make tiny splits; use info from WCS header

README.md

@@ -56,7 +56,7 @@ Then `cd SBI-16-3D` and start python like:
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("./SBI-16-2D.py", "tiny", data_dir="./data/")
+dataset = load_dataset("./SBI_16_2D.py", "tiny", data_dir="./data/")
 ds = dataset.with_format("np")
 ```
 
@@ -67,4 +67,4 @@ ds["test"][0]["image"].shape # -> (TBD)
 ```
 
 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.
-
+

SBI-16-2D.py

@@ -38,7 +38,7 @@ _REPO_ID = "AstroCompress/SBI-16-2D"
 class SBI_16_2D(datasets.GeneratorBasedBuilder):
     """SBI-16-2D Dataset"""
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.0.1")
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(

splits/tiny_test.jsonl

@@ -0,0 +1 @@
+{"ra1": 203.7732409387, "dec1": 13.8489878334, "ra2": 203.7214653161, "dec2": 13.8777594581, "ra3": 203.7050942963, "dec3": 13.8539247351, "ra4": 203.7568666382, "dec4": 13.8251565401, "exposure_time": 390.0, "image": "./data/jdrz89fdq_raw.fits", "image_id": "jdrz89fdq_raw", "ra": 203.7436405194, "dec": 13.83161538103, "pixscale": -1.21332e-05}

splits/tiny_train.jsonl

@@ -0,0 +1,2 @@
+{"ra1": 260.2513190778, "dec1": -19.6024730349, "ra2": 260.3018066706, "dec2": -19.6355333113, "ra3": 260.3208290377, "dec3": -19.6131799119, "ra4": 260.2703445491, "dec4": -19.5801249252, "exposure_time": 493.0, "image": "./data/jdig01dbq_raw.fits", "image_id": "jdig01dbq_raw", "ra": 260.2833237316, "dec": -19.58768272612, "pixscale": 1.14791e-05}
+{"ra1": 11.77879924, "dec1": -21.6292955803, "ra2": 11.7313561082, "dec2": -21.6668467959, "ra3": 11.7496899151, "dec3": -21.6898781547, "ra4": 11.7971358575, "dec4": -21.6523217318, "exposure_time": 279.0, "image": "./data/jeyv02heq_raw.fits", "image_id": "jeyv02heq_raw", "ra": 11.78591952774, "dec": -21.6622203537, "pixscale": -1.06419e-05}