --- /dev/null +++ b/portfolio_app/pages/toronto/callbacks/__init__.py @@ -0,0 +1,14 @@ +"""Toronto dashboard callbacks. + +Registers all callbacks for the neighbourhood dashboard including: +- Map interactions (choropleth click, metric selection) +- Chart updates (supporting visualizations) +- Selection handling (neighbourhood dropdown, details panels) +""" + +# Import all callback modules to register them with Dash +from . import ( + chart_callbacks, # noqa: F401 + map_callbacks, # noqa: F401 + selection_callbacks, # noqa: F401 +) diff --git a/portfolio_app/pages/toronto/callbacks/chart_callbacks.py b/portfolio_app/pages/toronto/callbacks/chart_callbacks.py new file mode 100644 index 0000000..1207100 --- /dev/null +++ b/portfolio_app/pages/toronto/callbacks/chart_callbacks.py @@ -0,0 +1,405 @@ +"""Chart callbacks for supporting visualizations.""" +# mypy: disable-error-code="misc,no-untyped-def,arg-type" + +import pandas as pd +import plotly.graph_objects as go +from dash import Input, Output, callback + +from portfolio_app.figures import ( + create_donut_chart, + create_horizontal_bar, + create_radar_figure, + create_scatter_figure, +) +from portfolio_app.toronto.services import ( + get_amenities_data, + get_city_averages, + get_demographics_data, + get_housing_data, + get_neighbourhood_details, + get_safety_data, +) + + +@callback( + Output("overview-scatter-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_overview_scatter(year: str) -> go.Figure: + """Update income vs safety scatter plot.""" + year_int = int(year) if year else 2021 + df = get_demographics_data(year_int) + safety_df = get_safety_data(year_int) + + if df.empty or safety_df.empty: + return _empty_chart("No data available") + + # Merge demographics with safety + merged = df.merge( + safety_df[["neighbourhood_id", "total_crime_rate"]], + on="neighbourhood_id", + how="left", + ) + + # Compute safety score (inverse of crime rate) + if "total_crime_rate" in merged.columns: + max_crime = merged["total_crime_rate"].max() + if max_crime and max_crime > 0: + merged["safety_score"] = 100 - ( + merged["total_crime_rate"] / max_crime * 100 + ) + else: + merged["safety_score"] = 50 # Default if no crime data + + # Fill NULL population with median or default value for sizing + if "population" in merged.columns: + median_pop = merged["population"].median() + default_pop = median_pop if pd.notna(median_pop) else 10000 + merged["population"] = merged["population"].fillna(default_pop) + + # Filter rows with required data for scatter plot + merged = merged.dropna(subset=["median_household_income", "safety_score"]) + + if merged.empty: + return _empty_chart("Insufficient data for scatter plot") + + data = merged.to_dict("records") + + return create_scatter_figure( + data=data, + x_column="median_household_income", + y_column="safety_score", + name_column="neighbourhood_name", + size_column="population", + title="Income vs Safety", + x_title="Median Household Income ($)", + y_title="Safety Score", + trendline=True, + ) + + +@callback( + Output("housing-trend-chart", "figure"), + Input("toronto-year-select", "value"), + Input("toronto-selected-neighbourhood", "data"), +) +def update_housing_trend(year: str, neighbourhood_id: int | None) -> go.Figure: + """Update housing rent trend chart.""" + # For now, show city averages as we don't have multi-year data + # This would be a time series if we had historical data + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + + if not averages: + return _empty_chart("No trend data available") + + # Placeholder for trend data - would be historical + base_rent = averages.get("avg_rent_2bed") or 2000 + data = [ + {"year": "2019", "avg_rent": base_rent * 0.85}, + {"year": "2020", "avg_rent": base_rent * 0.88}, + {"year": "2021", "avg_rent": base_rent * 0.92}, + {"year": "2022", "avg_rent": base_rent * 0.96}, + {"year": "2023", "avg_rent": base_rent}, + ] + + fig = go.Figure() + fig.add_trace( + go.Scatter( + x=[d["year"] for d in data], + y=[d["avg_rent"] for d in data], + mode="lines+markers", + line={"color": "#2196F3", "width": 2}, + marker={"size": 8}, + name="City Average", + ) + ) + + fig.update_layout( + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "rgba(128,128,128,0.2)"}, + yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Avg Rent (2BR)"}, + showlegend=False, + margin={"l": 40, "r": 10, "t": 10, "b": 30}, + ) + + return fig + + +@callback( + Output("housing-types-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_housing_types(year: str) -> go.Figure: + """Update dwelling types breakdown chart.""" + year_int = int(year) if year else 2021 + df = get_housing_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Aggregate tenure types across city + owner_pct = df["pct_owner_occupied"].mean() + renter_pct = df["pct_renter_occupied"].mean() + + data = [ + {"type": "Owner Occupied", "percentage": owner_pct}, + {"type": "Renter Occupied", "percentage": renter_pct}, + ] + + return create_donut_chart( + data=data, + name_column="type", + value_column="percentage", + colors=["#4CAF50", "#2196F3"], + ) + + +@callback( + Output("safety-trend-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_safety_trend(year: str) -> go.Figure: + """Update crime trend chart.""" + # Placeholder for trend - would need historical data + data = [ + {"year": "2019", "crime_rate": 4500}, + {"year": "2020", "crime_rate": 4200}, + {"year": "2021", "crime_rate": 4100}, + {"year": "2022", "crime_rate": 4300}, + {"year": "2023", "crime_rate": 4250}, + ] + + fig = go.Figure() + fig.add_trace( + go.Scatter( + x=[d["year"] for d in data], + y=[d["crime_rate"] for d in data], + mode="lines+markers", + line={"color": "#FF5722", "width": 2}, + marker={"size": 8}, + fill="tozeroy", + fillcolor="rgba(255,87,34,0.1)", + ) + ) + + fig.update_layout( + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"gridcolor": "rgba(128,128,128,0.2)"}, + yaxis={"gridcolor": "rgba(128,128,128,0.2)", "title": "Crime Rate per 100K"}, + showlegend=False, + margin={"l": 40, "r": 10, "t": 10, "b": 30}, + ) + + return fig + + +@callback( + Output("safety-types-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_safety_types(year: str) -> go.Figure: + """Update crime by category chart.""" + year_int = int(year) if year else 2021 + df = get_safety_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Aggregate crime types across city + violent = df["violent_crimes"].sum() if "violent_crimes" in df.columns else 0 + property_crimes = ( + df["property_crimes"].sum() if "property_crimes" in df.columns else 0 + ) + theft = df["theft_crimes"].sum() if "theft_crimes" in df.columns else 0 + other = ( + df["total_crimes"].sum() - violent - property_crimes - theft + if "total_crimes" in df.columns + else 0 + ) + + data = [ + {"category": "Violent", "count": int(violent)}, + {"category": "Property", "count": int(property_crimes)}, + {"category": "Theft", "count": int(theft)}, + {"category": "Other", "count": int(max(0, other))}, + ] + + return create_horizontal_bar( + data=data, + name_column="category", + value_column="count", + color="#FF5722", + ) + + +@callback( + Output("demographics-age-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_demographics_age(year: str) -> go.Figure: + """Update age distribution chart.""" + year_int = int(year) if year else 2021 + df = get_demographics_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Calculate average age distribution + under_18 = df["pct_under_18"].mean() if "pct_under_18" in df.columns else 20 + age_18_64 = df["pct_18_to_64"].mean() if "pct_18_to_64" in df.columns else 65 + over_65 = df["pct_65_plus"].mean() if "pct_65_plus" in df.columns else 15 + + data = [ + {"age_group": "Under 18", "percentage": under_18}, + {"age_group": "18-64", "percentage": age_18_64}, + {"age_group": "65+", "percentage": over_65}, + ] + + return create_donut_chart( + data=data, + name_column="age_group", + value_column="percentage", + colors=["#9C27B0", "#673AB7", "#3F51B5"], + ) + + +@callback( + Output("demographics-income-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_demographics_income(year: str) -> go.Figure: + """Update income distribution chart.""" + year_int = int(year) if year else 2021 + df = get_demographics_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Create income quintile distribution + if "income_quintile" in df.columns: + quintile_counts = df["income_quintile"].value_counts().sort_index() + data = [ + {"bracket": f"Q{q}", "count": int(count)} + for q, count in quintile_counts.items() + ] + else: + # Fallback to placeholder + data = [ + {"bracket": "Q1 (Low)", "count": 32}, + {"bracket": "Q2", "count": 32}, + {"bracket": "Q3 (Mid)", "count": 32}, + {"bracket": "Q4", "count": 31}, + {"bracket": "Q5 (High)", "count": 31}, + ] + + return create_horizontal_bar( + data=data, + name_column="bracket", + value_column="count", + color="#4CAF50", + sort=False, + ) + + +@callback( + Output("amenities-breakdown-chart", "figure"), + Input("toronto-year-select", "value"), +) +def update_amenities_breakdown(year: str) -> go.Figure: + """Update amenity breakdown chart.""" + year_int = int(year) if year else 2021 + df = get_amenities_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Aggregate amenity counts + parks = df["park_count"].sum() if "park_count" in df.columns else 0 + schools = df["school_count"].sum() if "school_count" in df.columns else 0 + childcare = df["childcare_count"].sum() if "childcare_count" in df.columns else 0 + + data = [ + {"type": "Parks", "count": int(parks)}, + {"type": "Schools", "count": int(schools)}, + {"type": "Childcare", "count": int(childcare)}, + ] + + return create_horizontal_bar( + data=data, + name_column="type", + value_column="count", + color="#4CAF50", + ) + + +@callback( + Output("amenities-radar-chart", "figure"), + Input("toronto-year-select", "value"), + Input("toronto-selected-neighbourhood", "data"), +) +def update_amenities_radar(year: str, neighbourhood_id: int | None) -> go.Figure: + """Update amenity comparison radar chart.""" + year_int = int(year) if year else 2021 + + # Get city averages + averages = get_city_averages(year_int) + + amenity_score = averages.get("avg_amenity_score") or 50 + city_data = { + "parks_per_1000": amenity_score / 100 * 10, + "schools_per_1000": amenity_score / 100 * 5, + "childcare_per_1000": amenity_score / 100 * 3, + "transit_access": 70, + } + + data = [city_data] + + # Add selected neighbourhood if available + if neighbourhood_id: + details = get_neighbourhood_details(neighbourhood_id, year_int) + if details: + selected_data = { + "parks_per_1000": details.get("park_count", 0) / 10, + "schools_per_1000": details.get("school_count", 0) / 5, + "childcare_per_1000": 3, + "transit_access": 70, + } + data.insert(0, selected_data) + + return create_radar_figure( + data=data, + metrics=[ + "parks_per_1000", + "schools_per_1000", + "childcare_per_1000", + "transit_access", + ], + fill=True, + ) + + +def _empty_chart(message: str) -> go.Figure: + """Create an empty chart with a message.""" + fig = go.Figure() + fig.update_layout( + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"visible": False}, + yaxis={"visible": False}, + ) + fig.add_annotation( + text=message, + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + return fig diff --git a/portfolio_app/pages/toronto/callbacks/map_callbacks.py b/portfolio_app/pages/toronto/callbacks/map_callbacks.py new file mode 100644 index 0000000..9aef1e8 --- /dev/null +++ b/portfolio_app/pages/toronto/callbacks/map_callbacks.py @@ -0,0 +1,304 @@ +"""Map callbacks for choropleth interactions.""" +# mypy: disable-error-code="misc,no-untyped-def,arg-type,no-any-return" + +import plotly.graph_objects as go +from dash import Input, Output, State, callback, no_update + +from portfolio_app.figures import create_choropleth_figure, create_ranking_bar +from portfolio_app.toronto.services import ( + get_amenities_data, + get_demographics_data, + get_housing_data, + get_neighbourhoods_geojson, + get_overview_data, + get_safety_data, +) + + +@callback( + Output("overview-choropleth", "figure"), + Input("overview-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_overview_choropleth(metric: str, year: str) -> go.Figure: + """Update the overview tab choropleth map.""" + year_int = int(year) if year else 2021 + df = get_overview_data(year_int) + geojson = get_neighbourhoods_geojson(year_int) + + if df.empty: + return _empty_map("No data available") + + data = df.to_dict("records") + + # Color scales based on metric + color_scale = { + "livability_score": "Viridis", + "safety_score": "Greens", + "affordability_score": "Blues", + "amenity_score": "Purples", + }.get(metric, "Viridis") + + return create_choropleth_figure( + geojson=geojson, + data=data, + location_key="neighbourhood_id", + color_column=metric or "livability_score", + hover_data=["neighbourhood_name", "population"], + color_scale=color_scale, + ) + + +@callback( + Output("housing-choropleth", "figure"), + Input("housing-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_housing_choropleth(metric: str, year: str) -> go.Figure: + """Update the housing tab choropleth map.""" + year_int = int(year) if year else 2021 + df = get_housing_data(year_int) + geojson = get_neighbourhoods_geojson(year_int) + + if df.empty: + return _empty_map("No housing data available") + + data = df.to_dict("records") + + color_scale = { + "affordability_index": "RdYlGn_r", + "avg_rent_2bed": "Oranges", + "rent_to_income_pct": "Reds", + "vacancy_rate": "Blues", + }.get(metric, "Oranges") + + return create_choropleth_figure( + geojson=geojson, + data=data, + location_key="neighbourhood_id", + color_column=metric or "affordability_index", + hover_data=["neighbourhood_name", "avg_rent_2bed", "vacancy_rate"], + color_scale=color_scale, + ) + + +@callback( + Output("safety-choropleth", "figure"), + Input("safety-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_safety_choropleth(metric: str, year: str) -> go.Figure: + """Update the safety tab choropleth map.""" + year_int = int(year) if year else 2021 + df = get_safety_data(year_int) + geojson = get_neighbourhoods_geojson(year_int) + + if df.empty: + return _empty_map("No safety data available") + + data = df.to_dict("records") + + return create_choropleth_figure( + geojson=geojson, + data=data, + location_key="neighbourhood_id", + color_column=metric or "total_crime_rate", + hover_data=["neighbourhood_name", "total_crimes"], + color_scale="Reds", + ) + + +@callback( + Output("demographics-choropleth", "figure"), + Input("demographics-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_demographics_choropleth(metric: str, year: str) -> go.Figure: + """Update the demographics tab choropleth map.""" + year_int = int(year) if year else 2021 + df = get_demographics_data(year_int) + geojson = get_neighbourhoods_geojson(year_int) + + if df.empty: + return _empty_map("No demographics data available") + + data = df.to_dict("records") + + color_scale = { + "population": "YlOrBr", + "median_income": "Greens", + "median_age": "Blues", + "diversity_index": "Purples", + }.get(metric, "YlOrBr") + + # Map frontend metric names to column names + column_map = { + "population": "population", + "median_income": "median_household_income", + "median_age": "median_age", + "diversity_index": "diversity_index", + } + column = column_map.get(metric, "population") + + return create_choropleth_figure( + geojson=geojson, + data=data, + location_key="neighbourhood_id", + color_column=column, + hover_data=["neighbourhood_name"], + color_scale=color_scale, + ) + + +@callback( + Output("amenities-choropleth", "figure"), + Input("amenities-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_amenities_choropleth(metric: str, year: str) -> go.Figure: + """Update the amenities tab choropleth map.""" + year_int = int(year) if year else 2021 + df = get_amenities_data(year_int) + geojson = get_neighbourhoods_geojson(year_int) + + if df.empty: + return _empty_map("No amenities data available") + + data = df.to_dict("records") + + # Map frontend metric names to column names + column_map = { + "amenity_score": "amenity_score", + "parks_per_capita": "parks_per_1000", + "schools_per_capita": "schools_per_1000", + "transit_score": "total_amenities_per_1000", + } + column = column_map.get(metric, "amenity_score") + + return create_choropleth_figure( + geojson=geojson, + data=data, + location_key="neighbourhood_id", + color_column=column, + hover_data=["neighbourhood_name", "park_count", "school_count"], + color_scale="Greens", + ) + + +@callback( + Output("toronto-selected-neighbourhood", "data"), + Input("overview-choropleth", "clickData"), + Input("housing-choropleth", "clickData"), + Input("safety-choropleth", "clickData"), + Input("demographics-choropleth", "clickData"), + Input("amenities-choropleth", "clickData"), + State("toronto-tabs", "value"), + prevent_initial_call=True, +) +def handle_map_click( + overview_click, + housing_click, + safety_click, + demographics_click, + amenities_click, + active_tab: str, +) -> int | None: + """Extract neighbourhood ID from map click.""" + # Get the click data for the active tab + click_map = { + "overview": overview_click, + "housing": housing_click, + "safety": safety_click, + "demographics": demographics_click, + "amenities": amenities_click, + } + + click_data = click_map.get(active_tab) + + if not click_data: + return no_update + + try: + # Extract neighbourhood_id from click data + point = click_data["points"][0] + location = point.get("location") or point.get("customdata", [None])[0] + if location: + return int(location) + except (KeyError, IndexError, TypeError): + pass + + return no_update + + +@callback( + Output("overview-rankings-chart", "figure"), + Input("overview-metric-select", "value"), + Input("toronto-year-select", "value"), +) +def update_rankings_chart(metric: str, year: str) -> go.Figure: + """Update the top/bottom rankings bar chart.""" + year_int = int(year) if year else 2021 + df = get_overview_data(year_int) + + if df.empty: + return _empty_chart("No data available") + + # Use the selected metric for ranking + metric = metric or "livability_score" + data = df.to_dict("records") + + return create_ranking_bar( + data=data, + name_column="neighbourhood_name", + value_column=metric, + title=f"Top & Bottom 10 by {metric.replace('_', ' ').title()}", + top_n=10, + bottom_n=10, + ) + + +def _empty_map(message: str) -> go.Figure: + """Create an empty map with a message.""" + fig = go.Figure() + fig.update_layout( + mapbox={ + "style": "carto-darkmatter", + "center": {"lat": 43.7, "lon": -79.4}, + "zoom": 9.5, + }, + margin={"l": 0, "r": 0, "t": 0, "b": 0}, + paper_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + ) + fig.add_annotation( + text=message, + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + return fig + + +def _empty_chart(message: str) -> go.Figure: + """Create an empty chart with a message.""" + fig = go.Figure() + fig.update_layout( + paper_bgcolor="rgba(0,0,0,0)", + plot_bgcolor="rgba(0,0,0,0)", + font_color="#c9c9c9", + xaxis={"visible": False}, + yaxis={"visible": False}, + ) + fig.add_annotation( + text=message, + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font={"size": 14, "color": "#888888"}, + ) + return fig diff --git a/portfolio_app/pages/toronto/callbacks/selection_callbacks.py b/portfolio_app/pages/toronto/callbacks/selection_callbacks.py new file mode 100644 index 0000000..ecb2655 --- /dev/null +++ b/portfolio_app/pages/toronto/callbacks/selection_callbacks.py @@ -0,0 +1,309 @@ +"""Selection callbacks for dropdowns and neighbourhood details.""" +# mypy: disable-error-code="misc,no-untyped-def,type-arg" + +import dash_mantine_components as dmc +from dash import Input, Output, callback + +from portfolio_app.toronto.services import ( + get_city_averages, + get_neighbourhood_details, + get_neighbourhood_list, +) + + +@callback( + Output("toronto-neighbourhood-select", "data"), + Input("toronto-year-select", "value"), +) +def populate_neighbourhood_dropdown(year: str) -> list[dict]: + """Populate the neighbourhood search dropdown.""" + year_int = int(year) if year else 2021 + neighbourhoods = get_neighbourhood_list(year_int) + + return [ + {"value": str(n["neighbourhood_id"]), "label": n["neighbourhood_name"]} + for n in neighbourhoods + ] + + +@callback( + Output("toronto-selected-neighbourhood", "data", allow_duplicate=True), + Input("toronto-neighbourhood-select", "value"), + prevent_initial_call=True, +) +def select_from_dropdown(value: str | None) -> int | None: + """Update selected neighbourhood from dropdown.""" + if value: + return int(value) + return None + + +@callback( + Output("toronto-compare-btn", "disabled"), + Input("toronto-selected-neighbourhood", "data"), +) +def toggle_compare_button(neighbourhood_id: int | None) -> bool: + """Enable compare button when a neighbourhood is selected.""" + return neighbourhood_id is None + + +# Overview tab KPIs +@callback( + Output("overview-city-avg", "children"), + Input("toronto-year-select", "value"), +) +def update_overview_city_avg(year: str) -> str: + """Update the city average livability score.""" + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + score = averages.get("avg_livability_score", 72) + return f"{score:.0f}" if score else "—" + + +@callback( + Output("overview-selected-name", "children"), + Output("overview-selected-scores", "children"), + Input("toronto-selected-neighbourhood", "data"), + Input("toronto-year-select", "value"), +) +def update_overview_selected(neighbourhood_id: int | None, year: str): + """Update the selected neighbourhood details in overview tab.""" + if not neighbourhood_id: + return "Click map to select", [dmc.Text("—", c="dimmed")] + + year_int = int(year) if year else 2021 + details = get_neighbourhood_details(neighbourhood_id, year_int) + + if not details: + return "Unknown", [dmc.Text("No data", c="dimmed")] + + name = details.get("neighbourhood_name", "Unknown") + scores = [ + dmc.Group( + [ + dmc.Text("Livability:", size="sm"), + dmc.Text( + f"{details.get('livability_score', 0):.0f}", size="sm", fw=700 + ), + ], + justify="space-between", + ), + dmc.Group( + [ + dmc.Text("Safety:", size="sm"), + dmc.Text(f"{details.get('safety_score', 0):.0f}", size="sm", fw=700), + ], + justify="space-between", + ), + dmc.Group( + [ + dmc.Text("Affordability:", size="sm"), + dmc.Text( + f"{details.get('affordability_score', 0):.0f}", size="sm", fw=700 + ), + ], + justify="space-between", + ), + ] + + return name, scores + + +# Housing tab KPIs +@callback( + Output("housing-city-rent", "children"), + Output("housing-rent-change", "children"), + Input("toronto-year-select", "value"), +) +def update_housing_kpis(year: str): + """Update housing tab KPI cards.""" + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + + rent = averages.get("avg_rent_2bed", 2450) + rent_str = f"${rent:,.0f}" if rent else "—" + + # Placeholder change - would come from historical data + change = "+4.2% YoY" + + return rent_str, change + + +@callback( + Output("housing-selected-name", "children"), + Output("housing-selected-details", "children"), + Input("toronto-selected-neighbourhood", "data"), + Input("toronto-year-select", "value"), +) +def update_housing_selected(neighbourhood_id: int | None, year: str): + """Update selected neighbourhood details in housing tab.""" + if not neighbourhood_id: + return "Click map to select", [dmc.Text("—", c="dimmed")] + + year_int = int(year) if year else 2021 + details = get_neighbourhood_details(neighbourhood_id, year_int) + + if not details: + return "Unknown", [dmc.Text("No data", c="dimmed")] + + name = details.get("neighbourhood_name", "Unknown") + rent = details.get("avg_rent_2bed") + vacancy = details.get("vacancy_rate") + + info = [ + dmc.Text(f"2BR Rent: ${rent:,.0f}" if rent else "2BR Rent: —", size="sm"), + dmc.Text(f"Vacancy: {vacancy:.1f}%" if vacancy else "Vacancy: —", size="sm"), + ] + + return name, info + + +# Safety tab KPIs +@callback( + Output("safety-city-rate", "children"), + Output("safety-rate-change", "children"), + Input("toronto-year-select", "value"), +) +def update_safety_kpis(year: str): + """Update safety tab KPI cards.""" + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + + rate = averages.get("avg_crime_rate", 4250) + rate_str = f"{rate:,.0f}" if rate else "—" + + # Placeholder change + change = "-2.1% YoY" + + return rate_str, change + + +@callback( + Output("safety-selected-name", "children"), + Output("safety-selected-details", "children"), + Input("toronto-selected-neighbourhood", "data"), + Input("toronto-year-select", "value"), +) +def update_safety_selected(neighbourhood_id: int | None, year: str): + """Update selected neighbourhood details in safety tab.""" + if not neighbourhood_id: + return "Click map to select", [dmc.Text("—", c="dimmed")] + + year_int = int(year) if year else 2021 + details = get_neighbourhood_details(neighbourhood_id, year_int) + + if not details: + return "Unknown", [dmc.Text("No data", c="dimmed")] + + name = details.get("neighbourhood_name", "Unknown") + crime_rate = details.get("crime_rate_per_100k") + + info = [ + dmc.Text( + f"Crime Rate: {crime_rate:,.0f}/100K" if crime_rate else "Crime Rate: —", + size="sm", + ), + ] + + return name, info + + +# Demographics tab KPIs +@callback( + Output("demographics-city-pop", "children"), + Output("demographics-pop-change", "children"), + Input("toronto-year-select", "value"), +) +def update_demographics_kpis(year: str): + """Update demographics tab KPI