# biking_home — cycling ride analysis

A Go toolkit for analyzing cycling rides from GPX exports: parse rides, detect climbs, score their difficulty, match the same climb across rides, and download French mountain passes into SQLite.

## Features

- **GPX parsing** — reads elevation-tracked rides from Garmin/Strava GPX exports into a `Ride` model
- **Climb detection** — automatically finds climbs within a ride, each scored and categorized (Cat 4 … HC)
- **Difficulty score (Cotacol)** — scores any ride or climb by splitting it into 100 m segments and summing `0.1 km × slope²` per segment
- **Similar-climb matching** — finds the same climb across rides by matching start/end coordinates, so times can be compared
- **Mountain pass download** — imports French mountain passes from centcols.org into a SQLite database
- **Plots** — renders elevation and score-profile charts as PNG

## Getting started

```bash
go build -o biking_home .
```

## Usage

```bash
# Download French mountain passes into biking_home.db (SQLite)
./biking_home -download

# Resume an interrupted download, reusing departments already cached on disk
./biking_home -download -resume

# Run the climb-similarity and difficulty-score demo on the example rides
./biking_home -demo

# Profile the demo with Go's CPU profiler
./biking_home -demo -cpuprofile /tmp/cpu.prof
```

## Options / Configuration

| Flag | Description | Default |
| --- | --- | --- |
| `-download` | Download mountain passes into the database | `false` |
| `-resume` | Skip departments already cached on disk | `false` |
| `-demo` | Run the climb-similarity demo | `false` |
| `-cpuprofile` | Write a CPU profile to file | `""` |

## Architecture

- Go 1.23; SQLite via `mattn/go-sqlite3`; charts via `gonum.org/v1/plot`
- `ride` — GPX parsing, climb detection, difficulty scores (KOM + Cotacol), similarity index
- `mountain_pass` — centcols.org department CSV download into SQLite, with disk caching and retries
- **Notable choices** — the difficulty score follows the Cotacol method: the ride is split into fixed 100 m segments and each scores `distance_km × slope²`, so steep sections weigh exponentially more than long flat ones

## Development

```bash
go vet ./...
go test ./...
```

## TODO

- Compute estimated power
- Export data from Strava / Garmin (GPX, TCX, FIT)
- Auto-detect when a mountain pass is crossed
- Plot speed and slope per segment, colored by heart rate
- Persist the chosen climb variant across activities
- Handle historical data
- Blog with pictures and markdown
- Store GPX files

## Resources

- [Climbfinder: difficulty points](https://climbfinder.com/en/difficulty-points) — the Cotacol formula behind the difficulty score
- [centcols.org](https://www.centcols.org) — French mountain pass reference used for the SQLite data
- [Pogacar 21 July 2022](https://www.strava.com/activities/7505784085) — example ride used for climb detection
- [gpxgo](https://github.com/tkrajina/gpxgo) — GPX parsing library
- [geodist](https://github.com/jftuga/geodist) — haversine distance used for climb matching