<p align="center">
<img src="docs/img/hilly-icon_sm.png" />
</p>
# Hilly
Tools for summarizing and viewing sporty GPX traces
## Install
```bash
pip install hilly
```
## Dev Install
```
git clone
cd hilly
poetry install
```
## Features
* Traces are loaded as GeoDataFrames (convenient exploring and editing)
* Rasterize traces to see popular routes/trails.
* Label/Filter Skiing Trace to label/exclude chair-lifts.
* Summary for Skiing day
## Usage
Import a trace:
```python
import hilly
df = hilly.io.load_trace("path/to/trace.gpx")
```
If it is a ski/snowboarding trace, apply filters to identify runs/lifts:
```python
ski_df = hilly.ski.apply_filters(df)
ski_df.labels_str.unique().tolist()
# ['', 'lift-1', 'run-1', 'run-2', 'lift-2', 'run-3', 'lift-3', 'run-4']
```
The `labels_str` column labels points in the trace as belonging to a lift ride or run, otherwise it is empty. The number indicates a count of lift/runs.
From there you can get a summary for the trace (assuming it is over a single day):
```python
summary = hilly.ski.summary(ski_df)
#🏂 Ski Stats ⛷
# runs: 4
# Total Dist(km): 4.815023209198009
# Longest Run(km): 1.409849302232853
# Total time: 0 days 00:17:32
# Total lift time: 0 days 00:36:55.990000
# Time Ratio: 0.4747313841668961
```
Or, you can view the trace:
```python
# open leaflet map showing points, dropping datetime column for proper JSON
m = ski_df.drop(columns=["time"]).explore("labels", cmap="coolwarm_r")
m.show_in_browser()
```
or rasterize the trace to get a density map:
```python
arr, bounds = hilly.utils.rasterize(ski_df, runs_only=True, res=1)
m = hilly.io.map_raster(arr, bounds)
m.show_in_browser()
```
# Collecting Traces
There are many apps available for collecting traces. I use [OpenTracks](https://opentracksapp.com/) and follow the Export instructions from the settings menu.
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