docs: slim card to provenance + one pointer to quantem.widget docs, license MIT

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-4.0
+license: mit
 tags:
   - electron-microscopy
   - 4D-STEM
@@ -10,66 +10,37 @@ pretty_name: quantem-data
 
 # quantem-data
 
-Reference electron-microscopy datasets for browsing and learning. Open in your browser via [quantem.widget](https://github.com/bobleesj/quantem.widget) — **no quantem.live install needed**.
+Reference electron-microscopy datasets (gold 4D-STEM and HAADF) for browsing
+and learning with [quantem.widget](https://github.com/bobleesj/quantem.widget).
+This repo just hosts the data — all instructions live in one place:
 
-Two buckets:
-
-- **`4dstem/`** — 4D-STEM acquisitions. `_npy_bin*` variants are pre-binned NumPy files for fast workshop / Colab demos; the originals are full Arina h5 bundles.
-- **`haadf/`** — HAADF survey images. `_npy` variants are pre-cooked NumPy + a `meta.json` sidecar carrying sampling + optics; the originals are full Velox EMD files.
-
-A ready-to-run notebook sits at `notebooks/show4dstem_colab.ipynb` in this repo.
-
-## One-click workshop notebook (Google Colab)
-
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/bobleesj/54864ce5b2a6f0a4fd5ae1e5d5719b45/show4dstem_colab.ipynb)
-
-It does the full pipeline in 7 cells: install `quantem.widget` (TestPyPI rc) + `quantem` (dev fork branch), download the pre-binned NumPy bundle from this dataset, wrap it as `Dataset4dstem.from_tensor`, render with `Show4DSTEM` in your browser via WebGPU. No CUDA on Colab. No quantem.live.
-
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/bobleesj/a05a90185c6cddbb331342cae6d7e9c1/berk_workshop_v1.ipynb)
-
-**Workshop v1** — real gold 4D-STEM: browse + bright field + dark field + probe + DPC, all on the Colab T4. No `quantem.live`, no local install. The notebook lives at `notebooks/berk_workshop_v1.ipynb` in this repo and on the `berk-workshop` branch of `bobleesj/quantem`.
-
-## Workshop quick start — Show4DSTEM (any Jupyter)
+**How to download and use this data:** follow the
+[Download data tutorial](https://bobleesj.github.io/quantem.widget/tutorials/download_data.html)
+in the quantem.widget docs. One call per dataset:
 
 ```python
-!pip install -q --pre --extra-index-url https://test.pypi.org/simple/ quantem.widget huggingface_hub
-!pip install -q git+https://github.com/electronmicroscopy/quantem.git@dev
-
-import os, json, numpy as np, torch
-from huggingface_hub import snapshot_download
-from quantem.core.datastructures import Dataset4dstem
 from quantem.widget import Show4DSTEM
+from quantem.widget.datasets import show4dstem_gold
 
-folder = snapshot_download("bobleesj/quantem-data", repo_type="dataset",
-                          allow_patterns=["4dstem/gold_512_npy_bin8/*"])
-asset = os.path.join(folder, "4dstem", "gold_512_npy_bin8")
-data = np.load(os.path.join(asset, "data.npy"))
-meta = json.load(open(os.path.join(asset, "meta.json")))
-
-dset = Dataset4dstem.from_tensor(torch.from_numpy(data),
-                                 sampling=meta["sampling"], units=meta["units"])
-Show4DSTEM(dset)
+Show4DSTEM(show4dstem_gold())
 ```
 
-## Workshop quick start — Show2D for HAADF
-
-```python
-import os, json, numpy as np, torch
-from huggingface_hub import snapshot_download
-from quantem.widget import Show2D
-
-folder = snapshot_download("bobleesj/quantem-data", repo_type="dataset",
-                          allow_patterns=["haadf/gold_haadf_npy/*"])
-asset = os.path.join(folder, "haadf", "gold_haadf_npy")
-img  = np.load(os.path.join(asset, "data.npy"))
-meta = json.load(open(os.path.join(asset, "meta.json")))
+Layout: `4dstem/` holds Arina h5 originals plus pre-binned `_npy_bin*` NumPy
+bundles; `haadf/` holds Velox EMD originals plus `_npy` bundles. Every `_npy*`
+bundle ships a `meta.json` (shape, dtype, sampling, units, optics) next to
+`data.npy`.
 
