# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

Property Map is a full-stack geospatial application for visualizing UK property data on an interactive map. It combines Land Registry price-paid data, EPC energy certificates, postcode geolocation, TFL journey times, Index of Deprivation scores, crime statistics, ethnicity data, broadband speeds, school ratings, road noise, and OpenStreetMap POIs into a single wide parquet file, then serves aggregated H3 hexagon statistics and POI data via a Rust backend.

## Commands

All commands use [Task](https://taskfile.dev) runner. Python uses `uv run`. Frontend uses `npm run` from `frontend/`.

```bash
# Development servers
task dev:server           # Rust backend on :8001 (cargo run --release)
task dev:frontend         # Webpack dev server on :3030 (proxies /api to :8001)

# Data pipeline
task prepare              # Build wide.parquet from all pre-downloaded sources

# Quality
task lint                 # Lint all: Python (ruff) + TypeScript (ESLint+Prettier) + Rust (clippy+fmt)
task format               # Auto-fix formatting for all languages
task test                 # Python tests (fuzzy join, haversine, POI counts)
task check                # Full validation: lint + build + test

# Building
task build:frontend       # TypeScript typecheck + webpack production build
task build:server         # cargo build --release (NOTE: dir is wrong in Taskfile, run from server-rs/)

# Granular lint/format
task lint:python          # uv run ruff check .
task lint:frontend        # eslint + prettier --check
task lint:rust            # cargo clippy -- -D warnings && cargo fmt --check
task format:python        # ruff check --fix && ruff format
task format:frontend      # eslint --fix + prettier --write
task format:rust          # cargo fmt --all
```

Running individual tests:
```bash
uv run pytest pipeline/utils/test_haversine.py       # Single test file
uv run pytest pipeline/utils/test_haversine.py -k "test_name"  # Single test
```

## Architecture

### Data Flow

```
Raw sources → [Download scripts] → data/*.parquet
  → [Fuzzy join EPC ↔ Price-Paid] → epc_pp.parquet
  → [Merge all datasets] → wide.parquet
  → [Rust server loads into memory + precomputes H3 + spatial grid]
  → [Frontend renders deck.gl H3HexagonLayer over MapLibre GL]
```

### Data Pipeline (`pipeline/`)

Python + Polars. Two phases:

1. **Download** (`pipeline/download/`) — Each script fetches one raw dataset into `data/`
2. **Transform** (`pipeline/transform/`) — Joins and derives features:
   - `join_epc_pp.py` — Fuzzy-joins EPC ↔ price-paid by address within postcode buckets
   - `merge.py` — **Main pipeline**: joins all datasets → `wide.parquet` with human-readable column names
   - `transform_poi.py` — Filters POIs, maps to friendly names + emoji (exhaustive category validation)
   - `poi_proximity.py` — Counts POIs within 2km per postcode using 0.05° spatial grid
   - `crime.py` — Aggregates crime CSVs into yearly averages by LSOA

**Critical: column renaming in `merge.py`** — The pipeline renames columns from snake_case to human-readable names before writing `wide.parquet`. The Rust server auto-discovers features from whatever column names exist in the parquet. Key renames:
- `pp_address` → `Address per Property Register`
- `postcode` → `Postcode`
- `latest_price` → `Last known price`
- `duration` → `Leashold/Freehold`
- `total_floor_area` → `Total floor area (sqm)`
- `current_energy_rating` → `Current energy rating`

The server and frontend must handle these human-readable names. See the full rename map in `merge.py`.

### Backend (`server-rs/`)

Rust + Axum. Loads parquet into memory at startup.

**Structure:**
- `data/property.rs` — Loads `wide.parquet`, auto-discovers numeric + enum features, computes histograms, sorts rows by spatial locality, precomputes H3 cells (resolutions 4–12)
- `data/poi.rs` — Loads `filtered_uk_pois.parquet`
- `index.rs` — `GridIndex`: 0.01° spatial grid for O(1) cell lookup
- `filter.rs` — Parses filter strings and checks rows. Format: `name:min:max` (numeric), `name:val1|val2` (enum)
- `routes/` — One file per endpoint
- `consts.rs` — Key constants (histogram bins, H3 range, max enum cardinality, excluded columns)

**API endpoints:**
- `GET /api/features` — Feature metadata with histograms and 2nd/98th percentiles
- `GET /api/hexagons?resolution=&bounds=&filters=` — H3 aggregates (min/max per feature per hex)
- `GET /api/hexagon-properties?h3=&resolution=&filters=&limit=&offset=` — Paginated properties within a hexagon
- `GET /api/pois?bounds=&categories=` — POIs by bounds (max 5000)
- `GET /api/poi-categories` — Available POI category names

Serves `frontend/dist/` as static fallback in production.

**Data representation:**
- Numeric features: row-major flat `Vec<f64>`, NaN = null
- Enum features: `Vec<u8>` indices into value list, 255 = null
- String fields (address, postcode): `Vec<String>`, empty = null
- The server accepts the parquet path as a CLI argument (defaults to `data_sources/processed/wide.parquet`)

### Frontend (`frontend/`)

React 18 + TypeScript. deck.gl `H3HexagonLayer` over MapLibre GL. TailwindCSS. No state management library — pure React hooks.

**Key patterns:**
- `App.tsx` manages all state, API fetching (150ms debounce), and URL state sync (300ms debounce)
- URL encodes view/filters/POI categories/active tab as query params for shareable links
- AbortControllers cancel in-flight requests on new queries
- Zoom → H3 resolution: `<7→7, <9.5→8, <11→9, <13→10, ≥13→11`
- Bounds quantized to 0.01° to match backend caching
- Properties pane uses feature names from API response (human-readable), not hardcoded field names
- Proxy: dev server on :3030 proxies `/api` to :8001; also handles VS Code `/proxy/PORT` patterns

## Key Implementation Details

- **Spatial sort**: Rows sorted by 0.01° grid cell at load time for cache-friendly sequential access
- **Row-major layout**: `feature_data[row * num_features + feat_idx]` — all features for one property are contiguous
- **H3 precomputation**: Resolutions 4–12 computed in parallel (rayon) at startup
- **Histogram percentiles without sorting**: O(n) two-pass algorithm — build histogram, interpolate percentiles
- **Direct JSON writing**: Hexagon endpoint writes JSON via string buffer, avoids serde_json::Value allocations
- **POI transform validation**: Fails if any OSM category is unmapped — guarantees exhaustive coverage
- **Fuzzy join**: Groups by postcode, uses `thefuzz.token_sort_ratio` with numeric token compatibility, greedy assignment from highest score
- **Filter bounds format**: `south,west,north,east` (not standard bbox order)
- **POI proximity**: Uses 0.05° grid (~5km cells) to reduce candidates before haversine distance check