cards.""" + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + + pop = averages.get("total_population", 2790000) + if pop and pop >= 1000000: + pop_str = f"{pop / 1000000:.2f}M" + elif pop: + pop_str = f"{pop:,.0f}" + else: + pop_str = "—" + + change = "+2.3% since 2016" + + return pop_str, change + + +@callback( + Output("demographics-selected-name", "children"), + Output("demographics-selected-details", "children"), + Input("toronto-selected-neighbourhood", "data"), + Input("toronto-year-select", "value"), +) +def update_demographics_selected(neighbourhood_id: int | None, year: str): + """Update selected neighbourhood details in demographics tab.""" + if not neighbourhood_id: + return "Click map to select", [dmc.Text("—", c="dimmed")] + + year_int = int(year) if year else 2021 + details = get_neighbourhood_details(neighbourhood_id, year_int) + + if not details: + return "Unknown", [dmc.Text("No data", c="dimmed")] + + name = details.get("neighbourhood_name", "Unknown") + pop = details.get("population") + income = details.get("median_household_income") + + info = [ + dmc.Text(f"Population: {pop:,}" if pop else "Population: —", size="sm"), + dmc.Text( + f"Median Income: ${income:,.0f}" if income else "Median Income: —", + size="sm", + ), + ] + + return name, info + + +# Amenities tab KPIs +@callback( + Output("amenities-city-score", "children"), + Input("toronto-year-select", "value"), +) +def update_amenities_kpis(year: str) -> str: + """Update amenities tab KPI cards.""" + year_int = int(year) if year else 2021 + averages = get_city_averages(year_int) + + score = averages.get("avg_amenity_score", 68) + return f"{score:.0f}" if score else "—" + + +@callback( + Output("amenities-selected-name", "children"), + Output("amenities-selected-details", "children"), + Input("toronto-selected-neighbourhood", "data"), + Input("toronto-year-select", "value"), +) +def update_amenities_selected(neighbourhood_id: int | None, year: str): + """Update selected neighbourhood details in amenities tab.""" + if not neighbourhood_id: + return "Click map to select", [dmc.Text("—", c="dimmed")] + + year_int = int(year) if year else 2021 + details = get_neighbourhood_details(neighbourhood_id, year_int) + + if not details: + return "Unknown", [dmc.Text("No data", c="dimmed")] + + name = details.get("neighbourhood_name", "Unknown") + parks = details.get("park_count") + schools = details.get("school_count") + + info = [ + dmc.Text(f"Parks: {parks}" if parks is not None else "Parks: —", size="sm"), + dmc.Text( + f"Schools: {schools}" if schools is not None else "Schools: —", size="sm" + ), + ] + + return name, info diff --git a/portfolio_app/pages/toronto/dashboard.py b/portfolio_app/pages/toronto/dashboard.py new file mode 100644 index 0000000..7625bd5 --- /dev/null +++ b/portfolio_app/pages/toronto/dashboard.py @@ -0,0 +1,206 @@ +"""Toronto Neighbourhood Dashboard page. + +Displays neighbourhood-level data across 5 tabs: Overview, Housing, Safety, +Demographics, and Amenities. Each tab provides interactive choropleth maps, +KPI cards, and supporting charts. +""" + +import dash +import dash_mantine_components as dmc +from dash import dcc +from dash_iconify import DashIconify + +from portfolio_app.pages.toronto.tabs import ( + create_amenities_tab, + create_demographics_tab, + create_housing_tab, + create_overview_tab, + create_safety_tab, +) + +dash.register_page(__name__, path="/toronto", name="Toronto Neighbourhoods") + +# Tab configuration +TAB_CONFIG = [ + { + "value": "overview", + "label": "Overview", + "icon": "tabler:chart-pie", + "color": "blue", + }, + { + "value": "housing", + "label": "Housing", + "icon": "tabler:home", + "color": "teal", + }, + { + "value": "safety", + "label": "Safety", + "icon": "tabler:shield-check", + "color": "orange", + }, + { + "value": "demographics", + "label": "Demographics", + "icon": "tabler:users", + "color": "violet", + }, + { + "value": "amenities", + "label": "Amenities", + "icon": "tabler:trees", + "color": "green", + }, +] + + +def create_header() -> dmc.Group: + """Create the dashboard header with title and controls.""" + return dmc.Group( + [ + dmc.Stack( + [ + dmc.Title("Toronto Neighbourhood Dashboard", order=1), + dmc.Text( + "Explore livability across 158 Toronto neighbourhoods", + c="dimmed", + ), + ], + gap="xs", + ), + dmc.Group( + [ + dcc.Link( + dmc.Button( + "Methodology", + leftSection=DashIconify( + icon="tabler:info-circle", width=18 + ), + variant="subtle", + color="gray", + ), + href="/toronto/methodology", + ), + dmc.Select( + id="toronto-year-select", + data=[ + {"value": "2021", "label": "2021"}, + {"value": "2022", "label": "2022"}, + {"value": "2023", "label": "2023"}, + ], + value="2021", + label="Census Year", + size="sm", + w=120, + ), + ], + gap="md", + ), + ], + justify="space-between", + align="flex-start", + ) + + +def create_neighbourhood_selector() -> dmc.Paper: + """Create the neighbourhood search/select component.""" + return dmc.Paper( + dmc.Group( + [ + DashIconify(icon="tabler:search", width=20, color="gray"), + dmc.Select( + id="toronto-neighbourhood-select", + placeholder="Search neighbourhoods...", + searchable=True, + clearable=True, + data=[], # Populated by callback + style={"flex": 1}, + ), + dmc.Button( + "Compare", + id="toronto-compare-btn", + leftSection=DashIconify(icon="tabler:git-compare", width=16), + variant="light", + disabled=True, + ), + ], + gap="sm", + ), + p="sm", + radius="sm", + withBorder=True, + ) + + +def create_tab_navigation() -> dmc.Tabs: + """Create the tab navigation with icons.""" + return dmc.Tabs( + [ + dmc.TabsList( + [ + dmc.TabsTab( + dmc.Group( + [ + DashIconify(icon=tab["icon"], width=18), + dmc.Text(tab["label"], size="sm"), + ], + gap="xs", + ), + value=tab["value"], + ) + for tab in TAB_CONFIG + ], + grow=True, + ), + # Tab panels + dmc.TabsPanel(create_overview_tab(), value="overview", pt="md"), + dmc.TabsPanel(create_housing_tab(), value="housing", pt="md"), + dmc.TabsPanel(create_safety_tab(), value="safety", pt="md"), + dmc.TabsPanel(create_demographics_tab(), value="demographics", pt="md"), + dmc.TabsPanel(create_amenities_tab(), value="amenities", pt="md"), + ], + id="toronto-tabs", + value="overview", + variant="default", + ) + + +def create_data_notice() -> dmc.Alert: + """Create a notice about data sources.""" + return dmc.Alert( + children=[ + dmc.Text( + "Data from Toronto Open Data (Census 2021, Crime Statistics) and " + "CMHC Rental Market Reports. Click neighbourhoods on the map for details.", + size="sm", + ), + ], + title="Data Sources", + color="blue", + variant="light", + icon=DashIconify(icon="tabler:info-circle", width=20), + ) + + +# Store for selected neighbourhood +neighbourhood_store = dcc.Store(id="toronto-selected-neighbourhood", data=None) + +# Register callbacks +from portfolio_app.pages.toronto import callbacks # noqa: E402, F401 + +layout = dmc.Container( + dmc.Stack( + [ + neighbourhood_store, + create_header(), + create_data_notice(), + create_neighbourhood_selector(), + create_tab_navigation(), + dmc.Space(h=40), + ], + gap="lg", + ), + size="xl", + py="xl", +) diff --git a/portfolio_app/pages/toronto/methodology.py b/portfolio_app/pages/toronto/methodology.py new file mode 100644 index 0000000..9264619 --- /dev/null +++ b/portfolio_app/pages/toronto/methodology.py @@ -0,0 +1,215 @@ +"""Methodology page for Toronto Housing Dashboard.""" + +import dash +import dash_mantine_components as dmc +from dash import dcc, html +from dash_iconify import DashIconify + +dash.register_page( + __name__, + path="/toronto/methodology", + title="Methodology | Toronto Housing Dashboard", + description="Data sources, methodology, and limitations for the Toronto Housing Dashboard", +) + + +def layout() -> dmc.Container: + """Render the methodology page layout.""" + return dmc.Container( + size="md", + py="xl", + children=[ + # Back to Dashboard button + dcc.Link( + dmc.Button( + "Back to Dashboard", + leftSection=DashIconify(icon="tabler:arrow-left", width=18), + variant="subtle", + color="gray", + ), + href="/toronto", + ), + # Header + dmc.Title("Methodology", order=1, mb="lg", mt="md"), + dmc.Text( + "This page documents the data sources, processing methodology, " + "and known limitations of the Toronto Housing Dashboard.", + size="lg", + c="dimmed", + mb="xl", + ), + # Data Sources Section + dmc.Paper( + p="lg", + radius="md", + withBorder=True, + mb="lg", + children=[ + dmc.Title("Data Sources", order=2, mb="md"), + # CMHC + dmc.Title("Rental Data: CMHC", order=3, size="h4", mb="sm"), + dmc.Text( + [ + "Canada Mortgage and Housing Corporation (CMHC) conducts the annual ", + html.Strong("Rental Market Survey"), + " providing rental market statistics for major urban centres.", + ], + mb="sm", + ), + dmc.List( + [ + dmc.ListItem("Source: CMHC Rental Market Survey (Excel)"), + dmc.ListItem( + "Geographic granularity: ~20 CMHC Zones (Census Tract aligned)" + ), + dmc.ListItem( + "Temporal granularity: Annual (October survey)" + ), + dmc.ListItem("Coverage: 2021-present"), + dmc.ListItem( + [ + "Metrics: Average/median rent, vacancy rate, universe count, ", + "turnover rate, year-over-year rent change", + ] + ), + ], + mb="md", + ), + dmc.Anchor( + "CMHC Housing Market Information Portal", + href="https://www.cmhc-schl.gc.ca/professionals/housing-markets-data-and-research/housing-data/data-tables/rental-market", + target="_blank", + ), + ], + ), + # Geographic Considerations + dmc.Paper( + p="lg", + radius="md", + withBorder=True, + mb="lg", + children=[ + dmc.Title("Geographic Considerations", order=2, mb="md"), + dmc.Text( + "The dashboard presents two geographic layers:", + mb="sm", + ), + dmc.List( + [ + dmc.ListItem( + [ + html.Strong("City Neighbourhoods (158): "), + "Official City of Toronto neighbourhood boundaries, " + "used for neighbourhood-level analysis.", + ] + ), + dmc.ListItem( + [ + html.Strong("CMHC Zones (~20): "), + "Used for rental data visualization. " + "Zones are aligned with Census Tract boundaries.", + ] + ), + ], + ), + ], + ), + # Policy Events + dmc.Paper( + p="lg", + radius="md", + withBorder=True, + mb="lg", + children=[ + dmc.Title("Policy Event Annotations", order=2, mb="md"), + dmc.Text( + "The time series charts include markers for significant policy events " + "that may have influenced housing market conditions. These annotations are " + "for contextual reference only.", + mb="md", + ), + dmc.Alert( + title="No Causation Claims", + color="blue", + children=[ + "The presence of a policy marker near a market trend change does ", + html.Strong("not"), + " imply causation. Housing markets are influenced by numerous factors " + "beyond policy interventions.", + ], + ), + ], + ), + # Limitations + dmc.Paper( + p="lg", + radius="md", + withBorder=True, + mb="lg", + children=[ + dmc.Title("Limitations", order=2, mb="md"), + dmc.List( + [ + dmc.ListItem( + [ + html.Strong("Aggregate Data: "), + "All statistics are aggregates. Individual property characteristics, " + "condition, and micro-location are not reflected.", + ] + ), + dmc.ListItem( + [ + html.Strong("Reporting Lag: "), + "CMHC rental data is annual (October survey). " + "Other data sources may have different update frequencies.", + ] + ), + dmc.ListItem( + [ + html.Strong("Data Suppression: "), + "Some cells may be suppressed for confidentiality when counts " + "are below thresholds.", + ] + ), + ], + ), + ], + ), + # Technical Implementation + dmc.Paper( + p="lg", + radius="md", + withBorder=True, + children=[ + dmc.Title("Technical Implementation", order=2, mb="md"), + dmc.Text("This dashboard is built with:", mb="sm"), + dmc.List( + [ + dmc.ListItem("Python 3.11+ with Dash and Plotly"), + dmc.ListItem("PostgreSQL with PostGIS for geospatial data"), + dmc.ListItem("dbt for data transformation"), + dmc.ListItem("Pydantic for data validation"), + dmc.ListItem("SQLAlchemy 2.0 for database operations"), + ], + mb="md", + ), + dmc.Anchor( + "View source code on GitHub", + href="https://github.com/lmiranda/personal-portfolio", + target="_blank", + ), + ], + ), + # Back link + dmc.Group( + mt="xl", + children=[ + dmc.Anchor( + "← Back to Dashboard", + href="/toronto", + size="lg", + ), + ], + ), + ], + ) diff --git a/portfolio_app/pages/toronto/tabs/__init__.py b/portfolio_app/pages/toronto/tabs/__init__.py new file mode 100644 index 0000000..d68704e --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/__init__.py @@ -0,0 +1,15 @@ +"""Tab modules for Toronto Neighbourhood Dashboard.""" + +from .amenities import create_amenities_tab +from .demographics import create_demographics_tab +from .housing import create_housing_tab +from .overview import create_overview_tab +from .safety import create_safety_tab + +__all__ = [ + "create_overview_tab", + "create_housing_tab", + "create_safety_tab", + "create_demographics_tab", + "create_amenities_tab", +] diff --git a/portfolio_app/pages/toronto/tabs/amenities.py b/portfolio_app/pages/toronto/tabs/amenities.py new file mode 100644 index 0000000..49b0188 --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/amenities.py @@ -0,0 +1,207 @@ +"""Amenities tab for Toronto Neighbourhood Dashboard. + +Displays parks, schools, transit, and other amenity metrics. +""" + +import dash_mantine_components as dmc +from dash import dcc + + +def create_amenities_tab() -> dmc.Stack: + """Create the Amenities tab layout. + + Layout: + - Choropleth map (amenity score) | KPI cards + - Amenity breakdown chart | Amenity comparison radar + + Returns: + Tab content as a Mantine Stack component. + """ + return dmc.Stack( + [ + # Main content: Map + KPIs + dmc.Grid( + [ + # Choropleth map + dmc.GridCol( + dmc.Paper( + [ + dmc.Group( + [ + dmc.Title( + "Neighbourhood Amenities", + order=4, + size="h5", + ), + dmc.Select( + id="amenities-metric-select", + data=[ + { + "value": "amenity_score", + "label": "Amenity Score", + }, + { + "value": "parks_per_capita", + "label": "Parks per 1K", + }, + { + "value": "schools_per_capita", + "label": "Schools per 1K", + }, + { + "value": "transit_score", + "label": "Transit Score", + }, + ], + value="amenity_score", + size="sm", + w=180, + ), + ], + justify="space-between", + mb="sm", + ), + dcc.Graph( + id="amenities-choropleth", + config={ + "scrollZoom": True, + "displayModeBar": False, + }, + style={"height": "450px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "lg": 8}, + ), + # KPI cards + dmc.GridCol( + dmc.Stack( + [ + dmc.Paper( + [ + dmc.Text( + "City Amenity Score", size="xs", c="dimmed" + ), + dmc.Title( + id="amenities-city-score", + children="68", + order=2, + ), + dmc.Text( + "Out of 100", + size="sm", + c="dimmed", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text("Total Parks", size="xs", c="dimmed"), + dmc.Title( + id="amenities-total-parks", + children="1,500+", + order=2, + ), + dmc.Text( + id="amenities-park-area", + children="8,000+ hectares", + size="sm", + c="green", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Selected Neighbourhood", + size="xs", + c="dimmed", + ), + dmc.Title( + id="amenities-selected-name", + children="Click map to select", + order=4, + size="h5", + ), + dmc.Stack( + id="amenities-selected-details", + children=[ + dmc.Text("—", c="dimmed"), + ], + gap="xs", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + ], + gap="md", + ), + span={"base": 12, "lg": 4}, + ), + ], + gutter="md", + ), + # Supporting charts + dmc.Grid( + [ + # Amenity breakdown + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Amenity Breakdown", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="amenities-breakdown-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + # Amenity comparison radar + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Amenity Comparison", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="amenities-radar-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + ], + gutter="md", + ), + ], + gap="md", + ) diff --git a/portfolio_app/pages/toronto/tabs/demographics.py b/portfolio_app/pages/toronto/tabs/demographics.py new file mode 100644 index 0000000..5f087c5 --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/demographics.py @@ -0,0 +1,211 @@ +"""Demographics tab for Toronto Neighbourhood Dashboard. + +Displays population, income, age, and diversity metrics. +""" + +import dash_mantine_components as dmc +from dash import dcc + + +def create_demographics_tab() -> dmc.Stack: + """Create the Demographics tab layout. + + Layout: + - Choropleth map (demographic metric) | KPI cards + - Age distribution chart | Income distribution chart + + Returns: + Tab content as a Mantine Stack component. + """ + return dmc.Stack( + [ + # Main content: Map + KPIs + dmc.Grid( + [ + # Choropleth map + dmc.GridCol( + dmc.Paper( + [ + dmc.Group( + [ + dmc.Title( + "Neighbourhood Demographics", + order=4, + size="h5", + ), + dmc.Select( + id="demographics-metric-select", + data=[ + { + "value": "population", + "label": "Population", + }, + { + "value": "median_income", + "label": "Median Income", + }, + { + "value": "median_age", + "label": "Median Age", + }, + { + "value": "diversity_index", + "label": "Diversity Index", + }, + ], + value="population", + size="sm", + w=180, + ), + ], + justify="space-between", + mb="sm", + ), + dcc.Graph( + id="demographics-choropleth", + config={ + "scrollZoom": True, + "displayModeBar": False, + }, + style={"height": "450px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "lg": 8}, + ), + # KPI cards + dmc.GridCol( + dmc.Stack( + [ + dmc.Paper( + [ + dmc.Text( + "City Population", size="xs", c="dimmed" + ), + dmc.Title( + id="demographics-city-pop", + children="2.79M", + order=2, + ), + dmc.Text( + id="demographics-pop-change", + children="+2.3% since 2016", + size="sm", + c="green", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Median Household Income", + size="xs", + c="dimmed", + ), + dmc.Title( + id="demographics-city-income", + children="$84,000", + order=2, + ), + dmc.Text( + "City average", + size="sm", + c="dimmed", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Selected Neighbourhood", + size="xs", + c="dimmed", + ), + dmc.Title( + id="demographics-selected-name", + children="Click map to select", + order=4, + size="h5", + ), + dmc.Stack( + id="demographics-selected-details", + children=[ + dmc.Text("—", c="dimmed"), + ], + gap="xs", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + ], + gap="md", + ), + span={"base": 12, "lg": 4}, + ), + ], + gutter="md", + ), + # Supporting charts + dmc.Grid( + [ + # Age distribution + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Age Distribution", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="demographics-age-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + # Income distribution + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Income Distribution", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="demographics-income-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + ], + gutter="md", + ), + ], + gap="md", + ) diff --git a/portfolio_app/pages/toronto/tabs/housing.py b/portfolio_app/pages/toronto/tabs/housing.py new file mode 100644 index 0000000..b695edb --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/housing.py @@ -0,0 +1,209 @@ +"""Housing tab for Toronto Neighbourhood Dashboard. + +Displays affordability metrics, rent trends, and housing indicators. +""" + +import dash_mantine_components as dmc +from dash import dcc + + +def create_housing_tab() -> dmc.Stack: + """Create the Housing tab layout. + + Layout: + - Choropleth map (affordability index) | KPI cards + - Rent trend line chart | Dwelling types breakdown + + Returns: + Tab content as a Mantine Stack component. + """ + return dmc.Stack( + [ + # Main content: Map + KPIs + dmc.Grid( + [ + # Choropleth map + dmc.GridCol( + dmc.Paper( + [ + dmc.Group( + [ + dmc.Title( + "Housing Affordability", + order=4, + size="h5", + ), + dmc.Select( + id="housing-metric-select", + data=[ + { + "value": "affordability_index", + "label": "Affordability Index", + }, + { + "value": "avg_rent_2bed", + "label": "Avg Rent (2BR)", + }, + { + "value": "rent_to_income_pct", + "label": "Rent-to-Income %", + }, + { + "value": "vacancy_rate", + "label": "Vacancy Rate", + }, + ], + value="affordability_index", + size="sm", + w=180, + ), + ], + justify="space-between", + mb="sm", + ), + dcc.Graph( + id="housing-choropleth", + config={ + "scrollZoom": True, + "displayModeBar": False, + }, + style={"height": "450px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "lg": 8}, + ), + # KPI cards + dmc.GridCol( + dmc.Stack( + [ + dmc.Paper( + [ + dmc.Text( + "City Avg 2BR Rent", size="xs", c="dimmed" + ), + dmc.Title( + id="housing-city-rent", + children="$2,450", + order=2, + ), + dmc.Text( + id="housing-rent-change", + children="+4.2% YoY", + size="sm", + c="red", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "City Avg Vacancy", size="xs", c="dimmed" + ), + dmc.Title( + id="housing-city-vacancy", + children="1.8%", + order=2, + ), + dmc.Text( + "Below healthy rate (3%)", + size="sm", + c="orange", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Selected Neighbourhood", + size="xs", + c="dimmed", + ), + dmc.Title( + id="housing-selected-name", + children="Click map to select", + order=4, + size="h5", + ), + dmc.Stack( + id="housing-selected-details", + children=[ + dmc.Text("—", c="dimmed"), + ], + gap="xs", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + ], + gap="md", + ), + span={"base": 12, "lg": 4}, + ), + ], + gutter="md", + ), + # Supporting charts + dmc.Grid( + [ + # Rent trend + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Rent Trends (5 Year)", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="housing-trend-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + # Dwelling types + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Dwelling Types", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="housing-types-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + ], + gutter="md", + ), + ], + gap="md", + ) diff --git a/portfolio_app/pages/toronto/tabs/overview.py b/portfolio_app/pages/toronto/tabs/overview.py new file mode 100644 index 0000000..2d188d7 --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/overview.py @@ -0,0 +1,233 @@ +"""Overview tab for Toronto Neighbourhood Dashboard. + +Displays composite livability score with safety, affordability, and amenity components. +""" + +import dash_mantine_components as dmc +from dash import dcc, html + + +def create_overview_tab() -> dmc.Stack: + """Create the Overview tab layout. + + Layout: + - Choropleth map (livability score) | KPI cards + - Top/Bottom 10 bar chart | Income vs Crime scatter + + Returns: + Tab content as a Mantine Stack component. + """ + return dmc.Stack( + [ + # Main content: Map + KPIs + dmc.Grid( + [ + # Choropleth map + dmc.GridCol( + dmc.Paper( + [ + dmc.Group( + [ + dmc.Title( + "Neighbourhood Livability", + order=4, + size="h5", + ), + dmc.Select( + id="overview-metric-select", + data=[ + { + "value": "livability_score", + "label": "Livability Score", + }, + { + "value": "safety_score", + "label": "Safety Score", + }, + { + "value": "affordability_score", + "label": "Affordability Score", + }, + { + "value": "amenity_score", + "label": "Amenity Score", + }, + ], + value="livability_score", + size="sm", + w=180, + ), + ], + justify="space-between", + mb="sm", + ), + dcc.Graph( + id="overview-choropleth", + config={ + "scrollZoom": True, + "displayModeBar": False, + }, + style={"height": "450px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "lg": 8}, + ), + # KPI cards + dmc.GridCol( + dmc.Stack( + [ + dmc.Paper( + [ + dmc.Text("City Average", size="xs", c="dimmed"), + dmc.Title( + id="overview-city-avg", + children="72", + order=2, + ), + dmc.Text("Livability Score", size="sm", fw=500), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Selected Neighbourhood", + size="xs", + c="dimmed", + ), + dmc.Title( + id="overview-selected-name", + children="Click map to select", + order=4, + size="h5", + ), + html.Div( + id="overview-selected-scores", + children=[ + dmc.Text("—", c="dimmed"), + ], + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Score Components", size="xs", c="dimmed" + ), + dmc.Stack( + [ + dmc.Group( + [ + dmc.Text("Safety", size="sm"), + dmc.Text( + "30%", + size="sm", + c="dimmed", + ), + ], + justify="space-between", + ), + dmc.Group( + [ + dmc.Text( + "Affordability", size="sm" + ), + dmc.Text( + "40%", + size="sm", + c="dimmed", + ), + ], + justify="space-between", + ), + dmc.Group( + [ + dmc.Text( + "Amenities", size="sm" + ), + dmc.Text( + "30%", + size="sm", + c="dimmed", + ), + ], + justify="space-between", + ), + ], + gap="xs", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + ], + gap="md", + ), + span={"base": 12, "lg": 4}, + ), + ], + gutter="md", + ), + # Supporting charts + dmc.Grid( + [ + # Top/Bottom rankings + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Top & Bottom Neighbourhoods", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="overview-rankings-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + # Scatter plot + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Income vs Safety", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="overview-scatter-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + ], + gutter="md", + ), + ], + gap="md", + ) diff --git a/portfolio_app/pages/toronto/tabs/safety.py b/portfolio_app/pages/toronto/tabs/safety.py new file mode 100644 index 0000000..a84a5f6 --- /dev/null +++ b/portfolio_app/pages/toronto/tabs/safety.py @@ -0,0 +1,211 @@ +"""Safety tab for Toronto Neighbourhood Dashboard. + +Displays crime statistics, trends, and safety indicators. +""" + +import dash_mantine_components as dmc +from dash import dcc + + +def create_safety_tab() -> dmc.Stack: + """Create the Safety tab layout. + + Layout: + - Choropleth map (crime rate) | KPI cards + - Crime trend line chart | Crime by type breakdown + + Returns: + Tab content as a Mantine Stack component. + """ + return dmc.Stack( + [ + # Main content: Map + KPIs + dmc.Grid( + [ + # Choropleth map + dmc.GridCol( + dmc.Paper( + [ + dmc.Group( + [ + dmc.Title( + "Crime Rate by Neighbourhood", + order=4, + size="h5", + ), + dmc.Select( + id="safety-metric-select", + data=[ + { + "value": "total_crime_rate", + "label": "Total Crime Rate", + }, + { + "value": "violent_crime_rate", + "label": "Violent Crime", + }, + { + "value": "property_crime_rate", + "label": "Property Crime", + }, + { + "value": "theft_rate", + "label": "Theft", + }, + ], + value="total_crime_rate", + size="sm", + w=180, + ), + ], + justify="space-between", + mb="sm", + ), + dcc.Graph( + id="safety-choropleth", + config={ + "scrollZoom": True, + "displayModeBar": False, + }, + style={"height": "450px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "lg": 8}, + ), + # KPI cards + dmc.GridCol( + dmc.Stack( + [ + dmc.Paper( + [ + dmc.Text( + "City Crime Rate", size="xs", c="dimmed" + ), + dmc.Title( + id="safety-city-rate", + children="4,250", + order=2, + ), + dmc.Text( + id="safety-rate-change", + children="-2.1% YoY", + size="sm", + c="green", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Total Incidents (2023)", + size="xs", + c="dimmed", + ), + dmc.Title( + id="safety-total-incidents", + children="125,430", + order=2, + ), + dmc.Text( + "Per 100,000 residents", + size="sm", + c="dimmed", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + dmc.Paper( + [ + dmc.Text( + "Selected Neighbourhood", + size="xs", + c="dimmed", + ), + dmc.Title( + id="safety-selected-name", + children="Click map to select", + order=4, + size="h5", + ), + dmc.Stack( + id="safety-selected-details", + children=[ + dmc.Text("—", c="dimmed"), + ], + gap="xs", + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + ], + gap="md", + ), + span={"base": 12, "lg": 4}, + ), + ], + gutter="md", + ), + # Supporting charts + dmc.Grid( + [ + # Crime trend + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Crime Trends (5 Year)", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="safety-trend-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + # Crime by type + dmc.GridCol( + dmc.Paper( + [ + dmc.Title( + "Crime by Category", + order=4, + size="h5", + mb="sm", + ), + dcc.Graph( + id="safety-types-chart", + config={"displayModeBar": False}, + style={"height": "300px"}, + ), + ], + p="md", + radius="sm", + withBorder=True, + ), + span={"base": 12, "md": 6}, + ), + ], + gutter="md", + ), + ], + gap="md", + ) diff --git a/portfolio_app/toronto/__init__.py b/portfolio_app/toronto/__init__.py new file mode 100644 index 0000000..b28b8ac --- /dev/null +++ b/portfolio_app/toronto/__init__.py @@ -0,0 +1 @@ +"""Toronto housing data logic.""" diff --git a/portfolio_app/toronto/demo_data.py b/portfolio_app/toronto/demo_data.py new file mode 100644 index 0000000..27b56ba --- /dev/null +++ b/portfolio_app/toronto/demo_data.py @@ -0,0 +1,148 @@ +"""Demo/sample data for testing the Toronto Housing Dashboard without full pipeline. + +This module provides synthetic data for development and demonstration purposes. +Replace with real data from the database in production. +""" + +from datetime import date +from typing import Any + + +def get_demo_rental_data() -> list[dict[str, Any]]: + """Return sample rental data for visualization.""" + data = [] + + zones = [ + ("Zone01", "Downtown"), + ("Zone02", "Midtown"), + ("Zone03", "North York"), + ("Zone04", "Scarborough"), + ("Zone05", "Etobicoke"), + ] + + bedroom_types = ["bachelor", "1_bedroom", "2_bedroom", "3_bedroom"] + + base_rents = { + "bachelor": 1800, + "1_bedroom": 2200, + "2_bedroom": 2800, + "3_bedroom": 3400, + } + + for year in [2021, 2022, 2023, 2024, 2025]: + for zone_code, zone_name in zones: + for bedroom in bedroom_types: + # Rental trend: ~5% increase per year + year_factor = 1 + ((year - 2021) * 0.05) + base_rent = base_rents[bedroom] + + data.append( + { + "zone_code": zone_code, + "zone_name": zone_name, + "survey_year": year, + "full_date": date(year, 10, 1), + "bedroom_type": bedroom, + "average_rent": int(base_rent * year_factor), + "median_rent": int(base_rent * year_factor * 0.98), + "vacancy_rate": round( + 2.5 - (year - 2021) * 0.3, 1 + ), # Decreasing vacancy + "universe": 5000 + (year - 2021) * 200, + } + ) + + return data + + +def get_demo_policy_events() -> list[dict[str, Any]]: + """Return sample policy events for annotation.""" + return [ + { + "event_date": date(2024, 6, 5), + "effective_date": date(2024, 6, 5), + "level": "federal", + "category": "monetary", + "title": "BoC Rate Cut (25bp)", + "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.75%", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 7, 24), + "effective_date": date(2024, 7, 24), + "level": "federal", + "category": "monetary", + "title": "BoC Rate Cut (25bp)", + "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.50%", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 9, 4), + "effective_date": date(2024, 9, 4), + "level": "federal", + "category": "monetary", + "title": "BoC Rate Cut (25bp)", + "description": "Bank of Canada cuts overnight rate by 25 basis points to 4.25%", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 10, 23), + "effective_date": date(2024, 10, 23), + "level": "federal", + "category": "monetary", + "title": "BoC Rate Cut (50bp)", + "description": "Bank of Canada cuts overnight rate by 50 basis points to 3.75%", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 12, 11), + "effective_date": date(2024, 12, 11), + "level": "federal", + "category": "monetary", + "title": "BoC Rate Cut (50bp)", + "description": "Bank of Canada cuts overnight rate by 50 basis points to 3.25%", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 9, 16), + "effective_date": date(2024, 12, 15), + "level": "federal", + "category": "regulatory", + "title": "CMHC 30-Year Amortization", + "description": "30-year amortization extended to all first-time buyers and new builds", + "expected_direction": "bullish", + }, + { + "event_date": date(2024, 9, 16), + "effective_date": date(2024, 12, 15), + "level": "federal", + "category": "regulatory", + "title": "Insured Mortgage Cap $1.5M", + "description": "Insured mortgage cap raised from $1M to $1.5M", + "expected_direction": "bullish", + }, + ] + + +def get_demo_summary_metrics() -> dict[str, dict[str, Any]]: + """Return summary metrics for KPI cards.""" + return { + "avg_rent": { + "value": 2450, + "title": "Avg. Rent (2025)", + "delta": 3.2, + "delta_suffix": "%", + "prefix": "$", + "format_spec": ",.0f", + "positive_is_good": False, + }, + "vacancy_rate": { + "value": 1.8, + "title": "Vacancy Rate", + "delta": -0.4, + "delta_suffix": "pp", + "suffix": "%", + "format_spec": ".1f", + "positive_is_good": False, + }, + } diff --git a/portfolio_app/toronto/loaders/__init__.py b/portfolio_app/toronto/loaders/__init__.py new file mode 100644 index 0000000..d828308 --- /dev/null +++ b/portfolio_app/toronto/loaders/__init__.py @@ -0,0 +1,51 @@ +"""Database loaders for Toronto housing data.""" + +from .amenities import load_amenities, load_amenity_counts +from .base import bulk_insert, get_session, upsert_by_key +from .census import load_census_data +from .cmhc import ( + ensure_toronto_cma_zone, + load_cmhc_record, + load_cmhc_rentals, + load_statcan_cmhc_data, +) +from .cmhc_crosswalk import ( + build_cmhc_neighbourhood_crosswalk, + disaggregate_zone_value, + get_neighbourhood_weights_for_zone, +) +from .crime import load_crime_data +from .dimensions import ( + generate_date_key, + load_cmhc_zones, + load_neighbourhoods, + load_policy_events, + load_time_dimension, +) + +__all__ = [ + # Base utilities + "get_session", + "bulk_insert", + "upsert_by_key", + # Dimension loaders + "generate_date_key", + "load_time_dimension", + "load_cmhc_zones", + "load_neighbourhoods", + "load_policy_events", + # Fact loaders + "load_cmhc_rentals", + "load_cmhc_record", + "load_statcan_cmhc_data", + "ensure_toronto_cma_zone", + # Phase 3 loaders + "load_census_data", + "load_crime_data", + "load_amenities", + "load_amenity_counts", + # CMHC crosswalk + "build_cmhc_neighbourhood_crosswalk", + "get_neighbourhood_weights_for_zone", + "disaggregate_zone_value", +] diff --git a/portfolio_app/toronto/loaders/amenities.py b/portfolio_app/toronto/loaders/amenities.py new file mode 100644 index 0000000..db3e2b3 --- /dev/null +++ b/portfolio_app/toronto/loaders/amenities.py @@ -0,0 +1,93 @@ +"""Loader for amenities data to fact_amenities table.""" + +from collections import Counter + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import FactAmenities +from portfolio_app.toronto.schemas import AmenityCount, AmenityRecord + +from .base import get_session, upsert_by_key + + +def load_amenities( + records: list[AmenityRecord], + year: int, + session: Session | None = None, +) -> int: + """Load amenity records to fact_amenities table. + + Aggregates individual amenity records into counts by neighbourhood + and amenity type before loading. + + Args: + records: List of validated AmenityRecord schemas. + year: Year to associate with the amenity counts. + session: Optional existing session. + + Returns: + Number of records loaded (inserted + updated). + """ + # Aggregate records by neighbourhood and amenity type + counts: Counter[tuple[int, str]] = Counter() + for r in records: + key = (r.neighbourhood_id, r.amenity_type.value) + counts[key] += 1 + + # Convert to AmenityCount schemas then to models + def _load(sess: Session) -> int: + models = [] + for (neighbourhood_id, amenity_type), count in counts.items(): + model = FactAmenities( + neighbourhood_id=neighbourhood_id, + amenity_type=amenity_type, + count=count, + year=year, + ) + models.append(model) + + inserted, updated = upsert_by_key( + sess, FactAmenities, models, ["neighbourhood_id", "amenity_type", "year"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def load_amenity_counts( + records: list[AmenityCount], + session: Session | None = None, +) -> int: + """Load pre-aggregated amenity counts to fact_amenities table. + + Args: + records: List of validated AmenityCount schemas. + session: Optional existing session. + + Returns: + Number of records loaded (inserted + updated). + """ + + def _load(sess: Session) -> int: + models = [] + for r in records: + model = FactAmenities( + neighbourhood_id=r.neighbourhood_id, + amenity_type=r.amenity_type.value, + count=r.count, + year=r.year, + ) + models.append(model) + + inserted, updated = upsert_by_key( + sess, FactAmenities, models, ["neighbourhood_id", "amenity_type", "year"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) diff --git a/portfolio_app/toronto/loaders/base.py b/portfolio_app/toronto/loaders/base.py new file mode 100644 index 0000000..2ddaf1e --- /dev/null +++ b/portfolio_app/toronto/loaders/base.py @@ -0,0 +1,85 @@ +"""Base loader utilities for database operations.""" + +from collections.abc import Generator +from contextlib import contextmanager +from typing import Any, TypeVar + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import get_session_factory + +T = TypeVar("T") + + +@contextmanager +def get_session() -> Generator[Session, None, None]: + """Get a database session with automatic cleanup. + + Yields: + SQLAlchemy session that auto-commits on success, rollbacks on error. + """ + session_factory = get_session_factory() + session = session_factory() + try: + yield session + session.commit() + except Exception: + session.rollback() + raise + finally: + session.close() + + +def bulk_insert(session: Session, objects: list[T]) -> int: + """Bulk insert objects into the database. + + Args: + session: Active SQLAlchemy session. + objects: List of ORM model instances to insert. + + Returns: + Number of objects inserted. + """ + session.add_all(objects) + session.flush() + return len(objects) + + +def upsert_by_key( + session: Session, + model_class: Any, + objects: list[T], + key_columns: list[str], +) -> tuple[int, int]: + """Upsert objects based on unique key columns. + + Args: + session: Active SQLAlchemy session. + model_class: The ORM model class. + objects: List of ORM model instances to upsert. + key_columns: Column names that form the unique key. + + Returns: + Tuple of (inserted_count, updated_count). + """ + inserted = 0 + updated = 0 + + for obj in objects: + # Build filter for existing record + filters = {col: getattr(obj, col) for col in key_columns} + existing = session.query(model_class).filter_by(**filters).first() + + if existing: + # Update existing record + for column in model_class.__table__.columns: + if column.name not in key_columns and column.name != "id": + setattr(existing, column.name, getattr(obj, column.name)) + updated += 1 + else: + # Insert new record + session.add(obj) + inserted += 1 + + session.flush() + return inserted, updated diff --git a/portfolio_app/toronto/loaders/census.py b/portfolio_app/toronto/loaders/census.py new file mode 100644 index 0000000..afcb0b4 --- /dev/null +++ b/portfolio_app/toronto/loaders/census.py @@ -0,0 +1,68 @@ +"""Loader for census data to fact_census table.""" + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import FactCensus +from portfolio_app.toronto.schemas import CensusRecord + +from .base import get_session, upsert_by_key + + +def load_census_data( + records: list[CensusRecord], + session: Session | None = None, +) -> int: + """Load census records to fact_census table. + + Args: + records: List of validated CensusRecord schemas. + session: Optional existing session. + + Returns: + Number of records loaded (inserted + updated). + """ + + def _load(sess: Session) -> int: + models = [] + for r in records: + model = FactCensus( + neighbourhood_id=r.neighbourhood_id, + census_year=r.census_year, + population=r.population, + population_density=float(r.population_density) + if r.population_density + else None, + median_household_income=float(r.median_household_income) + if r.median_household_income + else None, + average_household_income=float(r.average_household_income) + if r.average_household_income + else None, + unemployment_rate=float(r.unemployment_rate) + if r.unemployment_rate + else None, + pct_bachelors_or_higher=float(r.pct_bachelors_or_higher) + if r.pct_bachelors_or_higher + else None, + pct_owner_occupied=float(r.pct_owner_occupied) + if r.pct_owner_occupied + else None, + pct_renter_occupied=float(r.pct_renter_occupied) + if r.pct_renter_occupied + else None, + median_age=float(r.median_age) if r.median_age else None, + average_dwelling_value=float(r.average_dwelling_value) + if r.average_dwelling_value + else None, + ) + models.append(model) + + inserted, updated = upsert_by_key( + sess, FactCensus, models, ["neighbourhood_id", "census_year"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) diff --git a/portfolio_app/toronto/loaders/cmhc.py b/portfolio_app/toronto/loaders/cmhc.py new file mode 100644 index 0000000..fe31535 --- /dev/null +++ b/portfolio_app/toronto/loaders/cmhc.py @@ -0,0 +1,261 @@ +"""Loader for CMHC rental data into fact_rentals.""" + +import logging +from datetime import date +from typing import Any + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import DimCMHCZone, DimTime, FactRentals +from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord + +from .base import get_session, upsert_by_key +from .dimensions import generate_date_key + +logger = logging.getLogger(__name__) + +# Toronto CMA zone code for CMA-level data +TORONTO_CMA_ZONE_CODE = "TORCMA" +TORONTO_CMA_ZONE_NAME = "Toronto CMA" + + +def load_cmhc_rentals( + survey: CMHCAnnualSurvey, + session: Session | None = None, +) -> int: + """Load CMHC annual survey data into fact_rentals. + + Args: + survey: Validated CMHC annual survey containing records. + session: Optional existing session. + + Returns: + Number of records loaded. + """ + from datetime import date + + def _load(sess: Session) -> int: + # Get zone key mapping + zones = sess.query(DimCMHCZone).all() + zone_map = {z.zone_code: z.zone_key for z in zones} + + # CMHC surveys are annual - use October 1st as reference date + survey_date = date(survey.survey_year, 10, 1) + date_key = generate_date_key(survey_date) + + # Verify time dimension exists + time_dim = sess.query(DimTime).filter_by(date_key=date_key).first() + if not time_dim: + raise ValueError( + f"Time dimension not found for date_key {date_key}. " + "Load time dimension first." + ) + + records = [] + for record in survey.records: + zone_key = zone_map.get(record.zone_code) + if not zone_key: + # Skip records for unknown zones + continue + + fact = FactRentals( + date_key=date_key, + zone_key=zone_key, + bedroom_type=record.bedroom_type.value, + universe=record.universe, + avg_rent=record.average_rent, + median_rent=record.median_rent, + vacancy_rate=record.vacancy_rate, + availability_rate=record.availability_rate, + turnover_rate=record.turnover_rate, + rent_change_pct=record.rent_change_pct, + reliability_code=record.average_rent_reliability.value + if record.average_rent_reliability + else None, + ) + records.append(fact) + + inserted, updated = upsert_by_key( + sess, FactRentals, records, ["date_key", "zone_key", "bedroom_type"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def load_cmhc_record( + record: CMHCRentalRecord, + survey_year: int, + session: Session | None = None, +) -> int: + """Load a single CMHC record into fact_rentals. + + Args: + record: Single validated CMHC rental record. + survey_year: Year of the survey. + session: Optional existing session. + + Returns: + Number of records loaded (0 or 1). + """ + from datetime import date + + def _load(sess: Session) -> int: + # Get zone key + zone = sess.query(DimCMHCZone).filter_by(zone_code=record.zone_code).first() + if not zone: + return 0 + + survey_date = date(survey_year, 10, 1) + date_key = generate_date_key(survey_date) + + # Verify time dimension exists + time_dim = sess.query(DimTime).filter_by(date_key=date_key).first() + if not time_dim: + raise ValueError( + f"Time dimension not found for date_key {date_key}. " + "Load time dimension first." + ) + + fact = FactRentals( + date_key=date_key, + zone_key=zone.zone_key, + bedroom_type=record.bedroom_type.value, + universe=record.universe, + avg_rent=record.average_rent, + median_rent=record.median_rent, + vacancy_rate=record.vacancy_rate, + availability_rate=record.availability_rate, + turnover_rate=record.turnover_rate, + rent_change_pct=record.rent_change_pct, + reliability_code=record.average_rent_reliability.value + if record.average_rent_reliability + else None, + ) + + inserted, updated = upsert_by_key( + sess, FactRentals, [fact], ["date_key", "zone_key", "bedroom_type"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def ensure_toronto_cma_zone(session: Session | None = None) -> int: + """Ensure Toronto CMA zone exists in dim_cmhc_zone. + + Creates the zone if it doesn't exist. + + Args: + session: Optional existing session. + + Returns: + The zone_key for Toronto CMA. + """ + + def _ensure(sess: Session) -> int: + zone = ( + sess.query(DimCMHCZone).filter_by(zone_code=TORONTO_CMA_ZONE_CODE).first() + ) + if zone: + return int(zone.zone_key) + + # Create new zone + new_zone = DimCMHCZone( + zone_code=TORONTO_CMA_ZONE_CODE, + zone_name=TORONTO_CMA_ZONE_NAME, + geometry=None, # CMA-level doesn't need geometry + ) + sess.add(new_zone) + sess.flush() + logger.info(f"Created Toronto CMA zone with zone_key={new_zone.zone_key}") + return int(new_zone.zone_key) + + if session: + return _ensure(session) + with get_session() as sess: + result = _ensure(sess) + sess.commit() + return result + + +def load_statcan_cmhc_data( + records: list[Any], # List of CMHCRentalRecord from statcan_cmhc parser + session: Session | None = None, +) -> int: + """Load CMHC rental data from StatCan parser into fact_rentals. + + This function handles CMA-level data from the StatCan API, which provides + aggregate Toronto data rather than zone-level HMIP data. + + Args: + records: List of CMHCRentalRecord dataclass instances from statcan_cmhc parser. + session: Optional existing session. + + Returns: + Number of records loaded. + """ + from portfolio_app.toronto.parsers.statcan_cmhc import ( + CMHCRentalRecord as StatCanRecord, + ) + + def _load(sess: Session) -> int: + # Ensure Toronto CMA zone exists + zone_key = ensure_toronto_cma_zone(sess) + + loaded = 0 + for record in records: + if not isinstance(record, StatCanRecord): + logger.warning(f"Skipping invalid record type: {type(record)}") + continue + + # Generate date key for this record's survey date + survey_date = date(record.year, record.month, 1) + date_key = generate_date_key(survey_date) + + # Verify time dimension exists + time_dim = sess.query(DimTime).filter_by(date_key=date_key).first() + if not time_dim: + logger.warning( + f"Time dimension not found for {survey_date}, skipping record" + ) + continue + + # Create fact record + fact = FactRentals( + date_key=date_key, + zone_key=zone_key, + bedroom_type=record.bedroom_type, + universe=record.universe, + avg_rent=float(record.avg_rent) if record.avg_rent else None, + median_rent=None, # StatCan doesn't provide median + vacancy_rate=float(record.vacancy_rate) + if record.vacancy_rate + else None, + availability_rate=None, + turnover_rate=None, + rent_change_pct=None, + reliability_code=None, + ) + + # Upsert + inserted, updated = upsert_by_key( + sess, FactRentals, [fact], ["date_key", "zone_key", "bedroom_type"] + ) + loaded += inserted + updated + + logger.info(f"Loaded {loaded} CMHC rental records from StatCan") + return loaded + + if session: + return _load(session) + with get_session() as sess: + result = _load(sess) + sess.commit() + return result diff --git a/portfolio_app/toronto/loaders/cmhc_crosswalk.py b/portfolio_app/toronto/loaders/cmhc_crosswalk.py new file mode 100644 index 0000000..b856d52 --- /dev/null +++ b/portfolio_app/toronto/loaders/cmhc_crosswalk.py @@ -0,0 +1,131 @@ +"""Loader for CMHC zone to neighbourhood crosswalk with area weights.""" + +from sqlalchemy import text +from sqlalchemy.orm import Session + +from .base import get_session + + +def build_cmhc_neighbourhood_crosswalk( + session: Session | None = None, +) -> int: + """Calculate area overlap weights between CMHC zones and neighbourhoods. + + Uses PostGIS ST_Intersection and ST_Area functions to compute the + proportion of each CMHC zone that overlaps with each neighbourhood. + This enables disaggregation of CMHC zone-level data to neighbourhood level. + + The function is idempotent - it clears existing crosswalk data before + rebuilding. + + Args: + session: Optional existing session. + + Returns: + Number of bridge records created. + + Note: + Requires both dim_cmhc_zone and dim_neighbourhood tables to have + geometry columns populated with valid PostGIS geometries. + """ + + def _build(sess: Session) -> int: + # Clear existing crosswalk data + sess.execute(text("DELETE FROM bridge_cmhc_neighbourhood")) + + # Calculate overlap weights using PostGIS + # Weight = area of intersection / total area of CMHC zone + crosswalk_query = text( + """ + INSERT INTO bridge_cmhc_neighbourhood (cmhc_zone_code, neighbourhood_id, weight) + SELECT + z.zone_code, + n.neighbourhood_id, + CASE + WHEN ST_Area(z.geometry::geography) > 0 THEN + ST_Area(ST_Intersection(z.geometry, n.geometry)::geography) / + ST_Area(z.geometry::geography) + ELSE 0 + END as weight + FROM dim_cmhc_zone z + JOIN dim_neighbourhood n + ON ST_Intersects(z.geometry, n.geometry) + WHERE + z.geometry IS NOT NULL + AND n.geometry IS NOT NULL + AND ST_Area(ST_Intersection(z.geometry, n.geometry)::geography) > 0 + """ + ) + + sess.execute(crosswalk_query) + + # Count records created + count_result = sess.execute( + text("SELECT COUNT(*) FROM bridge_cmhc_neighbourhood") + ) + count = count_result.scalar() or 0 + + return int(count) + + if session: + return _build(session) + with get_session() as sess: + return _build(sess) + + +def get_neighbourhood_weights_for_zone( + zone_code: str, + session: Session | None = None, +) -> list[tuple[int, float]]: + """Get neighbourhood weights for a specific CMHC zone. + + Args: + zone_code: CMHC zone code. + session: Optional existing session. + + Returns: + List of (neighbourhood_id, weight) tuples. + """ + + def _get(sess: Session) -> list[tuple[int, float]]: + result = sess.execute( + text( + """ + SELECT neighbourhood_id, weight + FROM bridge_cmhc_neighbourhood + WHERE cmhc_zone_code = :zone_code + ORDER BY weight DESC + """ + ), + {"zone_code": zone_code}, + ) + return [(int(row[0]), float(row[1])) for row in result] + + if session: + return _get(session) + with get_session() as sess: + return _get(sess) + + +def disaggregate_zone_value( + zone_code: str, + value: float, + session: Session | None = None, +) -> dict[int, float]: + """Disaggregate a CMHC zone value to neighbourhoods using weights. + + Args: + zone_code: CMHC zone code. + value: Value to disaggregate (e.g., average rent). + session: Optional existing session. + + Returns: + Dictionary mapping neighbourhood_id to weighted value. + + Note: + For averages (like rent), the weighted value represents the + contribution from this zone. To get a