-Show2D(torch.from_numpy(img), sampling=meta["sampling"], units=meta["units"])
-```
+Workshop notebooks (Google Colab, no local install):
+[Show4DSTEM demo](https://colab.research.google.com/gist/bobleesj/54864ce5b2a6f0a4fd5ae1e5d5719b45/show4dstem_colab.ipynb)
+·
+[Workshop v1](https://colab.research.google.com/gist/bobleesj/a05a90185c6cddbb331342cae6d7e9c1/berk_workshop_v1.ipynb)
 
 ## Acquisition parameters
 
-The 20260423 drift session's optics are **confirmed via the session's own HAADF EMD** (`AccelerationVoltage`, `BeamConvergence`, `CameraLength`). 4D-STEM `scan_sampling` is an **operator pattern from a sibling SSB session** — the drift acquisition itself was never per-file calibrated.
+The 20260423 drift session's optics are **confirmed via the session's own
+HAADF EMD** (`AccelerationVoltage`, `BeamConvergence`, `CameraLength`).
+4D-STEM `scan_sampling` is an **operator pattern from a sibling SSB session**
+— the drift acquisition itself was never per-file calibrated.
 
 | dataset | voltage | probe | CL | scan | scan sampling | det pitch | mag |
 |---|---|---|---|---|---|---|---|
@@ -82,45 +53,8 @@ The 20260423 drift session's optics are **confirmed via the session's own HAADF
 
 ✓ = confirmed via EMD/yaml. `(op)` = operator pattern, not file-certified.
 
-## Datasets at a glance
-
-| name | kind | shape | dtype | size | use |
-|---|---|---|---|---|---|
-| `4dstem/gold_512_npy_bin8/` | NumPy bundle | (512, 512, 24, 24) | uint16 | ~302 MB | workshop / Colab demo |
-| `4dstem/gold_512_npy_bin4/` | NumPy bundle | (512, 512, 48, 48) | uint16 | ~1.2 GB | sharper workshop version |
-| `4dstem/gold_512/` | Arina h5 | (512, 512, 192, 192) | uint16 | ~5 GB | power user |
-| `4dstem/gold_30mrad1.3mx04` … `09` | Arina h5 | varies | uint16 | ~5 GB each | series demo |
-| `haadf/gold_haadf_npy/` | NumPy bundle | (4096, 4096) | uint16 | ~34 MB | workshop / Colab Show2D |
-| `haadf/gold_haadf.emd` | Velox EMD | (4096, 4096) | uint16 | a few MB | full optics carrier |
-
-Each `_npy*` bundle ships a `meta.json` next to `data.npy`: shape, dtype, sampling, units, voltage / probe / CL (with provenance flags) when known.
-
-## Power-user path (full data, GPU decompression)
-
-Got an NVIDIA GPU and want the full Arina h5 / Velox EMD path? Install [`quantem.live`](https://github.com/bobleesj/quantem.live):
-
-```python
-from quantem.live import io
-from quantem.widget import Show4DSTEM, Show2D
-import torch
-
-folder = io.download("gold_512")
-result = io.load(io.discover_masters(folder)[0], det_bin=2)
-Show4DSTEM(torch.from_dlpack(result.data))
-
-ds = io.read_image(io.download("gold_haadf"))
-Show2D(ds)
-```
-
-## Memory (VRAM) for the full h5
-
-| `det_bin` | detector | loaded | peak VRAM | fits 16 GB? |
-|---|---|---|---|---|
-| 1 | 192×192 | 18 GB | ~25 GB | no |
-| **2** | 96×96 | 4.5 GB | **~6.9 GB** | **yes** |
-| 4 | 48×48 | 1.1 GB | ~2.2 GB | yes |
-| 8 | 24×24 | 0.3 GB | ~0.5 GB | yes |
-
-## Licence
+## License
 
-CC-BY-4.0. Cite quantem.live / quantem.widget if you use these in a publication.
+MIT — free to use for anything. Citing
+[quantem.widget](https://github.com/bobleesj/quantem.widget) in publications
+is appreciated.