diff --git a/.gitattributes b/.gitattributes
index 55cab133643a2a73e083373d2106533678d0edd5..8055c9301bccc1997abc19b33726c5c3e96133ba 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.fits filter=lfs diff=lfs merge=lfs -text
diff --git a/GBI-16-4D.py b/GBI-16-4D.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec235d5d50d0d57c4030633421db84de22b3c9e6
--- /dev/null
+++ b/GBI-16-4D.py
@@ -0,0 +1,139 @@
+import os
+import random
+from glob import glob
+import json
+from huggingface_hub import hf_hub_download
+from tqdm import tqdm
+import numpy as np
+
+from astropy.io import fits
+from astropy.wcs import WCS
+import datasets
+from datasets import DownloadManager
+from fsspec.core import url_to_fs
+
+
+_DESCRIPTION = (
+"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."
+)
+
+_HOMEPAGE = "https://google.github.io/AstroCompress"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://huggingface.co/datasets/AstroCompress/GBI-16-4D/resolve/main/"
+
+_URLS = {
+    "tiny": {
+        "train": "./splits/tiny_train.jsonl",
+        "test": "./splits/tiny_test.jsonl",
+    },
+    "full": {
+        "train": "./splits/full_train.jsonl",
+        "test": "./splits/full_test.jsonl",
+    }
+}
+
+_REPO_ID = "AstroCompress/GBI-16-4D"
+
+class GBI_16_4D(datasets.GeneratorBasedBuilder):
+    """GBI-16-4D Dataset"""
+
+    VERSION = datasets.Version("1.0.3")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="tiny",
+            version=VERSION,
+            description="A small subset of the data, to test downsteam workflows.",
+        ),
+        datasets.BuilderConfig(
+            name="full",
+            version=VERSION,
+            description="The full dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "tiny"
+
+    def __init__(self, **kwargs):
+        super().__init__(version=self.VERSION, **kwargs)
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Array4D(shape=(None, 5, 800, 800), dtype="uint16"),
+                    "ra": datasets.Value("float64"),
+                    "dec": datasets.Value("float64"),
+                    "pixscale": datasets.Value("float64"),
+                    "ntimes": datasets.Value("int64"),
+                    "nbands": datasets.Value("int64"),
+                    "image_id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation="TBD",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+
+        ret = []
+        base_path = dl_manager._base_path
+        locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) 
+        _, path = url_to_fs(base_path)
+
+        for split in ["train", "test"]:
+            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 = []
+                for line in f:
+                    item = json.loads(line)
+                    data_filenames.append(item["image"])
+                    data_metadata.append({"ra": item["ra"], 
+                                          "dec": item["dec"], 
+                                          "pixscale": item["pixscale"], 
+                                          "ntimes": item["ntimes"], 
+                                          "nbands": item["nbands"],
+                                          "image_id": item["image_id"]})
+                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,
+                    gen_kwargs={"filepaths": data_files,
+                                "split_file": split_file,
+                                "split": split, 
+                                "data_metadata": data_metadata},
+                ),
+            )
+        return ret
+
+    def _generate_examples(self, filepaths, split_file, split, data_metadata):
+        """Generate GBI-16-4D examples"""
+
+        for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
+            task_instance_key = f"{self.config.name}-{split}-{idx}"
+            with fits.open(filepath, memmap=False, ignore_missing_simple=True) as hdul:
+                image_data = hdul[0].data.tolist()
+                yield task_instance_key, {**{"image": image_data}, **item}
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..24e82b1b745413a27c6b4faab7868e042c30ea3a
--- /dev/null
+++ b/README.md
@@ -0,0 +1,115 @@
+---
+license: cc-by-4.0
+pretty_name: SDSS 4d data cubes
+tags:
+- astronomy
+- compression
+- images
+dataset_info:
+  config_name: tiny
+  features:
+  - name: image
+    dtype:
+      array4_d:
+        shape:
+        - 5
+        - 800
+        - 800
+        dtype: uint16
+  - name: ra
+    dtype: float64
+  - name: dec
+    dtype: float64
+  - name: pixscale
+    dtype: float64
+  - name: ntimes
+    dtype: int64
+  - name: nbands
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 558194176
+    num_examples: 2
+  - name: test
+    num_bytes: 352881364
+    num_examples: 1
+  download_size: 908845172
+  dataset_size: 911075540
+---
+
+# 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 astropy
+```
+
+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.
+
+## Local Use (RECOMMENDED)
+
+You can clone this repo and use directly without connecting to hf:
+
+```bash
+git clone https://huggingface.co/datasets/AstroCompress/GBI-16-4D
+```
+
+```bash
+git lfs pull
+```
+
+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/", writer_batch_size=1, trust_remote_code=True)
+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.
+
+
+## Use from Huggingface Directly
+
+This method may only be an option when trying to access the "tiny" version of the dataset.