neighbourhood's total, + sum contributions from all overlapping zones. + """ + weights = get_neighbourhood_weights_for_zone(zone_code, session) + return {neighbourhood_id: value * weight for neighbourhood_id, weight in weights} diff --git a/portfolio_app/toronto/loaders/crime.py b/portfolio_app/toronto/loaders/crime.py new file mode 100644 index 0000000..29c4522 --- /dev/null +++ b/portfolio_app/toronto/loaders/crime.py @@ -0,0 +1,45 @@ +"""Loader for crime data to fact_crime table.""" + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import FactCrime +from portfolio_app.toronto.schemas import CrimeRecord + +from .base import get_session, upsert_by_key + + +def load_crime_data( + records: list[CrimeRecord], + session: Session | None = None, +) -> int: + """Load crime records to fact_crime table. + + Args: + records: List of validated CrimeRecord schemas. + session: Optional existing session. + + Returns: + Number of records loaded (inserted + updated). + """ + + def _load(sess: Session) -> int: + models = [] + for r in records: + model = FactCrime( + neighbourhood_id=r.neighbourhood_id, + year=r.year, + crime_type=r.crime_type.value, + count=r.count, + rate_per_100k=float(r.rate_per_100k) if r.rate_per_100k else None, + ) + models.append(model) + + inserted, updated = upsert_by_key( + sess, FactCrime, models, ["neighbourhood_id", "year", "crime_type"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) diff --git a/portfolio_app/toronto/loaders/dimensions.py b/portfolio_app/toronto/loaders/dimensions.py new file mode 100644 index 0000000..092fd05 --- /dev/null +++ b/portfolio_app/toronto/loaders/dimensions.py @@ -0,0 +1,213 @@ +"""Loaders for dimension tables.""" + +from datetime import date + +from sqlalchemy.orm import Session + +from portfolio_app.toronto.models import ( + DimCMHCZone, + DimNeighbourhood, + DimPolicyEvent, + DimTime, +) +from portfolio_app.toronto.schemas import ( + CMHCZone, + Neighbourhood, + PolicyEvent, +) + +from .base import get_session, upsert_by_key + + +def generate_date_key(d: date) -> int: + """Generate integer date key from date (YYYYMMDD format). + + Args: + d: Date to convert. + + Returns: + Integer in YYYYMMDD format. + """ + return d.year * 10000 + d.month * 100 + d.day + + +def load_time_dimension( + start_date: date, + end_date: date, + session: Session | None = None, +) -> int: + """Load time dimension with date range. + + Args: + start_date: Start of date range. + end_date: End of date range (inclusive). + session: Optional existing session. + + Returns: + Number of records loaded. + """ + + month_names = [ + "", + "January", + "February", + "March", + "April", + "May", + "June", + "July", + "August", + "September", + "October", + "November", + "December", + ] + + def _load(sess: Session) -> int: + records = [] + current = start_date.replace(day=1) # Start at month beginning + + while current <= end_date: + quarter = (current.month - 1) // 3 + 1 + dim = DimTime( + date_key=generate_date_key(current), + full_date=current, + year=current.year, + month=current.month, + quarter=quarter, + month_name=month_names[current.month], + is_month_start=True, + ) + records.append(dim) + + # Move to next month + if current.month == 12: + current = current.replace(year=current.year + 1, month=1) + else: + current = current.replace(month=current.month + 1) + + inserted, updated = upsert_by_key(sess, DimTime, records, ["date_key"]) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def load_cmhc_zones( + zones: list[CMHCZone], + session: Session | None = None, +) -> int: + """Load CMHC zone dimension. + + Args: + zones: List of validated zone schemas. + session: Optional existing session. + + Returns: + Number of records loaded. + """ + + def _load(sess: Session) -> int: + records = [] + for z in zones: + dim = DimCMHCZone( + zone_code=z.zone_code, + zone_name=z.zone_name, + geometry=z.geometry_wkt, + ) + records.append(dim) + + inserted, updated = upsert_by_key(sess, DimCMHCZone, records, ["zone_code"]) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def load_neighbourhoods( + neighbourhoods: list[Neighbourhood], + session: Session | None = None, +) -> int: + """Load neighbourhood dimension. + + Args: + neighbourhoods: List of validated neighbourhood schemas. + session: Optional existing session. + + Returns: + Number of records loaded. + """ + + def _load(sess: Session) -> int: + records = [] + for n in neighbourhoods: + dim = DimNeighbourhood( + neighbourhood_id=n.neighbourhood_id, + name=n.name, + geometry=n.geometry_wkt, + population=n.population, + land_area_sqkm=n.land_area_sqkm, + pop_density_per_sqkm=n.pop_density_per_sqkm, + pct_bachelors_or_higher=n.pct_bachelors_or_higher, + median_household_income=n.median_household_income, + pct_owner_occupied=n.pct_owner_occupied, + pct_renter_occupied=n.pct_renter_occupied, + census_year=n.census_year, + ) + records.append(dim) + + inserted, updated = upsert_by_key( + sess, DimNeighbourhood, records, ["neighbourhood_id"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) + + +def load_policy_events( + events: list[PolicyEvent], + session: Session | None = None, +) -> int: + """Load policy event dimension. + + Args: + events: List of validated policy event schemas. + session: Optional existing session. + + Returns: + Number of records loaded. + """ + + def _load(sess: Session) -> int: + records = [] + for e in events: + dim = DimPolicyEvent( + event_date=e.event_date, + effective_date=e.effective_date, + level=e.level.value, + category=e.category.value, + title=e.title, + description=e.description, + expected_direction=e.expected_direction.value, + source_url=e.source_url, + confidence=e.confidence.value, + ) + records.append(dim) + + # For policy events, use event_date + title as unique key + inserted, updated = upsert_by_key( + sess, DimPolicyEvent, records, ["event_date", "title"] + ) + return inserted + updated + + if session: + return _load(session) + with get_session() as sess: + return _load(sess) diff --git a/portfolio_app/toronto/models/__init__.py b/portfolio_app/toronto/models/__init__.py new file mode 100644 index 0000000..3fb386c --- /dev/null +++ b/portfolio_app/toronto/models/__init__.py @@ -0,0 +1,36 @@ +"""SQLAlchemy models for Toronto housing data.""" + +from .base import Base, create_tables, get_engine, get_session_factory +from .dimensions import ( + DimCMHCZone, + DimNeighbourhood, + DimPolicyEvent, + DimTime, +) +from .facts import ( + BridgeCMHCNeighbourhood, + FactAmenities, + FactCensus, + FactCrime, + FactRentals, +) + +__all__ = [ + # Base + "Base", + "get_engine", + "get_session_factory", + "create_tables", + # Dimensions + "DimTime", + "DimCMHCZone", + "DimNeighbourhood", + "DimPolicyEvent", + # Facts + "FactRentals", + "FactCensus", + "FactCrime", + "FactAmenities", + # Bridge tables + "BridgeCMHCNeighbourhood", +] diff --git a/portfolio_app/toronto/models/base.py b/portfolio_app/toronto/models/base.py new file mode 100644 index 0000000..7998726 --- /dev/null +++ b/portfolio_app/toronto/models/base.py @@ -0,0 +1,30 @@ +"""SQLAlchemy base configuration and engine setup.""" + +from sqlalchemy import Engine, create_engine +from sqlalchemy.orm import DeclarativeBase, Session, sessionmaker + +from portfolio_app.config import get_settings + + +class Base(DeclarativeBase): # type: ignore[misc] + """Base class for all SQLAlchemy models.""" + + pass + + +def get_engine() -> Engine: + """Create database engine from settings.""" + settings = get_settings() + return create_engine(settings.database_url, echo=False) + + +def get_session_factory() -> sessionmaker[Session]: + """Create session factory.""" + engine = get_engine() + return sessionmaker(bind=engine) + + +def create_tables() -> None: + """Create all tables in database.""" + engine = get_engine() + Base.metadata.create_all(engine) diff --git a/portfolio_app/toronto/models/dimensions.py b/portfolio_app/toronto/models/dimensions.py new file mode 100644 index 0000000..7e86265 --- /dev/null +++ b/portfolio_app/toronto/models/dimensions.py @@ -0,0 +1,90 @@ +"""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 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 diff --git a/portfolio_app/toronto/models/facts.py b/portfolio_app/toronto/models/facts.py new file mode 100644 index 0000000..2f1d5bd --- /dev/null +++ b/portfolio_app/toronto/models/facts.py @@ -0,0 +1,144 @@ +"""SQLAlchemy models for fact tables.""" + +from sqlalchemy import ForeignKey, Index, Integer, Numeric, String +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from .base import Base + + +class BridgeCMHCNeighbourhood(Base): + """Bridge table for CMHC zone to neighbourhood mapping with area weights. + + Enables disaggregation of CMHC zone-level rental data to neighbourhood level + using area-based proportional weights computed via PostGIS. + """ + + __tablename__ = "bridge_cmhc_neighbourhood" + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + cmhc_zone_code: Mapped[str] = mapped_column(String(10), nullable=False) + neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False) + weight: Mapped[float] = mapped_column( + Numeric(5, 4), nullable=False + ) # 0.0000 to 1.0000 + + __table_args__ = ( + Index("ix_bridge_cmhc_zone", "cmhc_zone_code"), + Index("ix_bridge_neighbourhood", "neighbourhood_id"), + ) + + +class FactCensus(Base): + """Census statistics by neighbourhood and year. + + Grain: One row per neighbourhood per census year. + """ + + __tablename__ = "fact_census" + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False) + census_year: Mapped[int] = mapped_column(Integer, nullable=False) + population: Mapped[int | None] = mapped_column(Integer, nullable=True) + population_density: Mapped[float | None] = mapped_column( + Numeric(10, 2), nullable=True + ) + median_household_income: Mapped[float | None] = mapped_column( + Numeric(12, 2), nullable=True + ) + average_household_income: Mapped[float | None] = mapped_column( + Numeric(12, 2), nullable=True + ) + unemployment_rate: Mapped[float | None] = mapped_column( + Numeric(5, 2), nullable=True + ) + pct_bachelors_or_higher: Mapped[float | None] = mapped_column( + Numeric(5, 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 + ) + median_age: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True) + average_dwelling_value: Mapped[float | None] = mapped_column( + Numeric(12, 2), nullable=True + ) + + __table_args__ = ( + Index("ix_fact_census_neighbourhood_year", "neighbourhood_id", "census_year"), + ) + + +class FactCrime(Base): + """Crime statistics by neighbourhood and year. + + Grain: One row per neighbourhood per year per crime type. + """ + + __tablename__ = "fact_crime" + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False) + year: Mapped[int] = mapped_column(Integer, nullable=False) + crime_type: Mapped[str] = mapped_column(String(50), nullable=False) + count: Mapped[int] = mapped_column(Integer, nullable=False) + rate_per_100k: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True) + + __table_args__ = ( + Index("ix_fact_crime_neighbourhood_year", "neighbourhood_id", "year"), + Index("ix_fact_crime_type", "crime_type"), + ) + + +class FactAmenities(Base): + """Amenity counts by neighbourhood. + + Grain: One row per neighbourhood per amenity type per year. + """ + + __tablename__ = "fact_amenities" + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + neighbourhood_id: Mapped[int] = mapped_column(Integer, nullable=False) + amenity_type: Mapped[str] = mapped_column(String(50), nullable=False) + count: Mapped[int] = mapped_column(Integer, nullable=False) + year: Mapped[int] = mapped_column(Integer, nullable=False) + + __table_args__ = ( + Index("ix_fact_amenities_neighbourhood_year", "neighbourhood_id", "year"), + Index("ix_fact_amenities_type", "amenity_type"), + ) + + +class FactRentals(Base): + """Fact table for CMHC rental market data. + + Grain: One row per zone per bedroom type per survey year. + """ + + __tablename__ = "fact_rentals" + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + date_key: Mapped[int] = mapped_column( + Integer, ForeignKey("dim_time.date_key"), nullable=False + ) + zone_key: Mapped[int] = mapped_column( + Integer, ForeignKey("dim_cmhc_zone.zone_key"), nullable=False + ) + bedroom_type: Mapped[str] = mapped_column(String(20), nullable=False) + universe: Mapped[int | None] = mapped_column(Integer, nullable=True) + avg_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True) + median_rent: Mapped[float | None] = mapped_column(Numeric(10, 2), nullable=True) + vacancy_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True) + availability_rate: Mapped[float | None] = mapped_column( + Numeric(5, 2), nullable=True + ) + turnover_rate: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True) + rent_change_pct: Mapped[float | None] = mapped_column(Numeric(5, 2), nullable=True) + reliability_code: Mapped[str | None] = mapped_column(String(2), nullable=True) + + # Relationships + time = relationship("DimTime", backref="rentals") + zone = relationship("DimCMHCZone", backref="rentals") diff --git a/portfolio_app/toronto/parsers/__init__.py b/portfolio_app/toronto/parsers/__init__.py new file mode 100644 index 0000000..50b4b39 --- /dev/null +++ b/portfolio_app/toronto/parsers/__init__.py @@ -0,0 +1,21 @@ +"""Parsers for Toronto housing data sources.""" + +from .cmhc import CMHCParser +from .geo import ( + CMHCZoneParser, + NeighbourhoodParser, + load_geojson, +) +from .toronto_open_data import TorontoOpenDataParser +from .toronto_police import TorontoPoliceParser + +__all__ = [ + "CMHCParser", + # GeoJSON parsers + "CMHCZoneParser", + "NeighbourhoodParser", + "load_geojson", + # API parsers (Phase 3) + "TorontoOpenDataParser", + "TorontoPoliceParser", +] diff --git a/portfolio_app/toronto/parsers/cmhc.py b/portfolio_app/toronto/parsers/cmhc.py new file mode 100644 index 0000000..52ac3ad --- /dev/null +++ b/portfolio_app/toronto/parsers/cmhc.py @@ -0,0 +1,147 @@ +"""CMHC CSV processor for rental market survey data. + +This module provides the structure for processing CMHC (Canada Mortgage and Housing +Corporation) rental market survey data from CSV exports. +""" + +from pathlib import Path +from typing import Any, cast + +import pandas as pd + +from portfolio_app.toronto.schemas import CMHCAnnualSurvey, CMHCRentalRecord + + +class CMHCParser: + """Parser for CMHC Rental Market Survey CSV data. + + CMHC conducts annual rental market surveys and publishes data including: + - Average and median rents by zone and bedroom type + - Vacancy rates + - Universe (total rental units) + - Year-over-year rent changes + + Data is available via the Housing Market Information Portal as CSV exports. + """ + + # Expected columns in CMHC CSV exports + REQUIRED_COLUMNS = { + "zone_code", + "zone_name", + "bedroom_type", + "survey_year", + } + + # Column name mappings from CMHC export format + COLUMN_MAPPINGS = { + "Zone Code": "zone_code", + "Zone Name": "zone_name", + "Bedroom Type": "bedroom_type", + "Survey Year": "survey_year", + "Universe": "universe", + "Average Rent ($)": "avg_rent", + "Median Rent ($)": "median_rent", + "Vacancy Rate (%)": "vacancy_rate", + "Availability Rate (%)": "availability_rate", + "Turnover Rate (%)": "turnover_rate", + "% Change in Rent": "rent_change_pct", + "Reliability Code": "reliability_code", + } + + def __init__(self, csv_path: Path) -> None: + """Initialize parser with path to CSV file. + + Args: + csv_path: Path to the CMHC CSV export file. + """ + self.csv_path = csv_path + self._validate_path() + + def _validate_path(self) -> None: + """Validate that the CSV path exists and is readable.""" + if not self.csv_path.exists(): + raise FileNotFoundError(f"CSV not found: {self.csv_path}") + if not self.csv_path.suffix.lower() == ".csv": + raise ValueError(f"Expected CSV file, got: {self.csv_path.suffix}") + + def parse(self) -> CMHCAnnualSurvey: + """Parse the CSV and return structured data. + + Returns: + CMHCAnnualSurvey containing all extracted records. + + Raises: + ValueError: If required columns are missing. + """ + df = self._load_csv() + df = self._normalize_columns(df) + self._validate_columns(df) + records = self._convert_to_records(df) + survey_year = self._infer_survey_year(df) + + return CMHCAnnualSurvey(survey_year=survey_year, records=records) + + def _load_csv(self) -> pd.DataFrame: + """Load CSV file into DataFrame. + + Returns: + Raw DataFrame from CSV. + """ + return pd.read_csv(self.csv_path) + + def _normalize_columns(self, df: pd.DataFrame) -> pd.DataFrame: + """Normalize column names to standard format. + + Args: + df: DataFrame with original column names. + + Returns: + DataFrame with normalized column names. + """ + rename_map = {k: v for k, v in self.COLUMN_MAPPINGS.items() if k in df.columns} + return df.rename(columns=rename_map) + + def _validate_columns(self, df: pd.DataFrame) -> None: + """Validate that all required columns are present. + + Args: + df: DataFrame to validate. + + Raises: + ValueError: If required columns are missing. + """ + missing = self.REQUIRED_COLUMNS - set(df.columns) + if missing: + raise ValueError(f"Missing required columns: {missing}") + + def _convert_to_records(self, df: pd.DataFrame) -> list[CMHCRentalRecord]: + """Convert DataFrame rows to validated schema records. + + Args: + df: Normalized DataFrame. + + Returns: + List of validated CMHCRentalRecord objects. + """ + records = [] + for _, row in df.iterrows(): + record_data = row.to_dict() + # Handle NaN values + record_data = { + k: (None if pd.isna(v) else v) for k, v in record_data.items() + } + records.append(CMHCRentalRecord(**cast(dict[str, Any], record_data))) + return records + + def _infer_survey_year(self, df: pd.DataFrame) -> int: + """Infer survey year from data. + + Args: + df: DataFrame with survey_year column. + + Returns: + Survey year as integer. + """ + if "survey_year" in df.columns: + return int(df["survey_year"].iloc[0]) + raise ValueError("Cannot infer survey year from data.") diff --git a/portfolio_app/toronto/parsers/geo.py b/portfolio_app/toronto/parsers/geo.py new file mode 100644 index 0000000..0e0f979 --- /dev/null +++ b/portfolio_app/toronto/parsers/geo.py @@ -0,0 +1,333 @@ +"""GeoJSON parser for geographic boundary files. + +This module provides parsers for loading geographic boundary files +(GeoJSON format) and converting them to Pydantic schemas for database +loading or direct use in Plotly choropleth maps. +""" + +import json +from pathlib import Path +from typing import Any + +from pyproj import Transformer +from shapely.geometry import mapping, shape +from shapely.ops import transform + +from portfolio_app.toronto.schemas import CMHCZone, Neighbourhood + +# Transformer for reprojecting from Web Mercator to WGS84 +_TRANSFORMER_3857_TO_4326 = Transformer.from_crs( + "EPSG:3857", "EPSG:4326", always_xy=True +) + + +def load_geojson(path: Path) -> dict[str, Any]: + """Load a GeoJSON file and return as dictionary. + + Args: + path: Path to the GeoJSON file. + + Returns: + GeoJSON as dictionary (FeatureCollection). + + Raises: + FileNotFoundError: If file does not exist. + ValueError: If file is not valid GeoJSON. + """ + if not path.exists(): + raise FileNotFoundError(f"GeoJSON file not found: {path}") + + if path.suffix.lower() not in (".geojson", ".json"): + raise ValueError(f"Expected GeoJSON file, got: {path.suffix}") + + with open(path, encoding="utf-8") as f: + data = json.load(f) + + if data.get("type") != "FeatureCollection": + raise ValueError("GeoJSON must be a FeatureCollection") + + return dict(data) + + +def geometry_to_wkt(geometry: dict[str, Any]) -> str: + """Convert GeoJSON geometry to WKT string. + + Args: + geometry: GeoJSON geometry dictionary. + + Returns: + WKT representation of the geometry. + """ + return str(shape(geometry).wkt) + + +def reproject_geometry( + geometry: dict[str, Any], source_crs: str = "EPSG:3857" +) -> dict[str, Any]: + """Reproject a GeoJSON geometry to WGS84 (EPSG:4326). + + Args: + geometry: GeoJSON geometry dictionary. + source_crs: Source CRS (default EPSG:3857 Web Mercator). + + Returns: + GeoJSON geometry in WGS84 coordinates. + """ + if source_crs == "EPSG:3857": + transformer = _TRANSFORMER_3857_TO_4326 + else: + transformer = Transformer.from_crs(source_crs, "EPSG:4326", always_xy=True) + + geom = shape(geometry) + reprojected = transform(transformer.transform, geom) + return dict(mapping(reprojected)) + + +class CMHCZoneParser: + """Parser for CMHC zone boundary GeoJSON files. + + CMHC zone boundaries are extracted from the R `cmhc` package using + `get_cmhc_geography(geography_type="ZONE", cma="Toronto")`. + + Expected GeoJSON properties: + - zone_code or Zone_Code: Zone identifier + - zone_name or Zone_Name: Zone name + """ + + # Property name mappings for different GeoJSON formats + CODE_PROPERTIES = ["zone_code", "Zone_Code", "ZONE_CODE", "zonecode", "code"] + NAME_PROPERTIES = [ + "zone_name", + "Zone_Name", + "ZONE_NAME", + "ZONE_NAME_EN", + "NAME_EN", + "zonename", + "name", + "NAME", + ] + + def __init__(self, geojson_path: Path) -> None: + """Initialize parser with path to GeoJSON file. + + Args: + geojson_path: Path to the CMHC zones GeoJSON file. + """ + self.geojson_path = geojson_path + self._geojson: dict[str, Any] | None = None + + @property + def geojson(self) -> dict[str, Any]: + """Lazy-load and return raw GeoJSON data.""" + if self._geojson is None: + self._geojson = load_geojson(self.geojson_path) + return self._geojson + + def _find_property( + self, properties: dict[str, Any], candidates: list[str] + ) -> str | None: + """Find a property value by checking multiple candidate names.""" + for name in candidates: + if name in properties and properties[name] is not None: + return str(properties[name]) + return None + + def parse(self) -> list[CMHCZone]: + """Parse GeoJSON and return list of CMHCZone schemas. + + Returns: + List of validated CMHCZone objects. + + Raises: + ValueError: If required properties are missing. + """ + zones = [] + for feature in self.geojson.get("features", []): + props = feature.get("properties", {}) + geom = feature.get("geometry") + + zone_code = self._find_property(props, self.CODE_PROPERTIES) + zone_name = self._find_property(props, self.NAME_PROPERTIES) + + if not zone_code: + raise ValueError( + f"Zone code not found in properties: {list(props.keys())}" + ) + if not zone_name: + zone_name = zone_code # Fallback to code if name missing + + geometry_wkt = geometry_to_wkt(geom) if geom else None + + zones.append( + CMHCZone( + zone_code=zone_code, + zone_name=zone_name, + geometry_wkt=geometry_wkt, + ) + ) + + return zones + + def _needs_reprojection(self) -> bool: + """Check if GeoJSON needs reprojection to WGS84.""" + crs = self.geojson.get("crs", {}) + crs_name = crs.get("properties", {}).get("name", "") + # EPSG:3857 or Web Mercator needs reprojection + return "3857" in crs_name or "900913" in crs_name + + def get_geojson_for_choropleth( + self, key_property: str = "zone_code" + ) -> dict[str, Any]: + """Get GeoJSON formatted for Plotly choropleth maps. + + Ensures the feature properties include a standardized key for + joining with data. Automatically reprojects from EPSG:3857 to + WGS84 if needed. + + Args: + key_property: Property name to use as feature identifier. + + Returns: + GeoJSON FeatureCollection with standardized properties in WGS84. + """ + needs_reproject = self._needs_reprojection() + features = [] + + for feature in self.geojson.get("features", []): + props = feature.get("properties", {}) + new_props = dict(props) + + # Ensure standardized property names exist + zone_code = self._find_property(props, self.CODE_PROPERTIES) + zone_name = self._find_property(props, self.NAME_PROPERTIES) + + new_props["zone_code"] = zone_code + new_props["zone_name"] = zone_name or zone_code + + # Reproject geometry if needed + geometry = feature.get("geometry") + if needs_reproject and geometry: + geometry = reproject_geometry(geometry) + + features.append( + { + "type": "Feature", + "properties": new_props, + "geometry": geometry, + } + ) + + return {"type": "FeatureCollection", "features": features} + + +class NeighbourhoodParser: + """Parser for City of Toronto neighbourhood boundary GeoJSON files. + + Neighbourhood boundaries are from the City of Toronto Open Data portal. + + Expected GeoJSON properties: + - neighbourhood_id or AREA_ID: Neighbourhood ID (1-158) + - name or AREA_NAME: Neighbourhood name + """ + + ID_PROPERTIES = [ + "neighbourhood_id", + "AREA_SHORT_CODE", # City of Toronto 158 neighbourhoods + "AREA_LONG_CODE", + "AREA_ID", + "area_id", + "id", + "ID", + "HOOD_ID", + ] + NAME_PROPERTIES = [ + "AREA_NAME", # City of Toronto 158 neighbourhoods + "name", + "NAME", + "area_name", + "neighbourhood_name", + ] + + def __init__(self, geojson_path: Path) -> None: + """Initialize parser with path to GeoJSON file.""" + self.geojson_path = geojson_path + self._geojson: dict[str, Any] | None = None + + @property + def geojson(self) -> dict[str, Any]: + """Lazy-load and return raw GeoJSON data.""" + if self._geojson is None: + self._geojson = load_geojson(self.geojson_path) + return self._geojson + + def _find_property( + self, properties: dict[str, Any], candidates: list[str] + ) -> str | None: + """Find a property value by checking multiple candidate names.""" + for name in candidates: + if name in properties and properties[name] is not None: + return str(properties[name]) + return None + + def parse(self) -> list[Neighbourhood]: + """Parse GeoJSON and return list of Neighbourhood schemas. + + Note: This parser only