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A python class for loading GESLA data files into convenient in-memory data objects

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# GeslaDataset
 
 File `gesla.py` defines `GeslaDataset`, a python class for loading GESLA sea-level data into convenient in-memory data objects.  Data can be loaded from local GESLA files (`source="local"`, the default) or from the [UHSLC ERDDAP server](https://uhslc.soest.hawaii.edu/erddap/tabledap/global_hourly_gesla.html) (`source="erddap"`). Using the ERDDAP server allows for accessing GESLA data on demand without maintaining a local copy of the dataset.

By default, single station requests are loaded into `pandas.DataFrame` objects,while multi-station requests are loaded into `xarray.Dataset` objects. 
   
See the [example usage](https://github.com/philiprt/gesla-dataset/blob/main/example_usage.ipynb) notebook for a demonstration of how to initialize and use the class.

## Data loading functions

- `record_to_pandas(record_id)` — load a single record into a `pandas.DataFrame` of sea levels and flags, plus a `pandas.Series` of record metadata. Null sea-level values are returned as NaN by default (disable with `mask_null=False`).
- `records_to_xarray(record_ids)` — load a list of records into a `xarray.Dataset` with per-station metadata variables. Stations are ordered to match the list of record IDs.
- `load_N_closest(lat, lon, N)` — load the N records closest to a lat/lon location. Distances account for the dateline and for the convergence of meridians with latitude.
- `load_lat_lon_range(south_lat, north_lat, west_lon, east_lon)` — load the records within a rectangular lat/lon range. Ranges crossing the dateline are supported.
- `load_country(country)` — load the records for one or more three-letter ISO 3166-1 alpha-3 country codes, e.g. `"USA"` or `["USA", "CAN"]`.
- `load_polygon(polygon)` — load the records located inside a polygon given as a sequence of (lon, lat) vertex pairs. Implemented with numpy alone; polygons crossing the dateline are supported.

All loading functions accept an optional `time_range` argument that restricts the period loaded: a two-element list-like (or pandas date range) whose elements can be datetime objects or date strings like `"1900-01-04"`, `"1900-01"`, or `"1900"`, with both bounds inclusive. With `source="erddap"`, the time range is applied in the server query, so only the requested period is downloaded.

The selection functions (`load_*`) return a `pandas.DataFrame`/`pandas.Series` pair when a single record is selected, and a `xarray.Dataset` otherwise; pass `force_xarray=True` to always get a dataset. The former `file_to_pandas` and `files_to_xarray` function names remain available as backward-compatible aliases of `record_to_pandas` and `records_to_xarray`.

The repository was developed with the following libraries and versions (see `requirements.txt`):
```
python 3.12.13
erddapy 3.3.0
netcdf4 1.7.4
numpy 2.5.1
pandas 3.0.3
xarray 2026.4.0
```
Using other versions may require adjustments to the code.

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