+
+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
+```
+
+or
+
+```
+import huggingface_hub
+huggingface_hub.login(token=token)
+```
+
+Then in your python script:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("AstroCompress/GBI-16-4D", "tiny", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np")
+```
+
+
+## Utils scripts
+Note that utils scripts such as `eval_baselines.py` must be run from the parent directory of `utils`, i.e. `python utils/eval_baselines.py`.
diff --git a/baseline_results_2d.csv b/baseline_results_2d.csv
new file mode 100644
index 0000000000000000000000000000000000000000..493593cffe40ffab29f106b8ee502a6e6580849b
--- /dev/null
+++ b/baseline_results_2d.csv
@@ -0,0 +1,200 @@
+,JPEG_XL_MAX_EFFORT_BPD,JPEG_XL_MAX_EFFORT_WRITE_RUNTIME,JPEG_XL_MAX_EFFORT_READ_RUNTIME,JPEG_XL_BPD,JPEG_XL_WRITE_RUNTIME,JPEG_XL_READ_RUNTIME,JPEG_2K_BPD,JPEG_2K_WRITE_RUNTIME,JPEG_2K_READ_RUNTIME,JPEG_LS_BPD,JPEG_LS_WRITE_RUNTIME,JPEG_LS_READ_RUNTIME,RICE_BPD,RICE_WRITE_RUNTIME,RICE_READ_RUNTIME
+./data/cube_center_run4203_camcol5_f203_49-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol4_f386_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5681_camcol1_f166_10-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5590_camcol4_f225_73-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5878_camcol5_f193_61-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol1_f94_67-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run1752_camcol6_f260_65-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3427_camcol6_f71_64-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3438_camcol3_f13_46-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3430_camcol3_f48_67-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2968_camcol4_f59_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4858_camcol2_f704_83-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4145_camcol1_f402_80-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5744_camcol5_f260_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3465_camcol5_f77_50-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5905_camcol4_f847_46-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4145_camcol2_f249_43-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5895_camcol1_f431_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4145_camcol3_f144_23-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5792_camcol1_f107_61-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5823_camcol4_f92_69-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol5_f390_55-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4933_camcol4_f384_78-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2728_camcol3_f237_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4253_camcol1_f69_76-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5800_camcol3_f83_68-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4253_camcol6_f51_71-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2873_camcol2_f100_86-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5895_camcol5_f321_64-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5813_camcol4_f516_76-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3460_camcol1_f223_2-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2677_camcol1_f156_72-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2738_camcol6_f306_67-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2728_camcol5_f534_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4253_camcol2_f76_74-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3355_camcol4_f89_75-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5895_camcol1_f120_71-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3384_camcol1_f311_74-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5597_camcol1_f183_13-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4797_camcol3_f156_29-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5902_camcol6_f784_53-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5597_camcol5_f229_68-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2583_camcol4_f138_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4263_camcol4_f390_21-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3461_camcol6_f12_60-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5924_camcol5_f538_26-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3437_camcol1_f435_78-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2886_camcol5_f277_46-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5754_camcol6_f536_73-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3388_camcol4_f580_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3384_camcol2_f711_84-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3388_camcol5_f229_70-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4203_camcol5_f549_73-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5759_camcol5_f149_74-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4192_camcol6_f174_66-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3460_camcol3_f55_8-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4933_camcol5_f257_46-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5823_camcol1_f303_37-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5781_camcol2_f366_7-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4933_camcol1_f75_70-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5603_camcol3_f118_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3384_camcol2_f499_9-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4128_camcol1_f446_84-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3438_camcol4_f156_56-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2578_camcol1_f108_76-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4192_camcol5_f450_71-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3465_camcol5_f321_12-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2738_camcol4_f66_42-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5642_camcol4_f257_85-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3388_camcol3_f681_76-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run1752_camcol2_f279_5-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3362_camcol6_f209_45-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2662_camcol1_f176_27-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2385_camcol2_f86_35-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4128_camcol1_f194_62-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4184_camcol6_f156_42-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5878_camcol6_f100_54-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4203_camcol1_f688_53-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5744_camcol5_f66_69-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4198_camcol2_f224_45-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4207_camcol6_f649_84-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5813_camcol3_f438_10-