update README and remote usage

GBI-16-4D.py

@@ -2,12 +2,12 @@ import os
 import random
 from glob import glob
 import json
+from huggingface_hub import hf_hub_download
 
 
-import pyarrow as pa
 from astropy.io import fits
 import datasets
-from datasets import DatasetInfo, DownloadManager
+from datasets import DownloadManager
 from fsspec.core import url_to_fs
 
 _DESCRIPTION = (
@@ -39,6 +39,8 @@ _URLS = {
     }
 }
 
+_REPO_ID = "AstroCompress/GBI-16-4D"
+
 class GBI_16_4D(datasets.GeneratorBasedBuilder):
     """GBI-16-4D Dataset"""
 
@@ -86,14 +88,15 @@ class GBI_16_4D(datasets.GeneratorBasedBuilder):
 
         ret = []
         base_path = dl_manager._base_path
-        #if base_path.startswith(datasets.config.HF_ENDPOINT):
-        #    base_path = base_path[len(datasets.config.HF_ENDPOINT):].replace("/resolve/", "@", 1)
-        #    base_path = "hf://" + base_path.lstrip("/")
+        locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) 
         _, path = url_to_fs(base_path)
 
         for split in ["train", "test"]:
-            split_file_location = os.path.normpath(os.path.join(path, _URLS[self.config.name][split]))
-            split_file = dl_manager.download(split_file_location)
+            if locally_run:
+                split_file_location = os.path.normpath(os.path.join(path, _URLS[self.config.name][split]))
+                split_file = dl_manager.download_and_extract(split_file_location)
+            else:
+                split_file = hf_hub_download(repo_id=_REPO_ID, filename=_URLS[self.config.name][split], repo_type="dataset")
             with open(split_file, encoding="utf-8") as f:
                 data_filenames = []
                 data_metadata = []
@@ -106,9 +109,12 @@ class GBI_16_4D(datasets.GeneratorBasedBuilder):
                                           "ntimes": item["ntimes"], 
                                           "nbands": item["nbands"],
                                           "image_id": item["image_id"]})
-
-                data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames]
-                data_files = [dl_manager.download(data_url) for data_url in data_urls]
+                if locally_run:
+                    data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames]
+                    data_files = [dl_manager.download(data_url) for data_url in data_urls]
+                else:
+                    data_urls = data_filenames
+                    data_files = [hf_hub_download(repo_id=_REPO_ID, filename=data_url, repo_type="dataset") for data_url in data_urls]
             ret.append(
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,

README.md

@@ -36,4 +36,62 @@ dataset_info:
   download_size: 908845172
   dataset_size: 911075540
 ---
-GBI-16-4D Dataset
+
+# GBI-16-4D Dataset
+
+GBI-16-4D is a dataset which is part of the AstroCompress project. It contains data assembled from the Sloan Digital SkySurvey (SDSS). Each FITS file contains a series of 800x800 pixel uint16 observations of the same portion of the Stripe82 field, taken in 5 bandpass filters (u, g, r, i, z) over time. The filenames give the 
+starting run, field, camcol of the observations, the number of filtered images per timestep, and the number of timesteps. For example: 
+
+```cube_center_run4203_camcol6_f44_35-5-800-800.fits```
+
+contains 35 frames of 800x800 pixel images in 5 bandpasses starting with run 4203, field 44, and camcol 6. The images are stored in the FITS standard.
+
+# Usage
+
+You first need to install the `datasets` and `astropy` packages:
+
+```bash
+pip install datasets satrapy
+```
+
+There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 4D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.
+
+## Use from Huggingface Directly
+
+To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:
+
+```bash
+huggingface-cli login
+```
+
+Then in your python script:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('AstroCompress/GBI-16-4D', 'tiny')
+ds = dataset.with_format("np")
+```
+
+## Local Use
+
+Alternatively, you can clone this repo and use directly without connecting to hf:
+
+```bash
+git clone https://huggingface.co/datasets/AstroCompress/GBI-16-4D
+```
+
+Then `cd GBI-16-4D` and start python like:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('./GBI-16-4D.py', 'tiny', data_dir="./data/")
+ds = dataset.with_format("np")
+```
+
+Now you should be able to use the `ds` variable like:
+
+```python
+ds["test"][0]["image"].shape # -> (55, 5, 800, 800)
+```
+
+Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk.