Update README.md

README.md

@@ -1,13 +1,66 @@
 ![deploy MCASS dashboard](https://github.com/hydrosolutions/MCASS/actions/workflows/docker.yml/badge.svg)
 
 # MCASS
-Visualization of the current snow water storage situation in mountainous Central Asia
 
-This repository contains the code to visualize the current snow water storage situation in mountainous Central Asia. The snow data is obtained from the [snomapperForecast](https://github.com/joelfiddes/snowmapperForecast) model deployed by [@joelfiddes](https://github.com/joelfiddes/), Swiss Federal Institute of Snow and Avalanche Research (SLF) on an AWS instance. The snow model is an operational version of [TopoPyScale](https://github.com/ArcticSnow/TopoPyScale) which runs the [Factorial Snow Model (FSM)](https://github.com/RichardEssery/FSM).
+Interactive dashboard for visualizing snow water storage in mountainous Central Asia.
 
-The deployed version of the app is available here: [snowmapper.ch](https://snowmapper.ch).
+**Live app:** [https://snowmapper.ch](https://snowmapper.ch)
 
+## About
 
+This dashboard visualizes snow water storage data from the [snowmapperForecast](https://github.com/joelfiddes/snowmapperForecast) model, an operational version of [TopoPyScale](https://github.com/ArcticSnow/TopoPyScale) running the [Factorial Snow Model (FSM)](https://github.com/RichardEssery/FSM). The model is deployed by [@joelfiddes](https://github.com/joelfiddes/) at the Swiss Federal Institute for Snow and Avalanche Research (SLF).
 
+## Data Format
 
+The dashboard expects two CSV files per basin in the data directory:
+- `<basin_code>_current.txt` - Current year data
+- `<basin_code>_climate.txt` - Long-term average data
 
+Required columns:
+| Column | Description |
+|--------|-------------|
+| `date` | Date (current year) |
+| `Q5_SWE` | 5th percentile snow water equivalent |
+| `Q50_SWE` | Median snow water equivalent |
+| `Q95_SWE` | 95th percentile snow water equivalent |
+| `Q5_HS` | 5th percentile snow depth |
+| `Q50_HS` | Median snow depth |
+| `Q95_HS` | 95th percentile snow depth |
+
+## Local Development
+
+1. Clone the repository and create a conda environment:
+   ```bash
+   conda create --name mcass
+   conda activate mcass
+   pip install -r requirements.txt
+   ```
+
+2. Configure the data path in `.env`:
+   ```
+   MCASS_DATA_PATH=<path-to-data>
+   ```
+
+3. Generate dummy data if needed (from the `tools/` directory):
+   ```bash
+   jupyter nbconvert --execute --clear-output generate_dummy_data.ipynb
+   ```
+
+4. Run the dashboard:
+   ```bash
+   panel serve mcass-dashboard.py --show --autoreload --port 5010
+   ```
+
+## Docker Deployment
+
+Pull and run the container:
+```bash
+docker pull mabesa/mcass-dashboard:latest
+docker run -d -v /path/to/data:/app/data -p 5006:5006 --name mcass-dashboard mabesa/mcass-dashboard
+```
+
+Commits to `main` trigger automated deployment via GitHub Actions and Watchtower.
+
+## License
+
+MIT