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2662_camcol2_f48_68-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4188_camcol2_f43_76-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4145_camcol3_f149_61-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4128_camcol6_f166_82-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5792_camcol4_f467_40-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5902_camcol5_f780_80-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4203_camcol3_f543_48-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run2968_camcol2_f57_1-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5853_camcol6_f261_69-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4203_camcol3_f79_63-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol3_f133_32-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4198_camcol3_f664_78-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4917_camcol4_f282_67-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol2_f481_68-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3438_camcol4_f127_81-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run1752_camcol4_f312_3-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run5781_camcol3_f287_69-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run3434_camcol6_f169_41-5-800-800.fits,,,,,,,,,,,,,,,
+./data/cube_center_run4203_camcol5_f203_49-5-800-800.fits_arr_0,0.608975,3.3165647983551025,0.07326626777648926,0.653840625,0.06509971618652344,0.04493141174316406,0.6427640625,0.10147905349731445,0.09110569953918457,0.64820625,0.018904685974121094,0.014516830444335938,0.692475,0.008882522583007812,0.006371021270751953
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diff --git a/baseline_results_3dt.csv b/baseline_results_3dt.csv
new file mode 100644
index 0000000000000000000000000000000000000000..41c3811259c66ce0a237affd9cc2e0dfabcd0cad
--- /dev/null
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diff --git a/baseline_results_3dw.csv b/baseline_results_3dw.csv
new file mode 100644
index 0000000000000000000000000000000000000000..376346cb85076bddce9c27b61959124e86dcbfb6
--- /dev/null
+++ b/baseline_results_3dw.csv
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diff --git a/splits/full_train.jsonl b/splits/full_train.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..09023423b7a030e69bb6fcd535f6e445fe1dacbd
--- /dev/null
+++ b/splits/full_train.jsonl
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+{"image_id": "cube_center_run4128_camcol3_f240_26-5-800-800", "image": "./data/cube_center_run4128_camcol3_f240_26-5-800-800.fits", "ra": 17.51675008, "dec": -0.10521871, "pixscale": 0.3961144943864628, "ntimes": 26, "nbands": 5, "footprint": [17.472908464829032, -0.14919006470783627, 17.560804531738395, -0.14918224074800362, 17.560811586074706, -0.06124727392307071, 17.472915766895422, -0.06125509764412338]}
+{"image_id": "cube_center_run5878_camcol2_f228_6-5-800-800", "image": "./data/cube_center_run5878_camcol2_f228_6-5-800-800.fits", "ra": 330.85520699, "dec": -0.5252047, "pixscale": 0.39581405737119957, "ntimes": 6, "nbands": 5, "footprint": [330.8113801445534, -0.5691415613916831, 330.89921524967787, -0.5691341069794252, 330.8992530787007, -0.4812675112570268, 330.8114192083419, -0.48127496595108704]}
+{"image_id": "cube_center_run4203_camcol6_f87_71-5-800-800", "image": "./data/cube_center_run4203_camcol6_f87_71-5-800-800.fits", "ra": 312.42252185, "dec": 0.94286621, "pixscale": 0.3959703182280903, "ntimes": 71, "nbands": 5, "footprint": [312.37868861082416, 0.8989055322853522, 312.46655076497706, 0.8989109532469841, 312.4665761319348, 0.986826346632472, 312.3787117589914, 0.9868209265415595]}
+{"image_id": "cube_center_run5918_camcol6_f418_75-5-800-800", "image": "./data/cube_center_run5918_camcol6_f418_75-5-800-800.fits", "ra": 359.58311229, "dec": 1.15262137, "pixscale": 0.39604537848525323, "ntimes": 75, "nbands": 5, "footprint": [359.53926558803613, 1.1086600582499497, 359.62715253326775, 1.108652425149954, 359.62718034478104, 1.1965819842701049, 359.5392906858235, 1.1965896184450606]}
+{"image_id": "cube_center_run5713_camcol3_f167_107-5-800-800", "image": "./data/cube_center_run5713_camcol3_f167_107-5-800-800.fits", "ra": 315.99009015, "dec": -0.31522609, "pixscale": 0.3960781216981505, "ntimes": 107, "nbands": 5, "footprint": [315.94625047710133, -0.3591868817179797, 316.0341461736697, -0.3591857315792253, 316.0341494650936, -0.2712651100630341, 315.94625451058323, -0.27126625997374304]}
diff --git a/splits/tiny_test.jsonl b/splits/tiny_test.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..80d48e7ceb4ae4740f3e4d651c131e6d47fa5d79
--- /dev/null
+++ b/splits/tiny_test.jsonl
@@ -0,0 +1 @@
+{"image_id": "cube_center_run2649_camcol4_f62_75-5-800-800", "image": "./data/cube_center_run2649_camcol4_f62_75-5-800-800.fits", "ra": 351.1062107, "dec": 0.31443867, "pixscale": 0.3961109627890131, "ntimes": 75, "nbands": 5, "footprint": [351.06237011981153, 0.2704733574688552, 351.1502653866254, 0.27046890527954176, 351.1502716643819, 0.35840378623959207, 351.0623756572411, 0.35840823873764716]}
diff --git a/splits/tiny_train.jsonl b/splits/tiny_train.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8e6ca79a68930618e43bb5b9088e1b332ca8905
--- /dev/null
+++ b/splits/tiny_train.jsonl
@@ -0,0 +1,2 @@
+{"image_id": "cube_center_run4128_camcol1_f448_1-5-800-800", "image": "./data/cube_center_run4128_camcol1_f448_1-5-800-800.fits", "ra": 48.65783238, "dec": -0.94502084, "pixscale": 0.39602209200915184, "ntimes": 1, "nbands": 5, "footprint": [48.61397959111616, -0.988973032899302, 48.70187424617686, -0.9889762521678604, 48.701904065092464, -0.9010680990323667, 48.614011634476945, -0.9010648802874447]}
+{"image_id": "cube_center_run5871_camcol2_f33_2-5-800-800", "image": "./data/cube_center_run5871_camcol2_f33_2-5-800-800.fits", "ra": 34.83516675, "dec": -0.73497152, "pixscale": 0.3959317155167329, "ntimes": 2, "nbands": 5, "footprint": [34.79132689182168, -0.778909849149798, 34.87921635377037, -0.778900997831137, 34.87922573985602, -0.6910327364280786, 34.79133800698183, -0.691041587689708]}
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..21a29ec0e60065cb27c27c5cf7604918f5e3a169
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1 @@
+from .create_splits import *
\ No newline at end of file
diff --git a/utils/create_splits.py b/utils/create_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..311cb73ab5a539b4f91f5ddabb71d628592add7b
--- /dev/null
+++ b/utils/create_splits.py
@@ -0,0 +1,107 @@
+import os
+import random
+from glob import glob
+import json
+from huggingface_hub import hf_hub_download
+from tqdm import tqdm
+import numpy as np
+
+from astropy.io import fits
+from astropy.wcs import WCS
+import datasets
+from datasets import DownloadManager
+from fsspec.core import url_to_fs
+
+def get_fits_footprint(fits_path):
+    """
+    Process a FITS file to extract WCS information and calculate the footprint.
