Upload README.md

README.md

@@ -97,7 +97,7 @@ This dataset is distributed as **NetCDF-4** files. There are two common ways to
 
 ### Option 1 (recommended): clone the ClimX code and use the helper loader
 
-The ClimX repository already includes a helper module (`src/data/climx_hf.py`) that allows you to download the dataset from Hugging Face and open it as three lazily-loaded “virtual” xarray datasets:
+The ClimX repository includes a helper module (`src/utils/hugging_face_utils.py`) that downloads the requested dataset variant from Hugging Face and opens it as three lazily-loaded “virtual” xarray datasets. Use Python 3.10 or newer:
 
 ```bash
 git clone https://github.com/IPL-UV/ClimX.git
@@ -106,13 +106,15 @@ pip install -U "huggingface-hub" xarray netcdf4 dask
 ```
 
 ```python
-from src.data.climx_hf import download_climx_from_hf, open_climx_virtual_datasets
+from pathlib import Path
+from src.utils.hugging_face_utils import get_dataset_from_hf, open_climx_virtual_datasets
 
 # Download NetCDF artifacts from HF into a local cache directory.
-root = download_climx_from_hf("/path/to/hf_cache", variant="full")
+root = Path("/path/to/hf_cache")
+get_dataset_from_hf(root, variant="full")
 
 # Open as three virtual datasets (lazy / dask-friendly).
-ds = open_climx_virtual_datasets(root, variant="full") # or "lite"
+ds = open_climx_virtual_datasets(root, variant="full")  # or "lite"
 
 ds.hist # historical (targets + forcings)
 ds.train # projections training scenarios (targets + forcings; excludes `ssp245` scenario)