Metadata-Version: 2.4
Name: sigwx-parser
Version: 1.4.4
Summary: A parser to convert WAFS IWXXM SIGWX XML files into GeoJSON FeatureCollections.
Author-email: Miguel Ebersbach <ebersbachmiguel@gmail.com>
Project-URL: Homepage, https://github.com/miguel-pub/sigwx-parser
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: lxml
Requires-Dist: shapely
Requires-Dist: antimeridian

# sigwx-parser

A Python parser for converting WAFS IWXXM SIGWX XML files into GeoJSON FeatureCollections.

SIGWX (Significant Weather) charts are issued by the World Area Forecast System (WAFS) and contain information about meteorological hazards for aviation — things like icing, turbulence, jet streams, and tropical cyclones. This library parses the IWXXM XML format of those files and outputs structured GeoJSON that's easy to work with.

## Installation

```bash
pip install sigwx-parser
```

## Usage

```python
from sigwx_parser import SigwxParser

# From a file
parser = SigwxParser(file_path="sigwx_output.xml")

# From a raw XML string
parser = SigwxParser(file_path=None, xml_content=xml_string)

forecasts = parser.parse()
```

Each item in the returned list corresponds to a forecast block in the XML:

```python
[
    {
        "valid_time": "2024-02-22T12:00:00Z",
        "issue_time": "2024-02-21T18:00:00Z",
        "geojson": {
            "type": "FeatureCollection",
            "features": [
                {
                    "type": "Feature",
                    "geometry": {
                        "type": "Polygon",
                        "coordinates": [ [ [lon, lat], ... ] ]
                    },
                    "properties": {
                        "feature_id": "uuid.12345...",
                        "phenomenon": "AIRFRAME_ICING",
                        "upper_fl": "240",
                        "lower_fl": "100"
                    }
                }
            ]
        }
    }
]
```

### Excluding feature types

You can skip specific phenomenon types by passing an `exclude` set to `parse()`:

```python
forecasts = parser.parse(exclude={"CLOUD", "RADIATION"})
```

Valid values for `exclude`:

| Value | Description |
|---|---|
| `AIRFRAME_ICING` | Airframe icing areas |
| `CLOUD` | Significant cloud (e.g. CB, TCU) |
| `JETSTREAM` | Jet stream axes |
| `TROPOPAUSE` | Tropopause height |
| `TURBULENCE` | Turbulence areas |
| `RADIATION` | Radiation hazards |
| `TROPICAL_CYCLONE` | Tropical cyclone positions |
| `VOLCANO` | Volcanic ash areas |

## Notes

- Polygon winding order is corrected automatically using Shapely.
- Geometries that cross the antimeridian are split correctly using the [antimeridian](https://github.com/gadomski/antimeridian) library.
- Namespaces are stripped from the XML before parsing, so the XPath queries stay clean.

## Dependencies

- [lxml](https://lxml.de/)
- [Shapely](https://shapely.readthedocs.io/)
- [antimeridian](https://github.com/gadomski/antimeridian)

## License

MIT