+
+    Parameters:
+    fits_path (str): Path to the FITS file.
+
+    Returns:
+    tuple: A tuple containing the WCS footprint coordinates.
+    """
+    with fits.open(fits_path) as hdul:
+        hdul[0].data = hdul[0].data[0, 0]
+        wcs = WCS(hdul[0].header)
+        shape = sorted(tuple(wcs.pixel_shape))[:2]
+        footprint = wcs.calc_footprint(axes=shape)
+        coords = list(footprint.flatten())
+    return coords
+    
+def calculate_pixel_scale(header):
+    """
+    Calculate the pixel scale in arcseconds per pixel from a FITS header.
+
+    Parameters:
+    header (astropy.io.fits.header.Header): The FITS header containing WCS information.
+
+    Returns:
+    Mean of the pixel scales in x and y.
+    """
+    # Extract the CD matrix elements
+    cd1_1 = header.get('CD1_1', np.nan)
+    cd1_2 = header.get('CD1_2', np.nan)
+    cd2_1 = header.get('CD2_1', np.nan)
+    cd2_2 = header.get('CD2_2', np.nan)
+    
+    # Calculate the pixel scales in arcseconds per pixel
+    pixscale_x = np.sqrt(cd1_1**2 + cd1_2**2) * 3600
+    pixscale_y = np.sqrt(cd2_1**2 + cd2_2**2) * 3600
+    
+    return np.mean([pixscale_x, pixscale_y])
+
+
+def make_split_jsonl_files(config_type="tiny", data_dir="./data", 
+                        outdir="./splits", seed=42):
+    """
+    Create jsonl files for the GBI-16-4D dataset.
+
+    config_type: str, default="tiny"
+        The type of split to create. Options are "tiny" and "full".
+    data_dir: str, default="./data"
+        The directory where the FITS files are located.
+    outdir: str, default="./splits"
+        The directory where the jsonl files will be created.
+    seed: int, default=42
+        The seed for the random split.
+    """
+    random.seed(seed)
+    os.makedirs(outdir, exist_ok=True)
+
+    fits_files = glob(os.path.join(data_dir, "*.fits"))
+    random.shuffle(fits_files)
+    if config_type == "tiny":
+        train_files = fits_files[:2]
+        test_files = fits_files[2:3]
+    elif config_type == "full":
+        split_idx = int(0.8 * len(fits_files))
+        train_files = fits_files[:split_idx]
+        test_files = fits_files[split_idx:]
+    else:
+        raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.")
+
+    def create_jsonl(files, split_name):
+        output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl")
+        with open(output_file, "w") as out_f:
+            for file in tqdm(files):
+                #print(file, flush=True, end="...")
+                with fits.open(file, memmap=False, ignore_missing_simple=True) as hdul:
+                    image_id = os.path.basename(file).split(".fits")[0]
+                    ra = hdul[0].header.get('CRVAL1', np.nan)
+                    dec = hdul[0].header.get('CRVAL2', np.nan)
+                    pixscale = calculate_pixel_scale(hdul[0].header)
+                    ntimes = hdul[0].data.shape[0]
+                    nbands = hdul[0].data.shape[1]
+                    footprint = get_fits_footprint(file)
+                    item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, 
+                            "pixscale": pixscale, "ntimes": ntimes, "nbands": nbands, "footprint": footprint}
+                    out_f.write(json.dumps(item) + "\n")
+
+    create_jsonl(train_files, "train")
+    create_jsonl(test_files, "test")
+
+if __name__ == "__main__":
+    make_split_jsonl_files("tiny")
+    make_split_jsonl_files("full")
\ No newline at end of file
diff --git a/utils/eval_baselines.py b/utils/eval_baselines.py
new file mode 100644
index 0000000000000000000000000000000000000000..4201efa62c29bb695d4be099a5e9e8c4412ac132
--- /dev/null
+++ b/utils/eval_baselines.py
@@ -0,0 +1,185 @@
+"""
+Runs several baseline compression algorithms and stores results for each FITS file in a csv.
+This code is written functionality-only and cleaning it up is a TODO.
