Add loading documentation

README.md

@@ -18,6 +18,73 @@ Each benchmark sample asks: given five past calving front observations (spanning
 
 ---
 
+## Quick start
+
+Requires **zarr >= 3.0** and **Python >= 3.11** — see [Requirements](#requirements). Read the archives **directly; do not unzip them.**
+
+```bash
+pip install "zarr>=3.0" huggingface_hub
+```
+
+```python
+import zarr
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="enscg/calvdb",
+    filename="zarr_zipped/RGI60-07.00552.zarr.zip",   # one glacier, 30 MB
+    repo_type="dataset",
+)
+
+group = zarr.open_group(zarr.storage.ZipStore(path, mode="r"), mode="r")
+
+print(dict(group.attrs))    # glacier_id, T, H, W, CRS, channel names
+print(group["sdt"].shape)   # (T, H, W)  signed distance transform, metres
+print(group["dates"][:5])   # observation dates, YYYY-MM-DD
+```
+
+Or with **xarray**, for named dimensions and lazy loading:
+
+```python
+import xarray as xr
+
+ds = xr.open_zarr(zarr.storage.ZipStore(path, mode="r"))
+print(ds.sdt.dims)          # ('time', 'y', 'x')
+```
+
+A complete runnable example — download, inspect, plot, and load a benchmark
+sample — is in **[`example_loading.ipynb`](example_loading.ipynb)**.
+
+---
+
+## Requirements
+
+| Component | Requirement | Why |
+|---|---|---|
+| `zarr` | **>= 3.0** (tested 3.1.5) | stores are Zarr **format 3**; zarr 2.x cannot read them |
+| Python | **>= 3.11** | hard floor of zarr-python 3.x |
+| `numcodecs` | >= 0.14 | installed with zarr 3; provides the **zstd** codec |
+| `numpy` | >= 1.26 | zarr 3 floor |
+| `xarray` | >= 2024.10 | optional, for the xarray route |
+| `huggingface_hub` | any recent | download only |
+
+### Troubleshooting
+
+**`GroupNotFoundError: group not found at path ''`** — you are on zarr 2.x. A v2
+reader looks for `.zgroup` / `.zarray`, which Zarr v3 stores do not contain.
+Check with `import zarr; print(zarr.__version__)`; it must be 3.x. Do not upgrade
+zarr inside an existing 2.x environment unless you are ready for a breaking API
+change — create a fresh environment instead.
+
+**`TypeError: cannot cast dtype StringDType()`** — under numpy >= 2 the `dates`
+array (vlen-utf8) arrives as `StringDType`. Use `dates.tolist()`, not
+`dates.astype(str)`.
+
+**Opening the `.zip` with `zipfile`, or unzipping it first** — unnecessary.
+`zarr.storage.ZipStore` reads chunks directly from the archive.
+
+---
+
 ## Dataset at a glance
 
 | Property | Value |
@@ -30,7 +97,8 @@ Each benchmark sample asks: given five past calving front observations (spanning
 | CRS | EPSG:3995 (Arctic Polar Stereographic) |
 | Prediction horizon | 365 days |
 | Input sequence length | 5 snapshots |
-| Version | 1.0.0 |
+| Download size | 5.4 GB (all glaciers); 0.4–300 MB per glacier |
+| Version | 1.1.0 |
 | License | CC BY 4.0 |
 
 ---
@@ -39,8 +107,10 @@ Each benchmark sample asks: given five past calving front observations (spanning
 
 ```
 calvdb/
-├── zarr_zipped/                 # per-glacier zip archives (for download)
-│   └── RGI60-07.XXXXX.zarr.zip
+├── zarr_zipped/                 # one Zarr v3 store per glacier, zipped
+│   └── RGI60-07.XXXXX.zarr.zip  #   read directly with zarr.storage.ZipStore
+├── zarr_sample/                 # one store, unzipped, for browsing only
+│   └── RGI60-07.00025.zarr/     #   click through it in the file browser above
 ├── splits/
 │   ├── train.json               # 866 benchmark samples
 │   ├── val.json                 # 109 benchmark samples
@@ -51,13 +121,95 @@ calvdb/
 └── README.md
 ```
 