extracts ID, name, and geometry. + Census enrichment data (population, income, etc.) should be + loaded separately and merged. + """ + neighbourhoods = [] + for feature in self.geojson.get("features", []): + props = feature.get("properties", {}) + geom = feature.get("geometry") + + neighbourhood_id_str = self._find_property(props, self.ID_PROPERTIES) + name = self._find_property(props, self.NAME_PROPERTIES) + + if not neighbourhood_id_str: + raise ValueError( + f"Neighbourhood ID not found in properties: {list(props.keys())}" + ) + + neighbourhood_id = int(neighbourhood_id_str) + if not name: + name = f"Neighbourhood {neighbourhood_id}" + + geometry_wkt = geometry_to_wkt(geom) if geom else None + + neighbourhoods.append( + Neighbourhood( + neighbourhood_id=neighbourhood_id, + name=name, + geometry_wkt=geometry_wkt, + ) + ) + + return neighbourhoods + + def get_geojson_for_choropleth( + self, key_property: str = "neighbourhood_id" + ) -> dict[str, Any]: + """Get GeoJSON formatted for Plotly choropleth maps.""" + features = [] + for feature in self.geojson.get("features", []): + props = feature.get("properties", {}) + new_props = dict(props) + + neighbourhood_id = self._find_property(props, self.ID_PROPERTIES) + name = self._find_property(props, self.NAME_PROPERTIES) + + new_props["neighbourhood_id"] = ( + int(neighbourhood_id) if neighbourhood_id else None + ) + new_props["name"] = name + + features.append( + { + "type": "Feature", + "properties": new_props, + "geometry": feature.get("geometry"), + } + ) + + return {"type": "FeatureCollection", "features": features} diff --git a/portfolio_app/toronto/parsers/statcan_cmhc.py b/portfolio_app/toronto/parsers/statcan_cmhc.py new file mode 100644 index 0000000..75d590c --- /dev/null +++ b/portfolio_app/toronto/parsers/statcan_cmhc.py @@ -0,0 +1,383 @@ +"""Parser for CMHC rental data via Statistics Canada API. + +Downloads rental market data (average rent, vacancy rates, universe) +from Statistics Canada's Web Data Service. + +Data Sources: +- Table 34-10-0127: Vacancy rates +- Table 34-10-0129: Rental universe (total units) +- Table 34-10-0133: Average rent by bedroom type +""" + +import contextlib +import io +import logging +import zipfile +from dataclasses import dataclass +from decimal import Decimal +from pathlib import Path +from typing import Any + +import httpx +import pandas as pd + +logger = logging.getLogger(__name__) + +# StatCan Web Data Service endpoints +STATCAN_API_BASE = "https://www150.statcan.gc.ca/t1/wds/rest" +STATCAN_DOWNLOAD_BASE = "https://www150.statcan.gc.ca/n1/tbl/csv" + +# CMHC table IDs +CMHC_TABLES = { + "vacancy": "34100127", + "universe": "34100129", + "rent": "34100133", +} + +# Toronto CMA identifier in StatCan data +TORONTO_DGUID = "2011S0503535" +TORONTO_GEO_NAME = "Toronto, Ontario" + + +@dataclass +class CMHCRentalRecord: + """Rental market record for database loading.""" + + year: int + month: int # CMHC surveys in October, so month=10 + zone_name: str + bedroom_type: str + avg_rent: Decimal | None + vacancy_rate: Decimal | None + universe: int | None + + +class StatCanCMHCParser: + """Parser for CMHC rental data from Statistics Canada. + + Downloads and processes rental market survey data including: + - Average rents by bedroom type + - Vacancy rates + - Rental universe (total units) + + Data is available from 1987 to present, updated annually in January. + """ + + BEDROOM_TYPE_MAP = { + "Bachelor units": "bachelor", + "One bedroom units": "1bed", + "Two bedroom units": "2bed", + "Three bedroom units": "3bed", + "Total": "total", + } + + STRUCTURE_FILTER = "Apartment structures of six units and over" + + def __init__( + self, + cache_dir: Path | None = None, + timeout: float = 60.0, + ) -> None: + """Initialize parser. + + Args: + cache_dir: Optional directory for caching downloaded files. + timeout: HTTP request timeout in seconds. + """ + self._cache_dir = cache_dir + self._timeout = timeout + self._client: httpx.Client | None = None + + @property + def client(self) -> httpx.Client: + """Lazy-initialize HTTP client.""" + if self._client is None: + self._client = httpx.Client( + timeout=self._timeout, + follow_redirects=True, + ) + return self._client + + def close(self) -> None: + """Close HTTP client.""" + if self._client is not None: + self._client.close() + self._client = None + + def __enter__(self) -> "StatCanCMHCParser": + return self + + def __exit__(self, *args: Any) -> None: + self.close() + + def _get_download_url(self, table_id: str) -> str: + """Get CSV download URL for a StatCan table. + + Args: + table_id: StatCan table ID (e.g., "34100133"). + + Returns: + Direct download URL for the CSV zip file. + """ + api_url = f"{STATCAN_API_BASE}/getFullTableDownloadCSV/{table_id}/en" + response = self.client.get(api_url) + response.raise_for_status() + + data = response.json() + if data.get("status") != "SUCCESS": + raise ValueError(f"StatCan API error: {data}") + + return str(data["object"]) + + def _download_table(self, table_id: str) -> pd.DataFrame: + """Download and extract a StatCan table as DataFrame. + + Args: + table_id: StatCan table ID. + + Returns: + DataFrame with table data. + """ + # Check cache first + if self._cache_dir: + cache_file = self._cache_dir / f"{table_id}.csv" + if cache_file.exists(): + logger.debug(f"Loading {table_id} from cache") + return pd.read_csv(cache_file) + + # Get download URL and fetch + download_url = self._get_download_url(table_id) + logger.info(f"Downloading StatCan table {table_id}...") + + response = self.client.get(download_url) + response.raise_for_status() + + # Extract CSV from zip + with zipfile.ZipFile(io.BytesIO(response.content)) as zf: + csv_name = f"{table_id}.csv" + with zf.open(csv_name) as f: + df = pd.read_csv(f) + + # Cache if directory specified + if self._cache_dir: + self._cache_dir.mkdir(parents=True, exist_ok=True) + df.to_csv(self._cache_dir / f"{table_id}.csv", index=False) + + logger.info(f"Downloaded {len(df)} records from table {table_id}") + return df + + def _filter_toronto(self, df: pd.DataFrame) -> pd.DataFrame: + """Filter DataFrame to Toronto CMA only. + + Args: + df: Full StatCan DataFrame. + + Returns: + DataFrame filtered to Toronto. + """ + # Try DGUID first, then GEO name + if "DGUID" in df.columns: + toronto_df = df[df["DGUID"] == TORONTO_DGUID] + if len(toronto_df) > 0: + return toronto_df + + if "GEO" in df.columns: + return df[df["GEO"] == TORONTO_GEO_NAME] + + raise ValueError("Could not identify Toronto data in DataFrame") + + def get_vacancy_rates( + self, + years: list[int] | None = None, + ) -> dict[int, Decimal]: + """Fetch Toronto vacancy rates by year. + + Args: + years: Optional list of years to filter. + + Returns: + Dictionary mapping year to vacancy rate. + """ + df = self._download_table(CMHC_TABLES["vacancy"]) + df = self._filter_toronto(df) + + # Filter years if specified + if years: + df = df[df["REF_DATE"].isin(years)] + + # Extract year -> rate mapping + rates = {} + for _, row in df.iterrows(): + year = int(row["REF_DATE"]) + value = row.get("VALUE") + if pd.notna(value): + rates[year] = Decimal(str(value)) + + logger.info(f"Fetched vacancy rates for {len(rates)} years") + return rates + + def get_rental_universe( + self, + years: list[int] | None = None, + ) -> dict[tuple[int, str], int]: + """Fetch Toronto rental universe (total units) by year and bedroom type. + + Args: + years: Optional list of years to filter. + + Returns: + Dictionary mapping (year, bedroom_type) to unit count. + """ + df = self._download_table(CMHC_TABLES["universe"]) + df = self._filter_toronto(df) + + # Filter to standard apartment structures + if "Type of structure" in df.columns: + df = df[df["Type of structure"] == self.STRUCTURE_FILTER] + + if years: + df = df[df["REF_DATE"].isin(years)] + + universe = {} + for _, row in df.iterrows(): + year = int(row["REF_DATE"]) + bedroom_raw = row.get("Type of unit", "Total") + bedroom = self.BEDROOM_TYPE_MAP.get(bedroom_raw, "other") + value = row.get("VALUE") + + if pd.notna(value) and value is not None: + universe[(year, bedroom)] = int(str(value)) + + logger.info( + f"Fetched rental universe for {len(universe)} year/bedroom combinations" + ) + return universe + + def get_average_rents( + self, + years: list[int] | None = None, + ) -> dict[tuple[int, str], Decimal]: + """Fetch Toronto average rents by year and bedroom type. + + Args: + years: Optional list of years to filter. + + Returns: + Dictionary mapping (year, bedroom_type) to average rent. + """ + df = self._download_table(CMHC_TABLES["rent"]) + df = self._filter_toronto(df) + + # Filter to standard apartment structures (most reliable data) + if "Type of structure" in df.columns: + df = df[df["Type of structure"] == self.STRUCTURE_FILTER] + + if years: + df = df[df["REF_DATE"].isin(years)] + + rents = {} + for _, row in df.iterrows(): + year = int(row["REF_DATE"]) + bedroom_raw = row.get("Type of unit", "Total") + bedroom = self.BEDROOM_TYPE_MAP.get(bedroom_raw, "other") + value = row.get("VALUE") + + if pd.notna(value) and str(value) not in ("F", ".."): + with contextlib.suppress(Exception): + rents[(year, bedroom)] = Decimal(str(value)) + + logger.info(f"Fetched average rents for {len(rents)} year/bedroom combinations") + return rents + + def get_all_rental_data( + self, + start_year: int = 2014, + end_year: int | None = None, + ) -> list[CMHCRentalRecord]: + """Fetch all Toronto rental data and combine into records. + + Args: + start_year: First year to include. + end_year: Last year to include (defaults to current year + 1). + + Returns: + List of CMHCRentalRecord objects ready for database loading. + """ + import datetime + + if end_year is None: + end_year = datetime.date.today().year + 1 + + years = list(range(start_year, end_year + 1)) + + logger.info( + f"Fetching CMHC rental data for Toronto ({start_year}-{end_year})..." + ) + + # Fetch all data types + vacancy_rates = self.get_vacancy_rates(years) + rents = self.get_average_rents(years) + universe = self.get_rental_universe(years) + + # Combine into records + records = [] + bedroom_types = ["bachelor", "1bed", "2bed", "3bed"] + + for year in years: + vacancy = vacancy_rates.get(year) + + for bedroom in bedroom_types: + avg_rent = rents.get((year, bedroom)) + units = universe.get((year, bedroom)) + + # Skip if no rent data for this year/bedroom + if avg_rent is None: + continue + + records.append( + CMHCRentalRecord( + year=year, + month=10, # CMHC surveys in October + zone_name="Toronto CMA", + bedroom_type=bedroom, + avg_rent=avg_rent, + vacancy_rate=vacancy, + universe=units, + ) + ) + + logger.info(f"Created {len(records)} CMHC rental records") + return records + + +def fetch_toronto_rental_data( + start_year: int = 2014, + end_year: int | None = None, + cache_dir: Path | None = None, +) -> list[CMHCRentalRecord]: + """Convenience function to fetch Toronto rental data. + + Args: + start_year: First year to include. + end_year: Last year to include. + cache_dir: Optional cache directory. + + Returns: + List of CMHCRentalRecord objects. + """ + with StatCanCMHCParser(cache_dir=cache_dir) as parser: + return parser.get_all_rental_data(start_year, end_year) + + +if __name__ == "__main__": + # Test the parser + logging.basicConfig(level=logging.INFO) + + records = fetch_toronto_rental_data(start_year=2020) + + print(f"\nFetched {len(records)} records") + print("\nSample records:") + for r in records[:10]: + print( + f" {r.year} {r.bedroom_type}: ${r.avg_rent} rent, {r.vacancy_rate}% vacancy" + ) diff --git a/portfolio_app/toronto/parsers/toronto_open_data.py b/portfolio_app/toronto/parsers/toronto_open_data.py new file mode 100644 index 0000000..30e756d --- /dev/null +++ b/portfolio_app/toronto/parsers/toronto_open_data.py @@ -0,0 +1,643 @@ +"""Parser for Toronto Open Data CKAN API. + +Fetches neighbourhood boundaries, census profiles, and amenities data +from the City of Toronto's Open Data Portal. + +API Documentation: https://open.toronto.ca/dataset/ +""" + +import contextlib +import json +import logging +from decimal import Decimal +from pathlib import Path +from typing import Any + +import httpx + +from portfolio_app.toronto.schemas import ( + AmenityRecord, + AmenityType, + CensusRecord, + NeighbourhoodRecord, +) + +logger = logging.getLogger(__name__) + + +class TorontoOpenDataParser: + """Parser for Toronto Open Data CKAN API. + + Provides methods to fetch and parse neighbourhood boundaries, census profiles, + and amenities (parks, schools, childcare) from the Toronto Open Data portal. + """ + + BASE_URL = "https://ckan0.cf.opendata.inter.prod-toronto.ca" + API_PATH = "/api/3/action" + + # Dataset package IDs + DATASETS = { + "neighbourhoods": "neighbourhoods", + "neighbourhood_profiles": "neighbourhood-profiles", + "parks": "parks", + "schools": "school-locations-all-types", + "childcare": "licensed-child-care-centres", + } + + def __init__( + self, + cache_dir: Path | None = None, + timeout: float = 30.0, + ) -> None: + """Initialize parser. + + Args: + cache_dir: Optional directory for caching API responses. + timeout: HTTP request timeout in seconds. + """ + self._cache_dir = cache_dir + self._timeout = timeout + self._client: httpx.Client | None = None + self._neighbourhood_name_map: dict[str, int] | None = None + + @property + def client(self) -> httpx.Client: + """Lazy-initialize HTTP client.""" + if self._client is None: + self._client = httpx.Client( + base_url=self.BASE_URL, + timeout=self._timeout, + headers={"Accept": "application/json"}, + ) + return self._client + + def close(self) -> None: + """Close HTTP client.""" + if self._client is not None: + self._client.close() + self._client = None + + def _get_neighbourhood_name_map(self) -> dict[str, int]: + """Build and cache a mapping of neighbourhood names to IDs. + + Returns: + Dictionary mapping normalized neighbourhood names to area_id. + """ + if self._neighbourhood_name_map is not None: + return self._neighbourhood_name_map + + neighbourhoods = self.get_neighbourhoods() + self._neighbourhood_name_map = {} + + for n in neighbourhoods: + # Add multiple variations of the name for flexible matching + name_lower = n.area_name.lower().strip() + self._neighbourhood_name_map[name_lower] = n.area_id + + # Also add without common suffixes/prefixes + for suffix in [" neighbourhood", " area", "-"]: + if suffix in name_lower: + alt_name = name_lower.replace(suffix, "").strip() + self._neighbourhood_name_map[alt_name] = n.area_id + + logger.debug( + f"Built neighbourhood name map with {len(self._neighbourhood_name_map)} entries" + ) + return self._neighbourhood_name_map + + def _match_neighbourhood_id(self, name: str) -> int | None: + """Match a neighbourhood name to its ID. + + Args: + name: Neighbourhood name from census data. + + Returns: + Neighbourhood ID or None if not found. + """ + name_map = self._get_neighbourhood_name_map() + name_lower = name.lower().strip() + + # Direct match + if name_lower in name_map: + return name_map[name_lower] + + # Try removing parenthetical content + if "(" in name_lower: + base_name = name_lower.split("(")[0].strip() + if base_name in name_map: + return name_map[base_name] + + # Try fuzzy matching with first few chars + for key, area_id in name_map.items(): + if key.startswith(name_lower[:10]) or name_lower.startswith(key[:10]): + return area_id + + return None + + def __enter__(self) -> "TorontoOpenDataParser": + return self + + def __exit__(self, *args: Any) -> None: + self.close() + + def _get_package(self, package_id: str) -> dict[str, Any]: + """Fetch package metadata from CKAN. + + Args: + package_id: The package/dataset ID. + + Returns: + Package metadata dictionary. + """ + response = self.client.get( + f"{self.API_PATH}/package_show", + params={"id": package_id}, + ) + response.raise_for_status() + result = response.json() + + if not result.get("success"): + raise ValueError(f"CKAN API error: {result.get('error', 'Unknown error')}") + + return dict(result["result"]) + + def _get_resource_url( + self, + package_id: str, + format_filter: str = "geojson", + ) -> str: + """Get the download URL for a resource in a package. + + Args: + package_id: The package/dataset ID. + format_filter: Resource format to filter by (e.g., 'geojson', 'csv'). + + Returns: + Resource download URL. + + Raises: + ValueError: If no matching resource is found. + """ + package = self._get_package(package_id) + resources = package.get("resources", []) + + for resource in resources: + resource_format = resource.get("format", "").lower() + if format_filter.lower() in resource_format: + return str(resource["url"]) + + available = [r.get("format") for r in resources] + raise ValueError( + f"No {format_filter} resource in {package_id}. Available: {available}" + ) + + def _fetch_geojson(self, package_id: str) -> dict[str, Any]: + """Fetch GeoJSON data from a package. + + Handles both pure GeoJSON responses and CSV responses with embedded + geometry columns (common in Toronto Open Data). + + Args: + package_id: The package/dataset ID. + + Returns: + GeoJSON FeatureCollection. + """ + # Check cache first + if self._cache_dir: + cache_file = self._cache_dir / f"{package_id}.geojson" + if cache_file.exists(): + logger.debug(f"Loading {package_id} from cache") + with open(cache_file, encoding="utf-8") as f: + return dict(json.load(f)) + + url = self._get_resource_url(package_id, format_filter="geojson") + logger.info(f"Fetching GeoJSON from {url}") + + response = self.client.get(url) + response.raise_for_status() + + # Try to parse as JSON first + try: + data = response.json() + # If it's already a valid GeoJSON FeatureCollection, return it + if isinstance(data, dict) and data.get("type") == "FeatureCollection": + if self._cache_dir: + self._cache_dir.mkdir(parents=True, exist_ok=True) + cache_file = self._cache_dir / f"{package_id}.geojson" + with open(cache_file, "w", encoding="utf-8") as f: + json.dump(data, f) + return dict(data) + except json.JSONDecodeError: + pass + + # If JSON parsing failed, it's likely CSV with embedded geometry + # Parse CSV and convert to GeoJSON FeatureCollection + logger.info("Response is CSV format, converting to GeoJSON...") + import csv + import io + + # Increase field size limit for large geometry columns + csv.field_size_limit(10 * 1024 * 1024) # 10 MB + + csv_text = response.text + reader = csv.DictReader(io.StringIO(csv_text)) + + features = [] + for row in reader: + # Extract geometry from the 'geometry' column if present + geometry = None + if "geometry" in row and row["geometry"]: + with contextlib.suppress(json.JSONDecodeError): + geometry = json.loads(row["geometry"]) + + # Build properties from all other columns + properties = {k: v for k, v in row.items() if k != "geometry"} + + features.append( + { + "type": "Feature", + "geometry": geometry, + "properties": properties, + } + ) + + geojson_data: dict[str, Any] = { + "type": "FeatureCollection", + "features": features, + } + + # Cache the converted response + if self._cache_dir: + self._cache_dir.mkdir(parents=True, exist_ok=True) + cache_file = self._cache_dir / f"{package_id}.geojson" + with open(cache_file, "w", encoding="utf-8") as f: + json.dump(geojson_data, f) + + return geojson_data + + def _fetch_csv_as_json(self, package_id: str) -> list[dict[str, Any]]: + """Fetch CSV data as JSON records via CKAN datastore. + + Args: + package_id: The package/dataset ID. + + Returns: + List of records as dictionaries. + """ + package = self._get_package(package_id) + resources = package.get("resources", []) + + # Find a datastore-enabled resource + for resource in resources: + if resource.get("datastore_active"): + resource_id = resource["id"] + break + else: + raise ValueError(f"No datastore resource in {package_id}") + + # Fetch all records via datastore_search + records: list[dict[str, Any]] = [] + offset = 0 + limit = 1000 + + while True: + response = self.client.get( + f"{self.API_PATH}/datastore_search", + params={"id": resource_id, "limit": limit, "offset": offset}, + ) + response.raise_for_status() + result = response.json() + + if not result.get("success"): + raise ValueError(f"Datastore error: {result.get('error')}") + + batch = result["result"]["records"] + records.extend(batch) + + if len(batch) < limit: + break + offset += limit + + return records + + def get_neighbourhoods(self) -> list[NeighbourhoodRecord]: + """Fetch 158 Toronto neighbourhood boundaries. + + Returns: + List of validated NeighbourhoodRecord objects. + """ + geojson = self._fetch_geojson(self.DATASETS["neighbourhoods"]) + features = geojson.get("features", []) + + records = [] + for feature in features: + props = feature.get("properties", {}) + geometry = feature.get("geometry") + + # Use AREA_SHORT_CODE as the primary ID (1-158 range) + # AREA_ID is a large internal identifier not useful for our schema + short_code = props.get("AREA_SHORT_CODE") or props.get( + "area_short_code", "" + ) + if short_code: + area_id = int("".join(c for c in str(short_code) if c.isdigit()) or "0") + else: + # Fallback to _id (row number) if AREA_SHORT_CODE not available + area_id = int(props.get("_id", 0)) + + if area_id == 0: + logger.warning(f"Skipping neighbourhood with no valid ID: {props}") + continue + + area_name = ( + props.get("AREA_NAME") + or props.get("area_name") + or f"Neighbourhood {area_id}" + ) + + records.append( + NeighbourhoodRecord( + area_id=area_id, + area_name=str(area_name), + area_short_code=str(short_code) if short_code else None, + geometry=geometry, + ) + ) + + logger.info(f"Parsed {len(records)} neighbourhoods") + return records + + # Mapping of indicator names to CensusRecord fields + # Keys are partial matches (case-insensitive) found in the "Characteristic" column + # Order matters - first match wins, so more specific patterns come first + # Note: owner/renter counts are raw numbers, not percentages - calculated in dbt + CENSUS_INDICATOR_MAPPING: dict[str, str] = { + "population, 2021": "population", + "population, 2016": "population", + "population density per square kilometre": "population_density", + "median total income of households in": "median_household_income", + "average total income of households in": "average_household_income", + "unemployment rate": "unemployment_rate", + "bachelor's degree or higher": "pct_bachelors_or_higher", + "average age": "median_age", + "average value of dwellings": "average_dwelling_value", + } + + def get_census_profiles(self, year: int = 2021) -> list[CensusRecord]: + """Fetch neighbourhood census profiles. + + The Toronto Open Data neighbourhood profiles dataset is pivoted: + - Rows are demographic indicators (e.g., "Population", "Median Income") + - Columns are neighbourhoods (e.g., "Agincourt North", "Alderwood") + + This method transposes the data to create one CensusRecord per neighbourhood. + + Args: + year: Census year (2016 or 2021). + + Returns: + List of validated CensusRecord objects. + """ + try: + raw_records = self._fetch_csv_as_json( + self.DATASETS["neighbourhood_profiles"] + ) + except ValueError as e: + logger.warning(f"Could not fetch census profiles: {e}") + return [] + + if not raw_records: + logger.warning("Census profiles dataset is empty") + return [] + + logger.info(f"Fetched {len(raw_records)} census profile rows") + + # Find the characteristic/indicator column name + # Prioritize "Characteristic" over "Category" since both may exist + sample_row = raw_records[0] + char_col = None + + # First try exact match for Characteristic + if "Characteristic" in sample_row: + char_col = "Characteristic" + else: + # Fall back to pattern matching + for col in sample_row: + col_lower = col.lower() + if "characteristic" in col_lower: + char_col = col + break + + # Last resort: try Category + if not char_col: + for col in sample_row: + if "category" in col.lower(): + char_col = col + break + + if not char_col: + # Try other common column names + for candidate in ["Topic", "_id"]: + if candidate in sample_row: + char_col = candidate + break + + if not char_col: + logger.warning("Could not find characteristic column in census data") + return [] + + # Identify neighbourhood columns (exclude metadata columns) + exclude_cols = { + char_col, + "_id", + "Topic", + "Data Source", + "Characteristic", + "Category", + } + neighbourhood_cols = [col for col in sample_row if col not in exclude_cols] + + logger.info(f"Found {len(neighbourhood_cols)} neighbourhood columns") + + # Build a lookup: neighbourhood_name -> {field: value} + neighbourhood_data: dict[str, dict[str, Decimal | int | None]] = { + col: {} for col in neighbourhood_cols + } + + # Process each row to extract indicator values + for row in raw_records: + characteristic = str(row.get(char_col, "")).lower().strip() + + # Check if this row matches any indicator we care about + for indicator_pattern, field_name in self.CENSUS_INDICATOR_MAPPING.items(): + if indicator_pattern in characteristic: + # Extract values for each neighbourhood + for col in neighbourhood_cols: + value = row.get(col) + if value is not None and value != "": + try: + # Clean and convert value + str_val = str(value).replace(",", "").replace("$", "") + str_val = str_val.replace("%", "").strip() + if str_val and str_val not in ("x", "X", "F", ".."): + numeric_val = Decimal(str_val) + # Only store if not already set (first match wins) + if field_name not in neighbourhood_data[col]: + neighbourhood_data[col][ + field_name + ] = numeric_val + except (ValueError, TypeError): + pass + break # Move to next row after matching + + # Convert to CensusRecord objects + records = [] + unmatched = [] + + for neighbourhood_name, data in neighbourhood_data.items(): + if not data: + continue + + # Match neighbourhood name to ID + neighbourhood_id = self._match_neighbourhood_id(neighbourhood_name) + if neighbourhood_id is None: + unmatched.append(neighbourhood_name) + continue + + try: + pop_val = data.get("population") + population = int(pop_val) if pop_val is not None else None + + record = CensusRecord( + neighbourhood_id=neighbourhood_id, + census_year=year, + population=population, + population_density=data.get("population_density"), + median_household_income=data.get("median_household_income"), + average_household_income=data.get("average_household_income"), + unemployment_rate=data.get("unemployment_rate"), + pct_bachelors_or_higher=data.get("pct_bachelors_or_higher"), + pct_owner_occupied=data.get("pct_owner_occupied"), + pct_renter_occupied=data.get("pct_renter_occupied"), + median_age=data.get("median_age"), + average_dwelling_value=data.get("average_dwelling_value"), + ) + records.append(record) + except Exception as e: + logger.debug(f"Skipping neighbourhood {neighbourhood_name}: {e}") + + if unmatched: + logger.warning( + f"Could not match {len(unmatched)} neighbourhoods: {unmatched[:5]}..." + ) + + logger.info(f"Parsed {len(records)} census records for year {year}") + return records + + def get_parks(self) -> list[AmenityRecord]: + """Fetch park locations. + + Returns: + List of validated AmenityRecord objects. + """ + return self._fetch_amenities( + self.DATASETS["parks"], + AmenityType.PARK, + name_field="ASSET_NAME", + address_field="ADDRESS_FULL", + ) + + def get_schools(self) -> list[AmenityRecord]: + """Fetch school locations. + + Returns: + List of validated AmenityRecord objects. + """ + return self._fetch_amenities( + self.DATASETS["schools"], + AmenityType.SCHOOL, + name_field="NAME", + address_field="ADDRESS_FULL", + ) + + def get_childcare_centres(self) -> list[AmenityRecord]: + """Fetch licensed childcare centre locations. + + Returns: + List of validated AmenityRecord objects. + """ + return self._fetch_amenities( + self.DATASETS["childcare"], + AmenityType.CHILDCARE, + name_field="LOC_NAME", + address_field="ADDRESS", + ) + + def _fetch_amenities( + self, + package_id: str, + amenity_type: AmenityType, + name_field: str, + address_field: str, + ) -> list[AmenityRecord]: + """Fetch and parse amenity data from GeoJSON. + + Args: + package_id: CKAN package ID. + amenity_type: Type of amenity. + name_field: Property name containing amenity name. + address_field: Property name containing address. + + Returns: + List of AmenityRecord objects. + """ + try: + geojson = self._fetch_geojson(package_id) + except (httpx.HTTPError, ValueError) as e: + logger.warning(f"Could not fetch {package_id}: {e}") + return [] + + features = geojson.get("features", []) + records = [] + + for feature in features: + props = feature.get("properties", {}) + geometry = feature.get("geometry") + + # Get coordinates from geometry + lat, lon = None, None + if geometry and geometry.get("type") == "Point": + coords = geometry.get("coordinates", []) + if len(coords) >= 2: + lon, lat = coords[0], coords[1] + + # Try to determine neighbourhood_id + # Many datasets include AREA_ID or similar + neighbourhood_id = ( + props.get("AREA_ID") + or props.get("area_id") + or props.get("NEIGHBOURHOOD_ID") + or 0 # Will need spatial join if not available + ) + + name = props.get(name_field) or props.get(name_field.lower()) or "Unknown" + address = props.get(address_field) or props.get(address_field.lower()) + + # Skip if we don't have a neighbourhood assignment + if neighbourhood_id == 0: + continue + + records.append( + AmenityRecord( + neighbourhood_id=int(neighbourhood_id), + amenity_type=amenity_type, + amenity_name=str(name)[:200], + address=str(address)[:300] if address else None, + latitude=Decimal(str(lat)) if lat else None, + longitude=Decimal(str(lon)) if lon else None, + ) + ) + + logger.info(f"Parsed {len(records)} {amenity_type.value} records") + return records diff --git a/portfolio_app/toronto/parsers/toronto_police.py b/portfolio_app/toronto/parsers/toronto_police.py new file mode 100644 index 0000000..2a5661b --- /dev/null +++ b/portfolio_app/toronto/parsers/toronto_police.py @@ -0,0 +1,371 @@ +"""Parser for Toronto Police crime data via CKAN API. + +Fetches neighbourhood crime rates and major crime indicators from the +Toronto Police Service data hosted on Toronto Open Data Portal. + +Data Sources: +- Neighbourhood Crime Rates: Annual crime rates by neighbourhood +- Major Crime Indicators (MCI): Detailed incident-level data +""" + +import contextlib +import logging +from decimal import Decimal +from typing import Any + +import httpx + +from portfolio_app.toronto.schemas import CrimeRecord, CrimeType + +logger = logging.getLogger(__name__) + + +# Mapping from Toronto Police crime categories to CrimeType enum +CRIME_TYPE_MAPPING: dict[str, CrimeType] = { + "assault": CrimeType.ASSAULT, + "assaults": CrimeType.ASSAULT, + "auto theft": CrimeType.AUTO_THEFT, + "autotheft": CrimeType.AUTO_THEFT, + "auto_theft": CrimeType.AUTO_THEFT, + "break and enter": CrimeType.BREAK_AND_ENTER, + "breakenter": CrimeType.BREAK_AND_ENTER, + "break_and_enter": CrimeType.BREAK_AND_ENTER, + "homicide": CrimeType.HOMICIDE, + "homicides": CrimeType.HOMICIDE, + "robbery": CrimeType.ROBBERY, + "robberies": CrimeType.ROBBERY, + "shooting": CrimeType.SHOOTING, + "shootings": CrimeType.SHOOTING, + "theft over": CrimeType.THEFT_OVER, + "theftover": CrimeType.THEFT_OVER, + "theft_over": CrimeType.THEFT_OVER, + "theft from motor vehicle": CrimeType.THEFT_FROM_MOTOR_VEHICLE, + "theftfrommv": CrimeType.THEFT_FROM_MOTOR_VEHICLE, + "theft_from_mv": CrimeType.THEFT_FROM_MOTOR_VEHICLE, +} + + +def _normalize_crime_type(crime_str: str) -> CrimeType: + """Normalize crime type string to CrimeType enum. + + Args: + crime_str: Raw crime type string from data source. + + Returns: + Matched CrimeType enum value, or CrimeType.OTHER if no match. + """ + normalized = crime_str.lower().strip().replace("-", " ").replace("_", " ") + return CRIME_TYPE_MAPPING.get(normalized, CrimeType.OTHER) + + +class TorontoPoliceParser: + """Parser for Toronto Police crime data via CKAN API. + + Crime data is hosted on Toronto Open Data Portal but sourced from + Toronto Police Service. + """ + + BASE_URL = "https://ckan0.cf.opendata.inter.prod-toronto.ca" + API_PATH = "/api/3/action" + + # Dataset package IDs + DATASETS = { + "crime_rates": "neighbourhood-crime-rates", + "mci": "major-crime-indicators", + "shootings": "shootings-firearm-discharges", + } + + def __init__(self, timeout: float = 30.0) -> None: + """Initialize parser. + + Args: + timeout: HTTP request timeout in seconds. + """ + self._timeout = timeout + self._client: httpx.Client | None = None + + @property + def client(self) -> httpx.Client: + """Lazy-initialize HTTP client.""" + if self._client is None: + self._client = httpx.Client( + base_url=self.BASE_URL, + timeout=self._timeout, + headers={"Accept": "application/json"}, + ) + return self._client + + def close(self) -> None: + """Close HTTP client.""" + if self._client is not None: + self._client.close() + self._client = None + + def __enter__(self) -> "TorontoPoliceParser": + return self + + def __exit__(self, *args: Any) -> None: + self.close() + + def _get_package(self, package_id: str) -> dict[str, Any]: + """Fetch package metadata from CKAN.""" + response = self.client.get( + f"{self.API_PATH}/package_show", + params={"id": package_id}, + ) + response.raise_for_status() + result = response.json() + + if not result.get("success"): + raise ValueError(f"CKAN API error: {result.get('error', 'Unknown error')}") + + return dict(result["result"]) + + def _fetch_datastore_records( + self, + package_id: str, + filters: dict[str, Any] | None = None, + ) -> list[dict[str, Any]]: + """Fetch records from CKAN datastore. + + Args: + package_id: CKAN package ID. + filters: Optional filters to apply. + + Returns: + List of records as dictionaries. + """ + package = self._get_package(package_id) + resources = package.get("resources", []) + + # Find datastore-enabled resource + resource_id = None + for resource in resources: + if resource.get("datastore_active"): + resource_id = resource["id"] + break + + if not resource_id: + raise ValueError(f"No datastore resource in {package_id}") + + # Fetch all records + records: list[dict[str, Any]] = [] + offset = 0 + limit = 1000 + + while True: + params: dict[str, Any] = { + "id": resource_id, + "limit": limit, + "offset": offset, + } + if filters: + params["filters"] = str(filters) + + response = self.client.get( + f"{self.API_PATH}/datastore_search", + params=params, + ) + response.raise_for_status() + result = response.json() + + if not result.get("success"): + raise ValueError(f"Datastore error: {result.get('error')}") + + batch = result["result"]["records"] + records.extend(batch) + + if len(batch) < limit: + break + offset += limit + + return records + + def get_crime_rates( + self, + years: list[int] | None = None, + ) -> list[CrimeRecord]: + """Fetch neighbourhood crime rates. + + The crime rates dataset contains annual counts and rates per 100k + population for each neighbourhood. + + Args: + years: Optional list of years to filter. If None, fetches all. + + Returns: + List of validated CrimeRecord objects. + """ + try: + raw_records = self._fetch_datastore_records(self.DATASETS["crime_rates"]) + except (httpx.HTTPError, ValueError) as e: + logger.warning(f"Could not fetch crime rates: {e}") + return [] + + records = [] + + for row in raw_records: + # Extract neighbourhood ID (Hood_ID maps to AREA_ID) + hood_id = row.get("HOOD_ID") or row.get("Hood_ID") or row.get("hood_id") + if not hood_id: + continue + + try: + neighbourhood_id = int(hood_id) + except (ValueError, TypeError): + continue + + # Crime rate data typically has columns like: + # ASSAULT_2019, ASSAULT_RATE_2019, AUTOTHEFT_2020, etc. + # We need to parse column names to extract crime type and year + + for col_name, value in row.items(): + if value is None or col_name in ( + "_id", + "HOOD_ID", + "Hood_ID", + "hood_id", + "AREA_NAME", + "NEIGHBOURHOOD", + ): + continue + + # Try to parse column name for crime type and year + # Pattern: CRIMETYPE_YEAR or CRIMETYPE_RATE_YEAR + parts = col_name.upper().split("_") + if len(parts) < 2: + continue + + # Check if last part is a year + try: + year = int(parts[-1]) + if year < 2014 or year > 2030: + continue + except ValueError: + continue + + # Filter by years if specified + if years and year not in years: + continue + + # Check if this is a rate column + is_rate = "RATE" in parts + + # Extract crime type (everything before RATE/year) + if is_rate: + rate_idx = parts.index("RATE") + crime_type_str = "_".join(parts[:rate_idx]) + else: + crime_type_str = "_".join(parts[:-1]) + + crime_type = _normalize_crime_type(crime_type_str) + + try: + numeric_value = Decimal(str(value)) + except (ValueError, TypeError): + continue + + if is_rate: + # This is a rate column - look for corresponding count + # We'll skip rate-only entries and create records from counts + continue + + # Find corresponding rate if available + rate_col = f"{crime_type_str}_RATE_{year}" + rate_value = row.get(rate_col) + rate_per_100k = None + if rate_value is not None: + with contextlib.suppress(ValueError, TypeError): + rate_per_100k = Decimal(str(rate_value)) + + records.append( + CrimeRecord( + neighbourhood_id=neighbourhood_id, + year=year, + crime_type=crime_type, + count=int(numeric_value), + rate_per_100k=rate_per_100k, + ) + ) + + logger.info(f"Parsed {len(records)} crime rate records") + return records + + def get_major_crime_indicators( + self, + years: list[int] | None = None, + ) -> list[CrimeRecord]: + """Fetch major crime indicators (detailed MCI data). + + MCI data contains incident-level records that need to be aggregated + by neighbourhood and year. + + Args: + years: Optional list of years to filter. + + Returns: + List of aggregated CrimeRecord objects. + """ + try: + raw_records = self._fetch_datastore_records(self.DATASETS["mci"]) + except (httpx.HTTPError, ValueError) as e: + logger.warning(f"Could not fetch MCI data: {e}") + return [] + + # Aggregate counts by neighbourhood, year, and crime type + aggregates: dict[tuple[int, int, CrimeType], int] = {} + + for row in raw_records: + # Extract neighbourhood ID + hood_id = ( + row.get("HOOD_158") + or row.get("HOOD_140") + or row.get("HOOD_ID") + or row.get("Hood_ID") + ) + if not hood_id: + continue + + try: + neighbourhood_id = int(hood_id) + except (ValueError, TypeError): + continue + + # Extract year from occurrence date + occ_year = row.get("OCC_YEAR") or row.get("REPORT_YEAR") + if not occ_year: + continue + + try: + year = int(occ_year) + if year < 2014 or year > 2030: + continue + except (ValueError, TypeError): + continue + + # Filter by years if specified + if years and year not in years: + continue + + # Extract crime type + mci_category = row.get("MCI_CATEGORY") or row.get("OFFENCE") or "" + crime_type = _normalize_crime_type(str(mci_category)) + + # Aggregate count + key = (neighbourhood_id, year, crime_type) + aggregates[key] = aggregates.get(key, 0) + 1 + + # Convert aggregates to CrimeRecord objects + records = [ + CrimeRecord( + neighbourhood_id=neighbourhood_id, + year=year, + crime_type=crime_type, + count=count, + rate_per_100k=None, # Would need population data to calculate + ) + for (neighbourhood_id, year, crime_type), count in aggregates.items() + ] + + logger.info(f"Parsed {len(records)} MCI records (aggregated)") + return records diff --git a/portfolio_app/toronto/schemas/__init__.py b/portfolio_app/toronto/schemas/__init__.py new file mode 100644 index 0000000..897f6c4 --- /dev/null +++ b/portfolio_app/toronto/schemas/__init__.py @@ -0,0 +1,42 @@ +"""Pydantic schemas for Toronto housing data validation.""" + +from .amenities import AmenityCount, AmenityRecord, AmenityType +from .cmhc import BedroomType, CMHCAnnualSurvey, CMHCRentalRecord, ReliabilityCode +from .dimensions import ( + CMHCZone, + Confidence, + ExpectedDirection, + Neighbourhood, + PolicyCategory, + PolicyEvent, + PolicyLevel, + TimeDimension, +) +from .neighbourhood import CensusRecord, CrimeRecord, CrimeType, NeighbourhoodRecord + +__all__ = [ + # CMHC + "CMHCRentalRecord", + "CMHCAnnualSurvey", + "BedroomType", + "ReliabilityCode", + # Dimensions + "TimeDimension", + "CMHCZone", + "Neighbourhood", + "PolicyEvent", + # Enums + "PolicyLevel", + "PolicyCategory", + "ExpectedDirection", + "Confidence", + # Neighbourhood data (Phase 3) + "NeighbourhoodRecord", + "CensusRecord", + "CrimeRecord", + "CrimeType", + # Amenities (Phase 3) + "AmenityType", + "AmenityRecord", + "AmenityCount", +] diff --git a/portfolio_app/toronto/schemas/amenities.py b/portfolio_app/toronto/schemas/amenities.py new file mode 100644 index 0000000..2922d61 --- /dev/null +++ b/portfolio_app/toronto/schemas/amenities.py @@ -0,0 +1,60 @@ +"""Pydantic schemas for Toronto amenities data. + +Includes schemas for parks, schools, childcare centres, and transit stops. +""" + +from decimal import Decimal +from enum import Enum + +from pydantic import BaseModel, Field + + +class AmenityType(str, Enum): + """Types of amenities tracked in the neighbourhood dashboard.""" + + PARK = "park" + SCHOOL = "school" + CHILDCARE = "childcare" + TRANSIT_STOP = "transit_stop" + LIBRARY = "library" + COMMUNITY_CENTRE = "community_centre" + HOSPITAL = "hospital" + + +class AmenityRecord(BaseModel): + """Amenity location record for a neighbourhood. + + Represents a single amenity (park, school, etc.) with its location + and associated neighbourhood. + """ + + neighbourhood_id: int = Field( + ge=1, le=200, description="Neighbourhood ID containing this amenity" + ) + amenity_type: AmenityType = Field(description="Type of amenity") + amenity_name: str = Field(max_length=200, description="Name of the amenity") + address: str | None = Field( + default=None, max_length=300, description="Street address" + ) + latitude: Decimal | None = Field( + default=None, ge=-90, le=90, description="Latitude (WGS84)" + ) + longitude: Decimal | None = Field( + default=None, ge=-180, le=180, description="Longitude (WGS84)" + ) + + model_config = {"str_strip_whitespace": True} + + +class AmenityCount(BaseModel): + """Aggregated amenity count for a neighbourhood. + + Used for dashboard metrics showing amenity density per neighbourhood. + """ + + neighbourhood_id: int = Field(ge=1, le=200, description="Neighbourhood ID") + amenity_type: AmenityType = Field(description="Type of amenity") + count: int = Field(ge=0, description="Number of amenities of this type") + year: int = Field(ge=2020, le=2030, description="Year of data snapshot") + + model_config = {"str_strip_whitespace": True} diff --git a/portfolio_app/toronto/schemas/cmhc.py b/portfolio_app/toronto/schemas/cmhc.py new file mode 100644 index 0000000..edd388b --- /dev/null +++ b/portfolio_app/toronto/schemas/cmhc.py @@ -0,0 +1,81 @@ +"""Pydantic schemas for CMHC rental market data.""" + +from decimal import Decimal +from enum import Enum + +from pydantic import BaseModel, Field + + +class BedroomType(str, Enum): + """CMHC bedroom type categories.""" + + BACHELOR = "Bachelor" + ONE_BED = "1 Bedroom" + TWO_BED = "2 Bedroom" + THREE_BED_PLUS = "3 Bedroom+" + TOTAL = "Total" + + +class ReliabilityCode(str, Enum): + """CMHC data reliability codes. + + Based on coefficient of variation (CV). + """ + + EXCELLENT = "a" # CV <= 2.5% + GOOD = "b" # 2.5% < CV <= 5% + FAIR = "c" # 5% < CV <= 10% + POOR = "d" # CV > 10% + SUPPRESSED = "**" # Sample too small + + +class CMHCRentalRecord(BaseModel): + """Schema for a single CMHC rental survey record. + + Represents rental data for one zone and bedroom type in one survey year. + """ + + survey_year: int = Field(ge=1990, description="Survey year (October snapshot)") + zone_code: str = Field(max_length=10, description="CMHC zone identifier") + zone_name: str = Field(max_length=100, description="Zone name") + bedroom_type: BedroomType = Field(description="Bedroom category") + universe: int | None = Field( + default=None, ge=0, description="Total rental units in zone" + ) + vacancy_rate: Decimal | None = Field( + default=None, ge=0, le=100, description="Vacancy rate (%)" + ) + vacancy_rate_reliability: ReliabilityCode | None = Field(default=None) + availability_rate: Decimal | None = Field( + default=None, ge=0, le=100, description="Availability rate (%)" + ) + average_rent: Decimal | None = Field( + default=None, ge=0, description="Average monthly rent ($)" + ) + average_rent_reliability: ReliabilityCode | None = Field(default=None) + median_rent: Decimal | None = Field( + default=None, ge=0, description="Median monthly rent ($)" + ) + rent_change_pct: Decimal | None = Field( + default=None, description="YoY rent change (%)" + ) + turnover_rate: Decimal | None = Field( + default=None, ge=0, le=100, description="Unit turnover rate (%)" + ) + + model_config = {"str_strip_whitespace": True} + + +class CMHCAnnualSurvey(BaseModel): + """Schema for a complete CMHC annual survey for Toronto. + + Contains all zone and bedroom type combinations for one survey year. + """ + + survey_year: int + records: list[CMHCRentalRecord] + + @property + def zone_count(self) -> int: + """Number of unique zones in survey.""" + return len({r.zone_code for r in self.records}) diff --git a/portfolio_app/toronto/schemas/dimensions.py b/portfolio_app/toronto/schemas/dimensions.py new file mode 100644 index 0000000..1eb71b8 --- /dev/null +++ b/portfolio_app/toronto/schemas/dimensions.py @@ -0,0 +1,103 @@ +"""Pydantic schemas for dimension tables.""" + +from datetime import date +from decimal import Decimal +from enum import Enum + +from pydantic import BaseModel, Field, HttpUrl + + +class PolicyLevel(str, Enum): + """Government level for policy events.""" + + FEDERAL = "federal" + PROVINCIAL = "provincial" + MUNICIPAL = "municipal" + + +class PolicyCategory(str, Enum): + """Policy event category.""" + + MONETARY = "monetary" + TAX = "tax" + REGULATORY = "regulatory" + SUPPLY = "supply" + ECONOMIC = "economic" + + +class ExpectedDirection(str, Enum): + """Expected price impact direction.""" + + BULLISH = "bullish" # Expected to increase prices + BEARISH = "bearish" # Expected to decrease prices + NEUTRAL = "neutral" # Uncertain or mixed impact + + +class Confidence(str, Enum): + """Confidence level in policy event data.""" + + HIGH = "high" + MEDIUM = "medium" + LOW = "low" + + +class TimeDimension(BaseModel): + """Schema for time dimension record.""" + + date_key: int = Field(description="Date key in YYYYMMDD format") + full_date: date + year: int = Field(ge=2000, le=2100) + month: int = Field(ge=1, le=12) + quarter: int = Field(ge=1, le=4) + month_name: str = Field(max_length=20) + is_month_start: bool = True + + +class CMHCZone(BaseModel): + """Schema for CMHC zone dimension.""" + + zone_code: str = Field(max_length=10) + zone_name: str = Field(max_length=100) + geometry_wkt: str | None = Field(default=None, description="WKT geometry string") + + +class Neighbourhood(BaseModel): + """Schema for City of Toronto neighbourhood dimension. + + Note: No FK to fact tables in V1 - reference overlay only. + """ + + neighbourhood_id: int = Field(ge=1, le=200) + name: str = Field(max_length=100) + geometry_wkt: str | None = Field(default=None) + population: int | None = Field(default=None, ge=0) + land_area_sqkm: Decimal | None = Field(default=None, ge=0) + pop_density_per_sqkm: Decimal | None = Field(default=None, ge=0) + pct_bachelors_or_higher: Decimal | None = Field(default=None, ge=0, le=100) + median_household_income: Decimal | None = Field(default=None, ge=0) + pct_owner_occupied: Decimal | None = Field(default=None, ge=0, le=100) + pct_renter_occupied: Decimal | None = Field(default=None, ge=0, le=100) + census_year: int = Field(default=2021, description="Census year for SCD tracking") + + +class PolicyEvent(BaseModel): + """Schema for policy event dimension. + + Used for time-series annotation. No causation claims. + """ + + event_date: date = Field(description="Date event was announced/occurred") + effective_date: date | None = Field( + default=None, description="Date policy took effect" + ) + level: PolicyLevel + category: PolicyCategory + title: str = Field(max_length=200, description="Short event title for display") + description: str | None = Field( + default=None, description="Longer description for tooltip" + ) + expected_direction: ExpectedDirection + source_url: HttpUrl | None = Field(default=None) + confidence: Confidence = Field(default=Confidence.MEDIUM) + + model_config = {"str_strip_whitespace": True} diff --git a/portfolio_app/toronto/schemas/neighbourhood.py b/portfolio_app/toronto/schemas/neighbourhood.py new file mode 100644 index 0000000..2a1c567 --- /dev/null +++ b/portfolio_app/toronto/schemas/neighbourhood.py @@ -0,0 +1,106 @@ +"""Pydantic schemas for Toronto neighbourhood data. + +Includes schemas for neighbourhood boundaries, census profiles, and crime statistics. +""" + +from decimal import Decimal +from enum import Enum +from typing import Any + +from pydantic import BaseModel, Field + + +class CrimeType(str, Enum): + """Major crime indicator types from Toronto Police data.""" + + ASSAULT = "assault" + AUTO_THEFT = "auto_theft" + BREAK_AND_ENTER = "break_and_enter" + HOMICIDE = "homicide" + ROBBERY = "robbery" + SHOOTING = "shooting" + THEFT_OVER = "theft_over" + THEFT_FROM_MOTOR_VEHICLE = "theft_from_motor_vehicle" + OTHER = "other" + + +class NeighbourhoodRecord(BaseModel): + """Schema for Toronto neighbourhood boundary data. + + Based on City of Toronto's 158 neighbourhoods dataset. + AREA_ID maps to neighbourhood_id for consistency with police data (Hood_ID). + """ + + area_id: int = Field(description="AREA_ID from Toronto Open Data (1-158)") + area_name: str = Field(max_length=100, description="Official neighbourhood name") + area_short_code: str | None = Field( + default=None, max_length=10, description="Short code (e.g., 'E01')" + ) + geometry: dict[str, Any] | None = Field( + default=None, description="GeoJSON geometry object" + ) + + model_config = {"str_strip_whitespace": True} + + +class CensusRecord(BaseModel): + """Census profile data for a neighbourhood. + + Contains demographic and socioeconomic indicators from Statistics Canada + census data, aggregated to the neighbourhood level. + """ + + neighbourhood_id: int = Field( + ge=1, le=200, description="Neighbourhood ID (AREA_ID)" + ) + census_year: int = Field(ge=2016, le=2030, description="Census year") + population: int | None = Field(default=None, ge=0, description="Total population") + population_density: Decimal | None = Field( + default=None, ge=0, description="Population per square kilometre" + ) + median_household_income: Decimal | None = Field( + default=None, ge=0, description="Median household income (CAD)" + ) + average_household_income: Decimal | None = Field( + default=None, ge=0, description="Average household income (CAD)" + ) + unemployment_rate: Decimal | None = Field( + default=None, ge=0, le=100, description="Unemployment rate percentage" + ) + pct_bachelors_or_higher: Decimal | None = Field( + default=None, ge=0, le=100, description="Percentage with bachelor's degree+" + ) + pct_owner_occupied: Decimal | None = Field( + default=None, ge=0, le=100, description="Percentage owner-occupied dwellings" + ) + pct_renter_occupied: Decimal | None = Field( + default=None, ge=0, le=100, description="Percentage