+"""
+
+
+import os
+import re
+from pathlib import Path
+import argparse
+import os.path
+from astropy.io import fits
+import numpy as np
+from time import time
+import pandas as pd
+from tqdm import tqdm
+
+from astropy.io.fits import CompImageHDU
+from imagecodecs import (
+    jpeg2k_encode, 
+    jpeg2k_decode, 
+    jpegls_encode, 
+    jpegls_decode, 
+    jpegxl_encode,
+    jpegxl_decode,
+    rcomp_encode,
+    rcomp_decode,
+)
+
+# Functions that require some preset parameters. All others default to lossless.
+
+jpegxl_encode_max_effort_preset = lambda x: jpegxl_encode(x, lossless=True, effort=9)
+jpegxl_encode_preset = lambda x: jpegxl_encode(x, lossless=True)
+
+
+def split_uint16_to_uint8(arr):
+    # Ensure the input is of the correct type
+    assert arr.dtype == np.uint16, "Input array must be of type np.uint16"
+    
+    # Compute the top 8 bits and the bottom 8 bits
+    top_bits = (arr >> 8).astype(np.uint8)
+    bottom_bits = (arr & 0xFF).astype(np.uint8)
+    
+    return top_bits, bottom_bits
+
+def find_matching_files():
+    """
+    Returns list of test set file paths.
+    """
+    df = pd.read_json("./splits/full_test.jsonl", lines=True)
+    return list(df['image'])
+
+def benchmark_imagecodecs_compression_algos(arr, compression_type):
+
+    encoder, decoder = ALL_CODECS[compression_type]
+
+    write_start_time = time()
+    encoded = encoder(arr)
+    write_time = time() - write_start_time
+
+    read_start_time = time()
+    if compression_type == "RICE":
+        decoded = decoder(encoded, shape=arr.shape, dtype=np.uint16)
+    else:
+        decoded = decoder(encoded)
+    read_time = time() - read_start_time
+
+    assert np.array_equal(arr, decoded)
+
+    buflength = len(encoded)
+
+    return {compression_type + "_BPD": buflength / arr.size,
+            compression_type + "_WRITE_RUNTIME": write_time,
+            compression_type + "_READ_RUNTIME": read_time,
+            #compression_type + "_TILE_DIVISOR": np.nan,
+           }
+
+def main(dim):
+
+    save_path = f"baseline_results_{dim}.csv"
+
+    file_paths = find_matching_files()
+    
+    df = pd.DataFrame(columns=columns, index=[str(p) for p in file_paths])
+    
+    print(f"Number of files to be tested: {len(file_paths)}")
+    
+    ct = 0
+
+    for path in tqdm(file_paths):
+        with fits.open(path) as hdul:
+            if dim == '2d': # compress the first timestep frame, R wavelength band (index 2)
+                arr = hdul[0].data[0][2]
+                arrs = [arr]
+            elif dim == '2d-top': # same as 2d, but only top 8 bits. This is to compare with similarly preprocessed neural approaches.
+                arr = hdul[0].data[0][2]
+                arr = split_uint16_to_uint8(arr)[0]
+                arrs = [arr]
+            elif dim == '2d-bottom': # same as 2d, but only bottom 8 bits. This is to compare with similarly preprocessed neural approaches.
+                arr = hdul[0].data[0][2]
+                arr = split_uint16_to_uint8(arr)[1]
+                arrs = [arr]
+            elif dim == '3dt' and len(hdul[0].data) > 2: # 3D tensor with first 3 timestep frames of wavelength band index 2
+                arr = hdul[0].data[0:3][2]
+                arrs = [arr]
+            elif dim == '3dw' and len(hdul[0].data[0]) > 2: # 3D tensor with first timestep frame on wavelength bands of indices 1,2,3 (G, R, I bands)
+                arr = hdul[0].data[0][0:3]
+                arrs = [arr]
+            elif dim == '3dt_reshape' and len(hdul[0].data) > 2: # Same as 3dt but reshape into 2D array, for compatibility with JPEG-LS and RICE
+                arr = hdul[0].data[0:3][2].reshape((800, -1))
+                arrs = [arr]
+            elif dim == '3dw_reshape' and len(hdul[0].data[0]) > 2: # Same as 3dw but reshape into 2D array, for compatibility with JPEG-LS and RICE
+                arr = hdul[0].data[0][0:3].reshape((800, -1))
+                arrs = [arr]
+            elif dim == 'tw': # Iterate through all possible arrays where the x,y spatial location is fixed, and the remaining 2D array consists of ALL timesteps, ALL wavelengths. 
+                init_arr = hdul[0].data
+                def arrs_gen():
+                    for i in range(init_arr.shape[-2]):
+                        for j in range(init_arr.shape[-1]):
+                            yield init_arr[:, :, i, j]
+                            
+                arrs = arrs_gen()
+            else:
+                continue
+
+        ct += 1
+        if ct % 10 == 0:
+            print(df.mean())
+            df.to_csv(save_path)
+
+        for arr_idx, arr in enumerate(arrs):
+            for algo in ALL_CODECS.keys():
+                try:
+                    if algo == "JPEG_2K" and (dim == '3dt' or dim == '3dw'):
+                        test_results = benchmark_imagecodecs_compression_algos(arr.transpose(1, 2, 0), algo)
+                    else:
+                        test_results = benchmark_imagecodecs_compression_algos(arr, algo)
+
+                    for column, value in test_results.items():
+                        if column in df.columns:
+                            df.at[path + f"_arr_{arr_idx}", column] = value
+
+                except Exception as e:
+                    print(f"Failed at {path} under exception {e}.")