+### Browsing the layout without downloading
+
+`zarr_sample/RGI60-07.00025.zarr/` is one glacier (Braasvellbreen, 55 timesteps)
+left **unzipped** so you can explore the store in the file browser above and read
+the `zarr.json` metadata documents in place — for example
+`zarr_sample/RGI60-07.00025.zarr/sdt/zarr.json` shows the shape, dtype, chunking
+and dimension names of the SDT array.
+
+Its arrays are identical to `zarr_zipped/RGI60-07.00025.zarr.zip`; it is provided
+for inspection only. **For actual use, download from `zarr_zipped/`** — one file
+of 275 MB rather than 617 loose files, and readable without unzipping.
+
+---
+
+## Store contents
+
+Each archive holds one glacier with `T` observations on an `H × W` grid at 30 m.
+
+| Array | Shape | Dtype | Description |
+|---|---|---|---|
+| `sdt` | `(T, H, W)` | float32 | Signed distance transform to the front, metres, clipped ±2000; the front is the zero level set |
+| `trace` | `(T, H, W)` | bool | Rasterised front line. Evaluation ground truth only — never a model input |
+| `imagery` | `(T, 5, H, W)` | float32 | Optical bands: `blue, green, red, nir, swir1` |
+| `geophys` | `(T, 6, H, W)` | float32 | `bed_elevation, surface_elevation, thickness, velocity, strain_rate, ice_fjord_mask` |
+| `climate` | `(T, 2)` | float32 | `mar_runoff, ocean_temp` |
+| `dates` | `(T,)` | string | Acquisition date, `YYYY-MM-DD` |
+| `sensors` | `(T,)` | uint8 | `0=S2, 1=LE07, 2=LC08, 3=LC09, 255=unknown` |
+| `valid_sdt`, `valid_img`, `valid_clm`, `valid_trace` | `(T,)` | bool | Per-timestep reliability flags — **check these before using a timestep** |
+
+Root attributes carry `glacier_id`, `T`, `H`, `W`, `resolution_m`, `crs`,
+`sdt_clip_m`, and the channel-name lists.
+
 ---
 
+## Benchmark splits
+
+Each entry in `splits/{train,val,test}.json` is one forecasting sample:
+
+```json
+{
+  "glacier_id": "RGI60-07.00030_D",
+  "reference_date": "2017-05-20",
+  "horizon_days": 365,
+  "input_zarr_indices": [0, 5, 7, 14, 22],
+  "target_zarr_index": 53,
+  "anchor_offsets": [-1460, -1095, -730, -365, -30]
+}
+```
+
+Index into the arrays with `input_zarr_indices` (the five inputs) and
+`target_zarr_index` (the front 365 days after the last input):
+
+```python
+group = zarr.open_group(zarr.storage.ZipStore(path, mode="r"), mode="r")
+inputs = group["sdt"][sample["input_zarr_indices"]]   # (5, H, W)
+target = group["sdt"][sample["target_zarr_index"]]    # (H, W)
+```
+
+> **Note:** entries also contain a `zarr_path` field (e.g. `../zarr/RGI60-07.00030_D.zarr`).
+> That is a path from the build machine and will not exist on your system —
+> use `glacier_id` to locate `zarr_zipped/<glacier_id>.zarr.zip` instead.
+
+### Suggested task
+
+Predict **displacement** rather than absolute position: `sdt(target) − sdt(last input)`.
+The natural baseline is persistence — predict zero displacement, i.e. the front
+does not move — which any useful model must beat.
+
 ## Normalisation
 
-`splits/normalisation_stats.json` contains per-channel mean and standard deviation computed **from the training split only**. Apply z-score normalisation before training.
+`splits/normalisation_stats.json` contains per-channel mean and standard
+deviation computed **from the training split only**. Apply z-score normalisation
+before training.
+
+---
+
+## Changelog
+
+**1.1.0** — Fixed archive packaging. Stores were previously nested under a
+`zarr/<GLACIER>.zarr/` prefix inside each zip, which made `zarr.open_group()`
+and `xarray.open_zarr()` fail with `GroupNotFoundError`. Stores are now at the
+archive root. Added `dimension_names` to all arrays (xarray previously could not
+open the stores at all) and consolidated metadata for faster opens. Added
+loading documentation and a runnable example notebook. Regenerated the unzipped
+`zarr_sample/` with the same corrected metadata, so it matches the archives.
+**Array data is unchanged** — only packaging and metadata.
 
+**1.0.0** — Initial release.
 
 ## License
 
-This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).