renter-occupied dwellings" + ) + median_age: Decimal | None = Field( + default=None, ge=0, le=120, description="Median age of residents" + ) + average_dwelling_value: Decimal | None = Field( + default=None, ge=0, description="Average dwelling value (CAD)" + ) + + model_config = {"str_strip_whitespace": True} + + +class CrimeRecord(BaseModel): + """Crime statistics for a neighbourhood. + + Based on Toronto Police neighbourhood crime rates data. + Hood_ID in source data maps to neighbourhood_id (AREA_ID). + """ + + neighbourhood_id: int = Field( + ge=1, le=200, description="Neighbourhood ID (Hood_ID -> AREA_ID)" + ) + year: int = Field(ge=2014, le=2030, description="Year of crime statistics") + crime_type: CrimeType = Field(description="Type of crime (MCI category)") + count: int = Field(ge=0, description="Number of incidents") + rate_per_100k: Decimal | None = Field( + default=None, ge=0, description="Rate per 100,000 population" + ) + + model_config = {"str_strip_whitespace": True} diff --git a/portfolio_app/toronto/services/__init__.py b/portfolio_app/toronto/services/__init__.py new file mode 100644 index 0000000..4a1f0ae --- /dev/null +++ b/portfolio_app/toronto/services/__init__.py @@ -0,0 +1,33 @@ +"""Data service layer for Toronto neighbourhood dashboard.""" + +from .geometry_service import ( + get_cmhc_zones_geojson, + get_neighbourhoods_geojson, +) +from .neighbourhood_service import ( + get_amenities_data, + get_city_averages, + get_demographics_data, + get_housing_data, + get_neighbourhood_details, + get_neighbourhood_list, + get_overview_data, + get_rankings, + get_safety_data, +) + +__all__ = [ + # Neighbourhood data + "get_overview_data", + "get_housing_data", + "get_safety_data", + "get_demographics_data", + "get_amenities_data", + "get_neighbourhood_details", + "get_neighbourhood_list", + "get_rankings", + "get_city_averages", + # Geometry + "get_neighbourhoods_geojson", + "get_cmhc_zones_geojson", +] diff --git a/portfolio_app/toronto/services/geometry_service.py b/portfolio_app/toronto/services/geometry_service.py new file mode 100644 index 0000000..fec82aa --- /dev/null +++ b/portfolio_app/toronto/services/geometry_service.py @@ -0,0 +1,176 @@ +"""Service layer for generating GeoJSON from PostGIS geometry.""" + +import json +from functools import lru_cache +from typing import Any + +import pandas as pd +from sqlalchemy import text + +from portfolio_app.toronto.models import get_engine + + +def _execute_query(sql: str, params: dict[str, Any] | None = None) -> pd.DataFrame: + """Execute SQL query and return DataFrame.""" + engine = get_engine() + with engine.connect() as conn: + return pd.read_sql(text(sql), conn, params=params) + + +@lru_cache(maxsize=8) +def get_neighbourhoods_geojson(year: int = 2021) -> dict[str, Any]: + """Get GeoJSON FeatureCollection for all neighbourhoods. + + Queries mart_neighbourhood_overview for geometries and basic properties. + + Args: + year: Year to query for joining properties. + + Returns: + GeoJSON FeatureCollection dictionary. + """ + # Query geometries with ST_AsGeoJSON + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + ST_AsGeoJSON(geometry)::json as geom, + population, + livability_score + FROM public_marts.mart_neighbourhood_overview + WHERE year = :year + AND geometry IS NOT NULL + """ + + try: + df = _execute_query(sql, {"year": year}) + except Exception: + # Table might not exist or have data yet + return _empty_geojson() + + if df.empty: + return _empty_geojson() + + # Build GeoJSON features + features = [] + for _, row in df.iterrows(): + geom = row["geom"] + if geom is None: + continue + + # Handle geometry that might be a string or dict + if isinstance(geom, str): + geom = json.loads(geom) + + feature = { + "type": "Feature", + "id": row["neighbourhood_id"], + "properties": { + "neighbourhood_id": int(row["neighbourhood_id"]), + "neighbourhood_name": row["neighbourhood_name"], + "population": int(row["population"]) + if pd.notna(row["population"]) + else None, + "livability_score": float(row["livability_score"]) + if pd.notna(row["livability_score"]) + else None, + }, + "geometry": geom, + } + features.append(feature) + + return { + "type": "FeatureCollection", + "features": features, + } + + +@lru_cache(maxsize=4) +def get_cmhc_zones_geojson() -> dict[str, Any]: + """Get GeoJSON FeatureCollection for CMHC zones. + + Queries dim_cmhc_zone for zone geometries. + + Returns: + GeoJSON FeatureCollection dictionary. + """ + sql = """ + SELECT + zone_code, + zone_name, + ST_AsGeoJSON(geometry)::json as geom + FROM dim_cmhc_zone + WHERE geometry IS NOT NULL + """ + + try: + df = _execute_query(sql, {}) + except Exception: + return _empty_geojson() + + if df.empty: + return _empty_geojson() + + features = [] + for _, row in df.iterrows(): + geom = row["geom"] + if geom is None: + continue + + if isinstance(geom, str): + geom = json.loads(geom) + + feature = { + "type": "Feature", + "id": row["zone_code"], + "properties": { + "zone_code": row["zone_code"], + "zone_name": row["zone_name"], + }, + "geometry": geom, + } + features.append(feature) + + return { + "type": "FeatureCollection", + "features": features, + } + + +def get_neighbourhood_geometry(neighbourhood_id: int) -> dict[str, Any] | None: + """Get GeoJSON geometry for a single neighbourhood. + + Args: + neighbourhood_id: The neighbourhood ID. + + Returns: + GeoJSON geometry dict, or None if not found. + """ + sql = """ + SELECT ST_AsGeoJSON(geometry)::json as geom + FROM dim_neighbourhood + WHERE neighbourhood_id = :neighbourhood_id + AND geometry IS NOT NULL + """ + + try: + df = _execute_query(sql, {"neighbourhood_id": neighbourhood_id}) + except Exception: + return None + + if df.empty: + return None + + geom = df.iloc[0]["geom"] + if isinstance(geom, str): + result: dict[str, Any] = json.loads(geom) + return result + return dict(geom) if geom is not None else None + + +def _empty_geojson() -> dict[str, Any]: + """Return an empty GeoJSON FeatureCollection.""" + return { + "type": "FeatureCollection", + "features": [], + } diff --git a/portfolio_app/toronto/services/neighbourhood_service.py b/portfolio_app/toronto/services/neighbourhood_service.py new file mode 100644 index 0000000..cefe606 --- /dev/null +++ b/portfolio_app/toronto/services/neighbourhood_service.py @@ -0,0 +1,393 @@ +"""Service layer for querying neighbourhood data from dbt marts.""" + +import logging +from functools import lru_cache +from typing import Any + +import pandas as pd +from sqlalchemy import text + +from portfolio_app.toronto.models import get_engine + +logger = logging.getLogger(__name__) + + +def _execute_query(sql: str, params: dict[str, Any] | None = None) -> pd.DataFrame: + """Execute SQL query and return DataFrame. + + Args: + sql: SQL query string. + params: Query parameters. + + Returns: + pandas DataFrame with results, or empty DataFrame on error. + """ + try: + engine = get_engine() + with engine.connect() as conn: + return pd.read_sql(text(sql), conn, params=params) + except Exception as e: + logger.error(f"Query failed: {e}") + logger.debug(f"Failed SQL: {sql}") + logger.debug(f"Params: {params}") + return pd.DataFrame() + + +def get_overview_data(year: int = 2021) -> pd.DataFrame: + """Get overview data for all neighbourhoods. + + Queries mart_neighbourhood_overview for livability scores and components. + + Args: + year: Census year to query. + + Returns: + DataFrame with columns: neighbourhood_id, neighbourhood_name, + livability_score, safety_score, affordability_score, amenity_score, + population, median_household_income, etc. + """ + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + year, + population, + median_household_income, + livability_score, + safety_score, + affordability_score, + amenity_score, + crime_rate_per_100k, + rent_to_income_pct, + avg_rent_2bed, + total_amenities_per_1000 + FROM public_marts.mart_neighbourhood_overview + WHERE year = :year + ORDER BY livability_score DESC NULLS LAST + """ + return _execute_query(sql, {"year": year}) + + +def get_housing_data(year: int = 2021) -> pd.DataFrame: + """Get housing data for all neighbourhoods. + + Queries mart_neighbourhood_housing for affordability metrics. + + Args: + year: Year to query. + + Returns: + DataFrame with columns: neighbourhood_id, neighbourhood_name, + avg_rent_2bed, vacancy_rate, rent_to_income_pct, affordability_index, etc. + """ + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + year, + pct_owner_occupied, + pct_renter_occupied, + average_dwelling_value, + median_household_income, + avg_rent_bachelor, + avg_rent_1bed, + avg_rent_2bed, + avg_rent_3bed, + vacancy_rate, + total_rental_units, + rent_to_income_pct, + is_affordable, + affordability_index, + rent_yoy_change_pct, + income_quintile + FROM public_marts.mart_neighbourhood_housing + WHERE year = :year + ORDER BY affordability_index ASC NULLS LAST + """ + return _execute_query(sql, {"year": year}) + + +def get_safety_data(year: int = 2021) -> pd.DataFrame: + """Get safety/crime data for all neighbourhoods. + + Queries mart_neighbourhood_safety for crime statistics. + + Args: + year: Year to query. + + Returns: + DataFrame with columns: neighbourhood_id, neighbourhood_name, + total_crime_rate, violent_crimes, property_crimes, etc. + """ + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + year, + total_incidents as total_crimes, + crime_rate_per_100k as total_crime_rate, + assault_count + robbery_count + homicide_count as violent_crimes, + break_enter_count + auto_theft_count as property_crimes, + theft_over_count as theft_crimes, + crime_yoy_change_pct + FROM public_marts.mart_neighbourhood_safety + WHERE year = :year + ORDER BY crime_rate_per_100k ASC NULLS LAST + """ + return _execute_query(sql, {"year": year}) + + +def get_demographics_data(year: int = 2021) -> pd.DataFrame: + """Get demographic data for all neighbourhoods. + + Queries mart_neighbourhood_demographics for population/income metrics. + + Args: + year: Census year to query. + + Returns: + DataFrame with columns: neighbourhood_id, neighbourhood_name, + population, median_age, median_income, diversity_index, etc. + """ + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + year, + population, + population_density, + median_household_income, + average_household_income, + income_quintile, + income_index, + median_age, + age_index, + pct_owner_occupied, + pct_renter_occupied, + education_bachelors_pct as pct_bachelors_or_higher, + unemployment_rate, + tenure_diversity_index as diversity_index + FROM public_marts.mart_neighbourhood_demographics + WHERE year = :year + ORDER BY population DESC NULLS LAST + """ + return _execute_query(sql, {"year": year}) + + +def get_amenities_data(year: int = 2021) -> pd.DataFrame: + """Get amenities data for all neighbourhoods. + + Queries mart_neighbourhood_amenities for parks, schools, transit. + + Args: + year: Year to query. + + Returns: + DataFrame with columns: neighbourhood_id, neighbourhood_name, + amenity_score, parks_per_1000, schools_per_1000, etc. + """ + sql = """ + SELECT + neighbourhood_id, + neighbourhood_name, + year, + parks_count as park_count, + parks_per_1000, + schools_count as school_count, + schools_per_1000, + transit_count as childcare_count, + transit_per_1000 as childcare_per_1000, + total_amenities, + total_amenities_per_1000, + amenity_index as amenity_score, + amenity_tier as amenity_rank + FROM public_marts.mart_neighbourhood_amenities + WHERE year = :year + ORDER BY amenity_index DESC NULLS LAST + """ + return _execute_query(sql, {"year": year}) + + +def get_neighbourhood_details( + neighbourhood_id: int, year: int = 2021 +) -> dict[str, Any]: + """Get detailed data for a single neighbourhood. + + Combines data from all mart tables for a complete neighbourhood profile. + + Args: + neighbourhood_id: The neighbourhood ID. + year: Year to query. + + Returns: + Dictionary with all metrics for the neighbourhood. + """ + sql = """ + SELECT + o.neighbourhood_id, + o.neighbourhood_name, + o.year, + o.population, + o.median_household_income, + o.livability_score, + o.safety_score, + o.affordability_score, + o.amenity_score, + s.total_crimes, + s.crime_rate_per_100k, + s.violent_crime_rate, + s.property_crime_rate, + h.avg_rent_2bed, + h.vacancy_rate, + h.rent_to_income_pct, + h.affordability_index, + h.pct_owner_occupied, + h.pct_renter_occupied, + d.median_age, + d.diversity_index, + d.unemployment_rate, + d.pct_bachelors_or_higher, + a.park_count, + a.school_count, + a.total_amenities + FROM public_marts.mart_neighbourhood_overview o + LEFT JOIN public_marts.mart_neighbourhood_safety s + ON o.neighbourhood_id = s.neighbourhood_id + AND o.year = s.year + LEFT JOIN public_marts.mart_neighbourhood_housing h + ON o.neighbourhood_id = h.neighbourhood_id + AND o.year = h.year + LEFT JOIN public_marts.mart_neighbourhood_demographics d + ON o.neighbourhood_id = d.neighbourhood_id + AND o.year = d.census_year + LEFT JOIN public_marts.mart_neighbourhood_amenities a + ON o.neighbourhood_id = a.neighbourhood_id + AND o.year = a.year + WHERE o.neighbourhood_id = :neighbourhood_id + AND o.year = :year + """ + df = _execute_query(sql, {"neighbourhood_id": neighbourhood_id, "year": year}) + + if df.empty: + return {} + + return {str(k): v for k, v in df.iloc[0].to_dict().items()} + + +@lru_cache(maxsize=32) +def get_neighbourhood_list(year: int = 2021) -> list[dict[str, Any]]: + """Get list of all neighbourhoods for dropdown selectors. + + Args: + year: Year to query. + + Returns: + List of dicts with neighbourhood_id, name, and population. + """ + sql = """ + SELECT DISTINCT + neighbourhood_id, + neighbourhood_name, + population + FROM public_marts.mart_neighbourhood_overview + WHERE year = :year + ORDER BY neighbourhood_name + """ + df = _execute_query(sql, {"year": year}) + if df.empty: + return [] + return list(df.to_dict("records")) # type: ignore[arg-type] + + +def get_rankings( + metric: str, + year: int = 2021, + top_n: int = 10, + ascending: bool = True, +) -> pd.DataFrame: + """Get top/bottom neighbourhoods for a specific metric. + + Args: + metric: Column name to rank by. + year: Year to query. + top_n: Number of top and bottom records. + ascending: If True, rank from lowest to highest (good for crime, rent). + + Returns: + DataFrame with top and bottom neighbourhoods. + """ + # Map metrics to their source tables + table_map = { + "livability_score": "public_marts.mart_neighbourhood_overview", + "safety_score": "public_marts.mart_neighbourhood_overview", + "affordability_score": "public_marts.mart_neighbourhood_overview", + "amenity_score": "public_marts.mart_neighbourhood_overview", + "crime_rate_per_100k": "public_marts.mart_neighbourhood_safety", + "total_crime_rate": "public_marts.mart_neighbourhood_safety", + "avg_rent_2bed": "public_marts.mart_neighbourhood_housing", + "affordability_index": "public_marts.mart_neighbourhood_housing", + "population": "public_marts.mart_neighbourhood_demographics", + "median_household_income": "public_marts.mart_neighbourhood_demographics", + } + + table = table_map.get(metric, "public_marts.mart_neighbourhood_overview") + year_col = "census_year" if "demographics" in table else "year" + + order = "ASC" if ascending else "DESC" + reverse_order = "DESC" if ascending else "ASC" + + sql = f""" + ( + SELECT neighbourhood_id, neighbourhood_name, {metric}, 'bottom' as rank_group + FROM {table} + WHERE {year_col} = :year AND {metric} IS NOT NULL + ORDER BY {metric} {order} + LIMIT :top_n + ) + UNION ALL + ( + SELECT neighbourhood_id, neighbourhood_name, {metric}, 'top' as rank_group + FROM {table} + WHERE {year_col} = :year AND {metric} IS NOT NULL + ORDER BY {metric} {reverse_order} + LIMIT :top_n + ) + """ + return _execute_query(sql, {"year": year, "top_n": top_n}) + + +def get_city_averages(year: int = 2021) -> dict[str, Any]: + """Get city-wide average metrics. + + Args: + year: Year to query. + + Returns: + Dictionary with city averages for key metrics. + """ + sql = """ + SELECT + AVG(livability_score) as avg_livability_score, + AVG(safety_score) as avg_safety_score, + AVG(affordability_score) as avg_affordability_score, + AVG(amenity_score) as avg_amenity_score, + SUM(population) as total_population, + AVG(median_household_income) as avg_median_income, + AVG(crime_rate_per_100k) as avg_crime_rate, + AVG(avg_rent_2bed) as avg_rent_2bed, + AVG(rent_to_income_pct) as avg_rent_to_income + FROM public_marts.mart_neighbourhood_overview + WHERE year = :year + """ + df = _execute_query(sql, {"year": year}) + + if df.empty: + return {} + + result: dict[str, Any] = {str(k): v for k, v in df.iloc[0].to_dict().items()} + # Round numeric values + for key, value in result.items(): + if pd.notna(value) and isinstance(value, float): + result[key] = round(value, 2) + + return result diff --git a/portfolio_app/utils/__init__.py b/portfolio_app/utils/__init__.py new file mode 100644 index 0000000..c823953 --- /dev/null +++ b/portfolio_app/utils/__init__.py @@ -0,0 +1,9 @@ +"""Utility modules for the portfolio app.""" + +from portfolio_app.utils.markdown_loader import ( + get_all_articles, + get_article, + render_markdown, +) + +__all__ = ["get_all_articles", "get_article", "render_markdown"] diff --git a/portfolio_app/utils/markdown_loader.py b/portfolio_app/utils/markdown_loader.py new file mode 100644 index 0000000..7070ed6 --- /dev/null +++ b/portfolio_app/utils/markdown_loader.py @@ -0,0 +1,109 @@ +"""Markdown article loader with frontmatter support.""" + +from pathlib import Path +from typing import TypedDict + +import frontmatter +import markdown +from markdown.extensions.codehilite import CodeHiliteExtension +from markdown.extensions.fenced_code import FencedCodeExtension +from markdown.extensions.tables import TableExtension +from markdown.extensions.toc import TocExtension + +# Content directory (relative to this file's package) +CONTENT_DIR = Path(__file__).parent.parent / "content" / "blog" + + +class ArticleMeta(TypedDict): + """Article metadata from frontmatter.""" + + slug: str + title: str + date: str + description: str + tags: list[str] + status: str # "published" or "draft" + + +class Article(TypedDict): + """Full article with metadata and content.""" + + meta: ArticleMeta + content: str + html: str + + +def render_markdown(content: str) -> str: + """Convert markdown to HTML with syntax highlighting. + + Args: + content: Raw markdown string. + + Returns: + HTML string with syntax-highlighted code blocks. + """ + md = markdown.Markdown( + extensions=[ + FencedCodeExtension(), + CodeHiliteExtension(css_class="highlight", guess_lang=False), + TableExtension(), + TocExtension(permalink=True), + "nl2br", + ] + ) + return str(md.convert(content)) + + +def get_article(slug: str) -> Article | None: + """Load a single article by slug. + + Args: + slug: Article slug (filename without .md extension). + + Returns: + Article dict or None if not found. + """ + filepath = CONTENT_DIR / f"{slug}.md" + if not filepath.exists(): + return None + + post = frontmatter.load(filepath) + + meta: ArticleMeta = { + "slug": slug, + "title": post.get("title", slug.replace("-", " ").title()), + "date": str(post.get("date", "")), + "description": post.get("description", ""), + "tags": post.get("tags", []), + "status": post.get("status", "published"), + } + + return { + "meta": meta, + "content": post.content, + "html": render_markdown(post.content), + } + + +def get_all_articles(include_drafts: bool = False) -> list[Article]: + """Load all articles from the content directory. + + Args: + include_drafts: If True, include articles with status="draft". + + Returns: + List of articles sorted by date (newest first). + """ + if not CONTENT_DIR.exists(): + return [] + + articles: list[Article] = [] + for filepath in CONTENT_DIR.glob("*.md"): + slug = filepath.stem + article = get_article(slug) + if article and (include_drafts or article["meta"]["status"] == "published"): + articles.append(article) + + # Sort by date descending + articles.sort(key=lambda a: a["meta"]["date"], reverse=True) + return articles diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ba3f5db --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,165 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "portfolio" +version = "0.1.0" +description = "Analytics Portfolio - Data engineering and visualization showcase" +readme = "README.md" +license = {text = "MIT"} +requires-python = ">=3.11" +authors = [ + {name = "Leo Miranda"} +] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] +dependencies = [ + # Database + "sqlalchemy>=2.0.45", + "psycopg2-binary>=2.9", + "geoalchemy2>=0.15", + + # Validation + "pydantic>=2.10", + "pydantic-settings>=2.6", + + # Data Processing + "pandas>=2.3", + "geopandas>=1.1", + "shapely>=2.0", + "pyproj>=3.6", + "statsmodels>=0.14", + + # Visualization + "dash>=3.3", + "plotly>=6.5", + "dash-mantine-components>=2.4", + "dash-iconify>=0.1", + + # PDF Parsing + "pdfplumber>=0.11", + "tabula-py>=2.9", + + # Utilities + "python-dotenv>=1.0", + "httpx>=0.28", + + # Blog/Markdown + "python-frontmatter>=1.1", + "markdown>=3.5", + "pygments>=2.17", +] + +[project.optional-dependencies] +dev = [ + # Testing + "pytest>=8.3", + "pytest-cov>=6.0", + "pytest-asyncio>=0.24", + + # Linting & Formatting + "ruff>=0.8", + "mypy>=1.14", + + # Pre-commit + "pre-commit>=4.0", + + # Jupyter + "jupyter>=1.0", + "ipykernel>=6.29", + + # Type stubs + "pandas-stubs", + "types-requests", +] +dbt = [ + "dbt-postgres>=1.9", +] + +[project.scripts] +portfolio = "portfolio_app.app:main" + +[tool.setuptools.packages.find] +where = ["."] +include = ["portfolio_app*"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_functions = ["test_*"] +addopts = [ + "-v", + "--tb=short", + "--strict-markers", +] +markers = [ + "slow: marks tests as slow (deselect with '-m \"not slow\"')", + "integration: marks tests as integration tests", +] + +[tool.ruff] +target-version = "py311" +line-length = 88 +exclude = [ + ".git", + ".venv", + "__pycache__", + "build", + "dist", + ".ruff_cache", + "dbt/target", +] + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade + "SIM", # flake8-simplify +] +ignore = [ + "E501", # line too long (handled by formatter) +] + +[tool.ruff.lint.isort] +known-first-party = ["portfolio_app"] + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false + +[tool.mypy] +python_version = "3.11" +strict = true +warn_return_any = true +warn_unused_ignores = false +disallow_untyped_defs = true +plugins = ["pydantic.mypy"] + +[[tool.mypy.overrides]] +module = [ + "dash.*", + "dash_mantine_components.*", + "dash_iconify.*", + "plotly.*", + "geopandas.*", + "shapely.*", + "pdfplumber.*", + "tabula.*", + "pydantic_settings.*", + "frontmatter.*", + "markdown.*", +] +ignore_missing_imports = true diff --git a/scripts/data/__init__.py b/scripts/data/__init__.py new file mode 100644 index 0000000..ef9b2c8 --- /dev/null +++ b/scripts/data/__init__.py @@ -0,0 +1 @@ +"""Data loading scripts for the portfolio app.""" diff --git a/scripts/data/load_toronto_data.py b/scripts/data/load_toronto_data.py new file mode 100644 index 0000000..3cc7495 --- /dev/null +++ b/scripts/data/load_toronto_data.py @@ -0,0 +1,423 @@ +#!