+                    
+                    
+        print(df.mean())
+        df.to_csv(save_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
+    parser.add_argument(
+        "dimension", 
+        choices=['2d', '2d-top', '2d-bottom', '3dt', '3dw', 'tw', '3dt_reshape', '3dw_reshape'],
+        help="Specify whether the data is 2d, 3dt (3d time dimension), 3dw (3d wavelength dimension), 2d-top (only top 8 bits), 2d-bottom (only bottom 8 bits), tw (only a single x,y spatial location but all timesteps and wavelengths), 3dt_reshape or 3dw_reshape for the 2D flattened 3D evals, for use on JPEG-LS or RICE."
+    )
+    args = parser.parse_args()
+    dim = args.dimension.lower()
+    
+    # RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER
+    
+    if dim == '3dw' or dim == '3dt' or dim == 'tw':
+        ALL_CODECS = {
+        "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
+        "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
+        "JPEG_2K": [jpeg2k_encode, jpeg2k_decode],
+        }
+    else:
+        ALL_CODECS = {
+            "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
+            "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
+            "JPEG_2K": [jpeg2k_encode, jpeg2k_decode], 
+            "JPEG_LS": [jpegls_encode, jpegls_decode],
+            "RICE": [rcomp_encode, rcomp_decode],
+        }
+
+    columns = []
+    for algo in ALL_CODECS.keys():
+        columns.append(algo + "_BPD")
+        columns.append(algo + "_WRITE_RUNTIME")
+        columns.append(algo + "_READ_RUNTIME")
+        #columns.append(algo + "_TILE_DIVISOR")
+        
+    main(dim)
\ No newline at end of file
diff --git a/utils/sdss_downloading.txt b/utils/sdss_downloading.txt
new file mode 100644
index 0000000000000000000000000000000000000000..73e27f3ddd0bbde93669485c4c0ec8dbede65f9b
--- /dev/null
+++ b/utils/sdss_downloading.txt
@@ -0,0 +1,2 @@
+Please visit this repo:
+https://github.com/profjsb/astrocompress/tree/main
\ No newline at end of file
diff --git a/utils/sdss_filtering.ipynb b/utils/sdss_filtering.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3cc45b0e43a91d97f48418dc3e8835f37514d3fe
--- /dev/null
+++ b/utils/sdss_filtering.ipynb
@@ -0,0 +1,638 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2058f8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "522"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import glob\n",
+    "from astropy.io import fits\n",
+    "import os\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from spherical_geometry.polygon import SphericalPolygon\n",
+    "import os\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from spherical_geometry.polygon import SphericalPolygon\n",
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from astropy.io import fits\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "Use this code after downloading all SDSS data first, using the file\n",
+    "titled sdss_downloading.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "def get_all_fits_files(root_dir):\n",
+    "    # Use glob to recursively find all .fits files\n",
+    "    pattern = os.path.join(root_dir, '**', '*.fits')\n",
+    "    fits_files = glob.glob(pattern, recursive=True)\n",
+    "    return fits_files\n",
+    "\n",
+    "valid_fits_paths = get_all_fits_files('./data')\n",
+    "len(valid_fits_paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "554c2fa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  9%|███▊                                      | 47/522 [00:28<06:45,  1.17it/s]WARNING: FITSFixedWarning: RADECSYS= 'ICRS ' / International Celestial Reference Sys \n",
+      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n",
+      "100%|█████████████████████████████████████████| 522/522 [06:48<00:00,  1.28it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Computes a spherical polygon object for each FITS file using WCS coordinates.\n",
+    "Stores each in a list.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "# Initialize the list of confirmed FITS paths\n",
+    "confirmed_fits_paths = []\n",
+    "\n",
+    "all_polys = []\n",
+    "\n",
+    "for i in tqdm(range(len(valid_fits_paths))):\n",
+    "    path1 = valid_fits_paths[i]\n",
+    "    try:\n",
+    "        with fits.open(path1) as hdul:\n",
+    "            hdul[0].data = hdul[0].data[0, 0]\n",
+    "            wcs1a = WCS(hdul[0].header)\n",
+    "            shape1a = sorted(tuple(wcs1a.pixel_shape))[:2]\n",
+    "            footprint1a = wcs1a.calc_footprint(axes=shape1a)\n",
+    "            poly1a = SphericalPolygon.from_radec(footprint1a[:, 0], footprint1a[:, 1])\n",
+    "            all_polys.append(poly1a)\n",
+    "    except Exception as e:\n",
+    "        print(e)\n",
+    "        continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c58c3c55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████| 522/522 [00:00<00:00, 17983.71it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "latitudes = []\n",
+    "longitudes = []\n",
+    "\n",
+    "\"\"\"\n",
+    "Get a list of all RA and DEC into a list before filtering.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "for poly in tqdm(all_polys):\n",
+    "    pts = list(poly.to_radec())[0]\n",
+    "    ra = pts[0][0]\n",
+    "    dec = pts[1][0]\n",
+    "    \n",
+    "    longitudes.append(ra)\n",
+    "    latitudes.append(dec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1c83484e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetric?\n",
+      "True\n",
+      "(522, 522)\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_points = len(latitudes)\n",
+    "\n",
+    "# Repeat each point n_points times for lat1, lon1\n",
+    "lat1 = np.repeat(latitudes, n_points)\n",
+    "lon1 = np.repeat(longitudes, n_points)\n",
+    "\n",
+    "# Tile the whole array n_points times for lat2, lon2\n",
+    "lat2 = np.tile(latitudes, n_points)\n",
+    "lon2 = np.tile(longitudes, n_points)\n",
+    "\n",
+    "# Calculates angular separation between two spherical coords\n",
+    "# This can be lat/lon or ra/dec\n",
+    "# Taken from astropy\n",
+    "def angular_separation_deg(lon1, lat1, lon2, lat2):\n",
+    "    lon1 = np.deg2rad(lon1)\n",
+    "    lon2 = np.deg2rad(lon2)\n",
+    "    lat1 = np.deg2rad(lat1)\n",
+    "    lat2 = np.deg2rad(lat2)\n",
+    "    \n",
+    "    sdlon = np.sin(lon2 - lon1)\n",
+    "    cdlon = np.cos(lon2 - lon1)\n",
+    "    slat1 = np.sin(lat1)\n",
+    "    slat2 = np.sin(lat2)\n",
+    "    clat1 = np.cos(lat1)\n",
+    "    clat2 = np.cos(lat2)\n",
+    "\n",
+    "    num1 = clat2 * sdlon\n",
+    "    num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n",
+    "    denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n",
+    "\n",
+    "    return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n",
+    "\n",
+    "# Compute the pairwise angular separations\n",
+    "angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n",
+    "\n",
+    "# Reshape the result into a matrix form\n",
+    "angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n",
+    "\n",
+    "def check_symmetric(a, rtol=1e-05, atol=1e-07):\n",
+    "    return np.allclose(a, a.T, rtol=rtol, atol=atol)\n",
+    "\n",
+    "print(\"Symmetric?\")\n",
+    "print(check_symmetric(angular_separations_matrix))\n",
+    "print(angular_separations_matrix.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c66e8c1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SDSS_FOV = 0.088\n",
+    "\n",
+    "THRESH = SDSS_FOV * 4\n",
+    "\n",
+    "\"\"\"\n",
+    "Initial clustering phase using just RA DEC, not WCS polygon footprints.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n",
+    "labels = clustering.fit_predict(angular_separations_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "51da93b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  3%|█▍                                        | 13/377 [00:00<00:21, 16.71it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 10, i: 1 IoU: 0.25944657797281734\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  8%|███▏                                      | 29/377 [00:01<00:15, 21.76it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 23, i: 2 IoU: 0.4090236242653364\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  9%|███▉                                      | 35/377 [00:02<00:15, 22.08it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 29, i: 2 IoU: 0.9007704140270892\n",
+      "FAIL label: 32, i: 2 IoU: 0.4056510771965438\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 10%|████▏                                     | 38/377 [00:02<00:18, 18.22it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 36, i: 2 IoU: 0.5762689111619909\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 15%|██████▏                                   | 55/377 [00:02<00:10, 31.49it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 47, i: 1 IoU: 0.9250531562187404\n",
+      "FAIL label: 48, i: 1 IoU: 0.8360586649509192\n",
+      "FAIL label: 53, i: 1 IoU: 0.10008227926664898\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 19%|████████▏                                 | 73/377 [00:03<00:09, 33.33it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 68, i: 2 IoU: 0.49064401487956755\n",
+      "FAIL label: 69, i: 2 IoU: 0.9662692069365345\n",
+      "FAIL label: 71, i: 1 IoU: 0.09857753647298885\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 22%|█████████▎                                | 84/377 [00:03<00:07, 38.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 74, i: 3 IoU: 0.5845239934642943\n",
+      "FAIL label: 81, i: 2 IoU: 0.7402716532101037\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28%|███████████▋                             | 107/377 [00:03<00:03, 69.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 91, i: 2 IoU: 0.30092583437382314\n",
+      "FAIL label: 106, i: 1 IoU: 0.5437761463648566\n",
+      "FAIL label: 110, i: 1 IoU: 0.978096219321612\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 43%|█████████████████▎                      | 163/377 [00:04<00:01, 124.92it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 137, i: 1 IoU: 0.5768840711253176\n",
+      "FAIL label: 138, i: 1 IoU: 0.426858068191846\n",
+      "FAIL label: 142, i: 1 IoU: 1.0\n",