/usr/bin/env python3 +"""Load Toronto neighbourhood data into the database. + +Usage: + python scripts/data/load_toronto_data.py [OPTIONS] + +Options: + --skip-fetch Skip API fetching, only run dbt + --skip-dbt Skip dbt run, only load data + --dry-run Show what would be done without executing + -v, --verbose Enable verbose logging + +This script orchestrates: +1. Fetching data from Toronto Open Data and CMHC APIs +2. Loading data into PostgreSQL fact tables +3. Running dbt to transform staging -> intermediate -> marts + +Exit codes: + 0 = Success + 1 = Error +""" + +import argparse +import logging +import subprocess +import sys +from datetime import date +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv + +# Add project root to path +PROJECT_ROOT = Path(__file__).parent.parent.parent + +# Load .env file so dbt can access POSTGRES_* environment variables +load_dotenv(PROJECT_ROOT / ".env") +sys.path.insert(0, str(PROJECT_ROOT)) + +from portfolio_app.toronto.loaders import ( # noqa: E402 + get_session, + load_amenities, + load_census_data, + load_crime_data, + load_neighbourhoods, + load_statcan_cmhc_data, + load_time_dimension, +) +from portfolio_app.toronto.parsers import ( # noqa: E402 + TorontoOpenDataParser, + TorontoPoliceParser, +) +from portfolio_app.toronto.parsers.statcan_cmhc import ( # noqa: E402 + fetch_toronto_rental_data, +) +from portfolio_app.toronto.schemas import Neighbourhood # noqa: E402 + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="%H:%M:%S", +) +logger = logging.getLogger(__name__) + + +class DataPipeline: + """Orchestrates data loading from APIs to database to dbt.""" + + def __init__(self, dry_run: bool = False, verbose: bool = False): + self.dry_run = dry_run + self.verbose = verbose + self.stats: dict[str, int] = {} + + if verbose: + logging.getLogger().setLevel(logging.DEBUG) + + def fetch_and_load(self) -> bool: + """Fetch data from APIs and load into database. + + Returns: + True if successful, False otherwise. + """ + logger.info("Starting data fetch and load pipeline...") + + try: + with get_session() as session: + # 1. Load time dimension first (for date keys) + self._load_time_dimension(session) + + # 2. Load neighbourhoods (required for foreign keys) + self._load_neighbourhoods(session) + + # 3. Load census data + self._load_census(session) + + # 4. Load crime data + self._load_crime(session) + + # 5. Load amenities + self._load_amenities(session) + + # 6. Load CMHC rental data from StatCan + self._load_rentals(session) + + session.commit() + logger.info("All data committed to database") + + self._print_stats() + return True + + except Exception as e: + logger.error(f"Pipeline failed: {e}") + if self.verbose: + import traceback + + traceback.print_exc() + return False + + def _load_time_dimension(self, session: Any) -> None: + """Load time dimension with date range for dashboard.""" + logger.info("Loading time dimension...") + + if self.dry_run: + logger.info( + " [DRY RUN] Would load time dimension 2019-01-01 to 2025-12-01" + ) + return + + count = load_time_dimension( + start_date=date(2019, 1, 1), + end_date=date(2025, 12, 1), + session=session, + ) + self.stats["time_dimension"] = count + logger.info(f" Loaded {count} time dimension records") + + def _load_neighbourhoods(self, session: Any) -> None: + """Fetch and load neighbourhood boundaries.""" + logger.info("Fetching neighbourhoods from Toronto Open Data...") + + if self.dry_run: + logger.info(" [DRY RUN] Would fetch and load neighbourhoods") + return + + import json + + parser = TorontoOpenDataParser() + raw_neighbourhoods = parser.get_neighbourhoods() + + # Convert NeighbourhoodRecord to Neighbourhood schema + neighbourhoods = [] + for n in raw_neighbourhoods: + # Convert GeoJSON geometry dict to WKT if present + geometry_wkt = None + if n.geometry: + # Store as GeoJSON string for PostGIS ST_GeomFromGeoJSON + geometry_wkt = json.dumps(n.geometry) + + neighbourhood = Neighbourhood( + neighbourhood_id=n.area_id, + name=n.area_name, + geometry_wkt=geometry_wkt, + population=None, # Will be filled from census data + land_area_sqkm=None, + pop_density_per_sqkm=None, + census_year=2021, + ) + neighbourhoods.append(neighbourhood) + + count = load_neighbourhoods(neighbourhoods, session) + self.stats["neighbourhoods"] = count + logger.info(f" Loaded {count} neighbourhoods") + + def _load_census(self, session: Any) -> None: + """Fetch and load census profile data.""" + logger.info("Fetching census profiles from Toronto Open Data...") + + if self.dry_run: + logger.info(" [DRY RUN] Would fetch and load census data") + return + + parser = TorontoOpenDataParser() + census_records = parser.get_census_profiles(year=2021) + + if not census_records: + logger.warning(" No census records fetched") + return + + count = load_census_data(census_records, session) + self.stats["census"] = count + logger.info(f" Loaded {count} census records") + + def _load_crime(self, session: Any) -> None: + """Fetch and load crime statistics.""" + logger.info("Fetching crime data from Toronto Police Service...") + + if self.dry_run: + logger.info(" [DRY RUN] Would fetch and load crime data") + return + + parser = TorontoPoliceParser() + crime_records = parser.get_crime_rates() + + if not crime_records: + logger.warning(" No crime records fetched") + return + + count = load_crime_data(crime_records, session) + self.stats["crime"] = count + logger.info(f" Loaded {count} crime records") + + def _load_amenities(self, session: Any) -> None: + """Fetch and load amenity data (parks, schools, childcare).""" + logger.info("Fetching amenities from Toronto Open Data...") + + if self.dry_run: + logger.info(" [DRY RUN] Would fetch and load amenity data") + return + + parser = TorontoOpenDataParser() + total_count = 0 + + # Fetch parks + try: + parks = parser.get_parks() + if parks: + count = load_amenities(parks, year=2024, session=session) + total_count += count + logger.info(f" Loaded {count} park amenities") + except Exception as e: + logger.warning(f" Failed to load parks: {e}") + + # Fetch schools + try: + schools = parser.get_schools() + if schools: + count = load_amenities(schools, year=2024, session=session) + total_count += count + logger.info(f" Loaded {count} school amenities") + except Exception as e: + logger.warning(f" Failed to load schools: {e}") + + # Fetch childcare centres + try: + childcare = parser.get_childcare_centres() + if childcare: + count = load_amenities(childcare, year=2024, session=session) + total_count += count + logger.info(f" Loaded {count} childcare amenities") + except Exception as e: + logger.warning(f" Failed to load childcare: {e}") + + self.stats["amenities"] = total_count + + def _load_rentals(self, session: Any) -> None: + """Fetch and load CMHC rental data from StatCan.""" + logger.info("Fetching CMHC rental data from Statistics Canada...") + + if self.dry_run: + logger.info(" [DRY RUN] Would fetch and load CMHC rental data") + return + + try: + # Fetch rental data (2014-present) + rental_records = fetch_toronto_rental_data(start_year=2014) + + if not rental_records: + logger.warning(" No rental records fetched") + return + + count = load_statcan_cmhc_data(rental_records, session) + self.stats["rentals"] = count + logger.info(f" Loaded {count} CMHC rental records") + except Exception as e: + logger.warning(f" Failed to load CMHC rental data: {e}") + if self.verbose: + import traceback + + traceback.print_exc() + + def run_dbt(self) -> bool: + """Run dbt to transform data. + + Returns: + True if successful, False otherwise. + """ + logger.info("Running dbt transformations...") + + dbt_project_dir = PROJECT_ROOT / "dbt" + venv_dbt = PROJECT_ROOT / ".venv" / "bin" / "dbt" + + # Use venv dbt if available, otherwise fall back to system dbt + dbt_cmd = str(venv_dbt) if venv_dbt.exists() else "dbt" + + if not dbt_project_dir.exists(): + logger.error(f"dbt project directory not found: {dbt_project_dir}") + return False + + if self.dry_run: + logger.info(" [DRY RUN] Would run: dbt deps") + logger.info(" [DRY RUN] Would run: dbt run") + logger.info(" [DRY RUN] Would run: dbt test") + return True + + try: + # Install dbt packages if needed + logger.info(" Running dbt deps...") + result = subprocess.run( + [dbt_cmd, "deps", "--profiles-dir", str(dbt_project_dir)], + cwd=dbt_project_dir, + capture_output=True, + text=True, + ) + + if result.returncode != 0: + logger.error(f"dbt deps failed:\n{result.stdout}\n{result.stderr}") + return False + + # Run dbt models + logger.info(" Running dbt run...") + result = subprocess.run( + [dbt_cmd, "run", "--profiles-dir", str(dbt_project_dir)], + cwd=dbt_project_dir, + capture_output=True, + text=True, + ) + + if result.returncode != 0: + logger.error(f"dbt run failed:\n{result.stdout}\n{result.stderr}") + return False + + logger.info(" dbt run completed successfully") + + # Run dbt tests + logger.info(" Running dbt test...") + result = subprocess.run( + [dbt_cmd, "test", "--profiles-dir", str(dbt_project_dir)], + cwd=dbt_project_dir, + capture_output=True, + text=True, + ) + + if result.returncode != 0: + logger.warning( + f"dbt test had failures:\n{result.stdout}\n{result.stderr}" + ) + # Don't fail on test failures, just warn + else: + logger.info(" dbt test completed successfully") + + return True + + except FileNotFoundError: + logger.error( + "dbt not found in PATH. Install with: pip install dbt-postgres" + ) + return False + except Exception as e: + logger.error(f"dbt execution failed: {e}") + return False + + def _print_stats(self) -> None: + """Print loading statistics.""" + if not self.stats: + return + + logger.info("Loading statistics:") + for key, count in self.stats.items(): + logger.info(f" {key}: {count} records") + + +def main() -> int: + """Main entry point for the data loading script.""" + parser = argparse.ArgumentParser( + description="Load Toronto neighbourhood data into the database", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + + parser.add_argument( + "--skip-fetch", + action="store_true", + help="Skip API fetching, only run dbt", + ) + parser.add_argument( + "--skip-dbt", + action="store_true", + help="Skip dbt run, only load data", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Show what would be done without executing", + ) + parser.add_argument( + "-v", + "--verbose", + action="store_true", + help="Enable verbose logging", + ) + + args = parser.parse_args() + + if args.skip_fetch and args.skip_dbt: + logger.error("Cannot skip both fetch and dbt - nothing to do") + return 1 + + pipeline = DataPipeline(dry_run=args.dry_run, verbose=args.verbose) + + # Execute pipeline stages + if not args.skip_fetch and not pipeline.fetch_and_load(): + return 1 + + if not args.skip_dbt and not pipeline.run_dbt(): + return 1 + + logger.info("Pipeline completed successfully!") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/data/seed_amenity_data.py b/scripts/data/seed_amenity_data.py new file mode 100644 index 0000000..cdeb3ea --- /dev/null +++ b/scripts/data/seed_amenity_data.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +"""Seed sample data for development/testing. + +This script: +- Populates fact_amenities with sample data +- Updates dim_neighbourhood with population from fact_census +- Seeds median_age in fact_census where missing +- Seeds census housing columns (tenure, income, dwelling value) +- Seeds housing mart data (rent, affordability) +- Seeds overview mart data (safety_score, population) +- Runs dbt to rebuild the marts + +Usage: + python scripts/data/seed_amenity_data.py + +Run this after load_toronto_data.py to ensure notebooks have data. +""" + +import os +import random +import subprocess +import sys +from pathlib import Path + +from dotenv import load_dotenv +from sqlalchemy import create_engine, text + +PROJECT_ROOT = Path(__file__).parent.parent.parent +load_dotenv(PROJECT_ROOT / ".env") + +DATABASE_URL = os.environ.get("DATABASE_URL") +if not DATABASE_URL: + print("ERROR: DATABASE_URL not set in .env") + sys.exit(1) + + +def seed_amenities() -> int: + """Insert sample amenity data for all neighbourhoods.""" + engine = create_engine(DATABASE_URL) + + with engine.connect() as conn: + result = conn.execute( + text("SELECT neighbourhood_id FROM public.dim_neighbourhood") + ) + neighbourhood_ids = [row[0] for row in result] + + print(f"Found {len(neighbourhood_ids)} neighbourhoods") + + amenity_types = [ + "Parks", + "Schools", + "Transit Stops", + "Libraries", + "Community Centres", + "Recreation", + ] + year = 2024 + + with engine.begin() as conn: + conn.execute(text("DELETE FROM public.fact_amenities")) + + total = 0 + for n_id in neighbourhood_ids: + for amenity_type in amenity_types: + count = random.randint(1, 50) + conn.execute( + text( + """ + INSERT INTO public.fact_amenities + (neighbourhood_id, amenity_type, count, year) + VALUES (:neighbourhood_id, :amenity_type, :count, :year) + """ + ), + { + "neighbourhood_id": n_id, + "amenity_type": amenity_type, + "count": count, + "year": year, + }, + ) + total += 1 + + print(f"Inserted {total} amenity records") + return total + + +def update_population() -> int: + """Update dim_neighbourhood with population from fact_census.""" + engine = create_engine(DATABASE_URL) + + with engine.begin() as conn: + result = conn.execute( + text( + """ + UPDATE public.dim_neighbourhood dn + SET population = fc.population + FROM public.fact_census fc + WHERE dn.neighbourhood_id = fc.neighbourhood_id + AND fc.census_year = 2021 + """ + ) + ) + count = int(result.rowcount) + + print(f"Updated {count} neighbourhoods with population") + return count + + +def seed_median_age() -> int: + """Seed median_age in fact_census where missing.""" + engine = create_engine(DATABASE_URL) + + with engine.begin() as conn: + result = conn.execute( + text("SELECT id FROM public.fact_census WHERE median_age IS NULL") + ) + null_ids = [row[0] for row in result] + + if not null_ids: + print("No NULL median_age values found") + return 0 + + for census_id in null_ids: + age = random.randint(30, 50) + conn.execute( + text("UPDATE public.fact_census SET median_age = :age WHERE id = :id"), + {"age": age, "id": census_id}, + ) + + print(f"Seeded median_age for {len(null_ids)} census records") + return len(null_ids) + + +def seed_census_housing() -> int: + """Seed housing columns in fact_census where missing.""" + engine = create_engine(DATABASE_URL) + + with engine.begin() as conn: + result = conn.execute( + text("SELECT id FROM public.fact_census WHERE pct_owner_occupied IS NULL") + ) + null_ids = [row[0] for row in result] + + if not null_ids: + print("No NULL census housing values found") + return 0 + + for census_id in null_ids: + conn.execute( + text( + """ + UPDATE public.fact_census SET + pct_owner_occupied = :owner, + pct_renter_occupied = :renter, + average_dwelling_value = :dwelling, + median_household_income = :income + WHERE id = :id + """ + ), + { + "id": census_id, + "owner": round(random.uniform(30, 80), 1), + "renter": round(random.uniform(20, 70), 1), + "dwelling": random.randint(400000, 1500000), + "income": random.randint(50000, 180000), + }, + ) + + print(f"Seeded census housing data for {len(null_ids)} records") + return len(null_ids) + + +def seed_housing_mart() -> int: + """Seed housing mart with rental and affordability data for multiple years.""" + engine = create_engine(DATABASE_URL) + total = 0 + + # First update existing NULL records + with engine.begin() as conn: + result = conn.execute( + text( + """ + SELECT neighbourhood_id, year + FROM public_marts.mart_neighbourhood_housing + WHERE avg_rent_2bed IS NULL + """ + ) + ) + rows = [dict(row._mapping) for row in result] + + for row in rows: + avg_rent = random.randint(1800, 3200) + income = random.randint(55000, 180000) + rent_to_income = round((avg_rent * 12 / income) * 100, 2) + affordability = round(rent_to_income / 30 * 100, 1) + + conn.execute( + text( + """ + UPDATE public_marts.mart_neighbourhood_housing SET + avg_rent_bachelor = :bachelor, + avg_rent_1bed = :onebed, + avg_rent_2bed = :twobed, + avg_rent_3bed = :threebed, + vacancy_rate = :vacancy, + rent_to_income_pct = :rent_income, + affordability_index = :afford_idx, + is_affordable = :is_afford, + median_household_income = :income, + pct_owner_occupied = :owner, + pct_renter_occupied = :renter + WHERE neighbourhood_id = :nid AND year = :year + """ + ), + { + "nid": row["neighbourhood_id"], + "year": row["year"], + "bachelor": avg_rent - 500, + "onebed": avg_rent - 300, + "twobed": avg_rent, + "threebed": avg_rent + 400, + "vacancy": round(random.uniform(0.5, 4.5), 1), + "rent_income": rent_to_income, + "afford_idx": affordability, + "is_afford": rent_to_income <= 30, + "income": income, + "owner": round(random.uniform(30, 75), 1), + "renter": round(random.uniform(25, 70), 1), + }, + ) + total += 1 + + # Then insert multi-year data for trend charts + years = [2019, 2020, 2022, 2023, 2024] + with engine.begin() as conn: + result = conn.execute( + text( + "SELECT neighbourhood_id, name, geometry FROM public.dim_neighbourhood" + ) + ) + neighbourhoods = [dict(row._mapping) for row in result] + + for n in neighbourhoods: + for year in years: + # Check if exists + exists = conn.execute( + text( + """ + SELECT 1 FROM public_marts.mart_neighbourhood_housing + WHERE neighbourhood_id = :nid AND year = :year + """ + ), + {"nid": n["neighbourhood_id"], "year": year}, + ).fetchone() + + if exists: + continue + + base_rent = random.randint(1800, 2800) + year_factor = (year - 2019) * random.randint(50, 150) + avg_rent = base_rent + year_factor + income = random.randint(55000, 180000) + rent_to_income = round((avg_rent * 12 / income) * 100, 2) + + conn.execute( + text( + """ + INSERT INTO public_marts.mart_neighbourhood_housing + (neighbourhood_id, neighbourhood_name, geometry, year, + avg_rent_bachelor, avg_rent_1bed, avg_rent_2bed, avg_rent_3bed, + vacancy_rate, rent_to_income_pct, affordability_index, is_affordable, + median_household_income, pct_owner_occupied, pct_renter_occupied) + VALUES + (:nid, :name, :geom, :year, + :bachelor, :onebed, :twobed, :threebed, + :vacancy, :rent_income, :afford_idx, :is_afford, + :income, :owner, :renter) + """ + ), + { + "nid": n["neighbourhood_id"], + "name": n["name"], + "geom": n["geometry"], + "year": year, + "bachelor": avg_rent - 500, + "onebed": avg_rent - 300, + "twobed": avg_rent, + "threebed": avg_rent + 400, + "vacancy": round(random.uniform(0.5, 4.5), 1), + "rent_income": rent_to_income, + "afford_idx": round(rent_to_income / 30 * 100, 1), + "is_afford": rent_to_income <= 30, + "income": income, + "owner": round(random.uniform(30, 75), 1), + "renter": round(random.uniform(25, 70), 1), + }, + ) + total += 1 + + print(f"Seeded housing mart data for {total} records") + return total + + +def seed_overview_mart() -> int: + """Seed overview mart with safety_score and population.""" + engine = create_engine(DATABASE_URL) + total = 0 + + with engine.begin() as conn: + # Seed safety_score + result = conn.execute( + text( + """ + SELECT neighbourhood_id, year + FROM public_marts.mart_neighbourhood_overview + WHERE safety_score IS NULL + """ + ) + ) + rows = [dict(row._mapping) for row in result] + + for row in rows: + conn.execute( + text( + """ + UPDATE public_marts.mart_neighbourhood_overview + SET safety_score = :score + WHERE neighbourhood_id = :nid AND year = :year + """ + ), + { + "nid": row["neighbourhood_id"], + "year": row["year"], + "score": round(random.uniform(40, 95), 1), + }, + ) + total += 1 + + # Seed population + result = conn.execute( + text( + """ + SELECT neighbourhood_id, year + FROM public_marts.mart_neighbourhood_overview + WHERE population IS NULL + """ + ) + ) + rows = [dict(row._mapping) for row in result] + + for row in rows: + conn.execute( + text( + """ + UPDATE public_marts.mart_neighbourhood_overview + SET population = :pop + WHERE neighbourhood_id = :nid AND year = :year + """ + ), + { + "nid": row["neighbourhood_id"], + "year": row["year"], + "pop": random.randint(8000, 45000), + }, + ) + total += 1 + + print(f"Seeded overview mart data for {total} records") + return total + + +def run_dbt() -> bool: + """Run dbt to rebuild marts.""" + dbt_dir = PROJECT_ROOT / "dbt" + venv_dbt = PROJECT_ROOT / ".venv" / "bin" / "dbt" + dbt_cmd = str(venv_dbt) if venv_dbt.exists() else "dbt" + + print("Running dbt to rebuild marts...") + + env = os.environ.copy() + env["POSTGRES_PASSWORD"] = os.environ.get("POSTGRES_PASSWORD", "") + + result = subprocess.run( + [dbt_cmd, "run", "--profiles-dir", str(dbt_dir)], + cwd=dbt_dir, + capture_output=True, + text=True, + env=env, + ) + + if result.returncode != 0: + print(f"dbt failed:\n{result.stdout}\n{result.stderr}") + return False + + print("dbt completed successfully") + return True + + +def main() -> int: + """Main entry point.""" + print("Seeding development data...") + + seed_amenities() + update_population() + seed_median_age() + seed_census_housing() + + if not run_dbt(): + return 1 + + # Seed mart tables after dbt rebuild + seed_housing_mart() + seed_overview_mart() + + print("\nDone! Development data is ready.") + return 0 + + +if __name__ == "__main__": + result = main() + sys.exit(result) diff --git a/scripts/db/.gitkeep b/scripts/db/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scripts/db/init-postgis.sql b/scripts/db/init-postgis.sql new file mode 100644 index 0000000..141d754 --- /dev/null +++ b/scripts/db/init-postgis.sql @@ -0,0 +1,8 @@ +-- Initialize PostGIS extension +-- This script runs automatically on first container start + +-- Enable PostGIS extension +CREATE EXTENSION IF NOT EXISTS postgis; + +-- Verify installation +SELECT PostGIS_Version(); diff --git a/scripts/db/init_schema.py b/scripts/db/init_schema.py new file mode 100644 index 0000000..19ac89d --- /dev/null +++ b/scripts/db/init_schema.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +"""Initialize database schema. + +Usage: + python scripts/db/init_schema.py + +This script creates all SQLAlchemy tables in the database. +Run this after docker-compose up to initialize the schema. +""" + +import sys +from pathlib import Path + +# Add project root to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from portfolio_app.toronto.models import create_tables, get_engine # noqa: E402 + + +def main() -> int: + """Initialize the database schema.""" + print("Initializing database schema...") + + try: + engine = get_engine() + + # Test connection + from sqlalchemy import text + + with engine.connect() as conn: + result = conn.execute(text("SELECT 1")) + result.fetchone() + print("Database connection successful") + + # Create all tables + create_tables() + print("Schema created successfully") + + # List created tables + from sqlalchemy import inspect + + inspector = inspect(engine) + tables = inspector.get_table_names() + print(f"Created tables: {', '.join(tables)}") + + return 0 + + except Exception as e: + print(f"Error: {e}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/dbt/.gitkeep b/scripts/dbt/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scripts/deploy/.gitkeep b/scripts/deploy/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scripts/dev/.gitkeep b/scripts/dev/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scripts/docker/.gitkeep b/scripts/docker/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scripts/etl/toronto.sh b/scripts/etl/toronto.sh new file mode 100755 index 0000000..b770691 --- /dev/null +++ b/scripts/etl/toronto.sh @@ -0,0 +1,72 @@ +#!/usr/bin/env bash +# scripts/etl/toronto.sh - Run Toronto data pipeline +# +# Usage: +# ./scripts/etl/toronto.sh --full # Complete reload of all data +# ./scripts/etl/toronto.sh --incremental # Only new data since last run +# ./scripts/etl/toronto.sh # Default: incremental +# +# Logs are written to .dev/logs/etl/ + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" +LOG_DIR="$PROJECT_ROOT/.dev/logs/etl" +TIMESTAMP=$(date +%Y%m%d_%H%M%S) +LOG_FILE="$LOG_DIR/toronto_${TIMESTAMP}.log" + +MODE="${1:---incremental}" + +mkdir -p "$LOG_DIR" + +log() { + echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE" +} + +log "Starting Toronto ETL pipeline (mode: $MODE)" +log "Log file: $LOG_FILE" + +cd "$PROJECT_ROOT" + +# Activate virtual environment if it exists +if [ -d ".venv" ]; then + source .venv/bin/activate + log "Activated virtual environment" +fi + +case "$MODE" in + --full) + log "Running FULL data reload..." + + log "Step 1/4: Parsing neighbourhood/geographic data..." + python -m portfolio_app.toronto.parsers.geo 2>&1 | tee -a "$LOG_FILE" + + log "Step 2/4: Parsing Toronto Open Data (census, amenities)..." + python -m portfolio_app.toronto.parsers.toronto_open_data 2>&1 | tee -a "$LOG_FILE" + + log "Step 3/4: Parsing crime data..." + python -m portfolio_app.toronto.parsers.toronto_police 2>&1 | tee -a "$LOG_FILE" + + log "Step 4/4: Running dbt transformations..." + cd dbt && dbt run --full-refresh --profiles-dir . 2>&1 | tee -a "$LOG_FILE" && cd .. + ;; + + --incremental) + log "Running INCREMENTAL update..." + + log "Step 1/2: Checking for new data..." + # Add incremental logic here when implemented + + log "Step 2/2: Running dbt transformations..." + cd dbt && dbt run --profiles-dir . 2>&1 | tee -a "$LOG_FILE" && cd .. + ;; + + *) + log "ERROR: Unknown mode '$MODE'. Use --full or --incremental" + exit 1 + ;; +esac + +log "Toronto ETL pipeline completed successfully" +log "Full log available at: $LOG_FILE" diff --git a/scripts/logs.sh b/scripts/logs.sh new file mode 100755 index 0000000..8d53153 --- /dev/null +++ b/scripts/logs.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +# scripts/logs.sh - Follow docker compose logs +# +# Usage: +# ./scripts/logs.sh # All services +# ./scripts/logs.sh postgres # Specific service +# ./scripts/logs.sh -n 100 # Last 100 lines + +set -euo pipefail + +SERVICE="${1:-}" +EXTRA_ARGS="${@:2}" + +if [[ -n "$SERVICE" && "$SERVICE" != -* ]]; then + echo "Following logs for service: $SERVICE" + docker compose logs -f "$SERVICE" $EXTRA_ARGS +else + echo "Following logs for all services" + docker compose logs -f $@ +fi diff --git a/scripts/run-detached.sh b/scripts/run-detached.sh new file mode 100755 index 0000000..e4de1d5 --- /dev/null +++ b/scripts/run-detached.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash +# scripts/run-detached.sh - Start containers and wait for health +# +# Usage: +# ./scripts/run-detached.sh + +set -euo pipefail + +TIMEOUT=60 +INTERVAL=5 + +echo "Starting containers in detached mode..." +docker compose up -d + +echo "Waiting for services to become healthy..." +elapsed=0 + +while [ $elapsed -lt $TIMEOUT ]; do + # Check if postgres is ready + if docker compose exec -T postgres pg_isready -U portfolio > /dev/null 2>&1; then + echo "PostgreSQL is ready!" + + # Check if app health endpoint responds (if running) + if curl -sf http://localhost:8050/health > /dev/null 2>&1; then + echo "Application health check passed!" + echo "All services are healthy." + exit 0 + fi + fi + + echo "Waiting... ($elapsed/$TIMEOUT seconds)" + sleep $INTERVAL + elapsed=$((elapsed + INTERVAL)) +done + +echo "ERROR: Health check timed out after $TIMEOUT seconds" +docker compose ps +exit 1 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e8673c5 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""Test suite for analytics portfolio.""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..717d081 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,9 @@ +"""Pytest configuration and fixtures.""" + +import pytest + + +@pytest.fixture +def sample_fixture(): + """Example fixture - replace with actual fixtures as needed.""" + return {} diff --git a/tests/test_placeholder.py b/tests/test_placeholder.py new file mode 100644 index 0000000..211c037 --- /dev/null +++ b/tests/test_placeholder.py @@ -0,0 +1,6 @@ +"""Placeholder test to ensure pytest collection succeeds.""" + + +def test_placeholder(): + """Remove this once real tests are added.""" + assert True