+      "FAIL label: 151, i: 1 IoU: 0.6865076393310577\n",
+      "FAIL label: 162, i: 1 IoU: 0.40902362440677925\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████| 377/377 [00:04<00:00, 82.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL label: 166, i: 2 IoU: 0.3312197305714273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "failed_labels = []\n",
+    "failed_paths = []\n",
+    "\n",
+    "for label in tqdm(np.unique(labels)):\n",
+    "    polys = [(all_polys[i], valid_fits_paths[i]) for i in range(len(labels)) if labels[i] == label]\n",
+    "    if len(polys) > 1:\n",
+    "        total_poly = polys[0][0]\n",
+    "        for i in range(1, len(polys)):\n",
+    "            new_poly = polys[i][0]\n",
+    "            new_path = polys[i][1]\n",
+    "            if total_poly.intersects_poly(new_poly):\n",
+    "                union_over_max = total_poly.intersection(new_poly).area() / new_poly.area()\n",
+    "                print(f\"FAIL label: {label}, i: {i} IoU: {union_over_max}\")\n",
+    "                failed_labels.append(label)\n",
+    "                failed_paths.append(new_path)\n",
+    "                continue\n",
+    "            else:\n",
+    "                total_poly = total_poly.union(new_poly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "46c6217a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./data/cube_center_run4203_camcol6_f746_73-5-800-800.fits',\n",
+       " './data/cube_center_run2700_camcol2_f56_86-5-800-800.fits',\n",
+       " './data/cube_center_run4198_camcol6_f243_3-5-800-800.fits',\n",
+       " './data/cube_center_run5658_camcol2_f33_25-5-800-800.fits',\n",
+       " './data/cube_center_run4933_camcol2_f695_81-5-800-800.fits',\n",
+       " './data/cube_center_run2709_camcol5_f236_72-5-800-800.fits',\n",
+       " './data/cube_center_run5637_camcol4_f385_1-5-800-800.fits',\n",
+       " './data/cube_center_run5759_camcol4_f118_72-5-800-800.fits',\n",
+       " './data/cube_center_run2700_camcol5_f163_86-5-800-800.fits',\n",
+       " './data/cube_center_run3434_camcol4_f456_34-5-800-800.fits',\n",
+       " './data/cube_center_run5792_camcol6_f342_73-5-800-800.fits',\n",
+       " './data/cube_center_run5918_camcol5_f278_69-5-800-800.fits',\n",
+       " './data/cube_center_run4128_camcol3_f475_1-5-800-800.fits',\n",
+       " './data/cube_center_run5590_camcol2_f272_56-5-800-800.fits',\n",
+       " './data/cube_center_run5836_camcol6_f545_52-5-800-800.fits',\n",
+       " './data/cube_center_run4933_camcol3_f554_85-5-800-800.fits',\n",
+       " './data/cube_center_run4128_camcol5_f348_8-5-800-800.fits',\n",
+       " './data/cube_center_run2886_camcol1_f164_78-5-800-800.fits',\n",
+       " './data/cube_center_run5642_camcol1_f374_80-5-800-800.fits',\n",
+       " './data/cube_center_run4188_camcol5_f87_42-5-800-800.fits',\n",
+       " './data/cube_center_run5628_camcol5_f238_69-5-800-800.fits',\n",
+       " './data/cube_center_run5781_camcol5_f291_76-5-800-800.fits']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failed_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4af5240f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['tiny_train.jsonl', 'full_train.jsonl', 'full_test.jsonl', 'tiny_test.jsonl']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir('./splits')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "abce2e5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Path to the JSONL file\n",
+    "file_path = './splits/full_train.jsonl'\n",
+    "\n",
+    "# Read the JSONL file into a DataFrame\n",
+    "df_train = pd.read_json(file_path, lines=True)\n",
+    "\n",
+    "# Path to the JSONL file\n",
+    "file_path = './splits/full_test.jsonl'\n",
+    "\n",
+    "# Read the JSONL file into a DataFrame\n",
+    "df_test = pd.read_json(file_path, lines=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c5844d7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df_train, df_test])\n",
+    "df = df[~df['image'].isin(failed_paths)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b94657a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Assuming df is your DataFrame\n",
+    "# df = pd.DataFrame(...) # Your DataFrame should already be defined\n",
+    "\n",
+    "# Perform an 85/15 train-test split\n",
+    "train_df, test_df = train_test_split(df, test_size=0.15, random_state=42)\n",
+    "\n",
+    "# Save the train and test DataFrames to CSV files\n",
+    "train_df.to_csv('full_train.csv', index=False)\n",
+    "test_df.to_csv('full_test.csv', index=False)\n",
+    "\n",
+    "print(\"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "987b9fd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "1\n",
+      "Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Path to the JSONL file\n",
+    "file_path = './splits/tiny_train.jsonl'\n",
+    "\n",
+    "# Read the JSONL file into a DataFrame\n",
+    "df_train = pd.read_json(file_path, lines=True)\n",
+    "\n",
+    "# Path to the JSONL file\n",
+    "file_path = './splits/tiny_test.jsonl'\n",
+    "\n",
+    "# Read the JSONL file into a DataFrame\n",
+    "df_test = pd.read_json(file_path, lines=True)\n",
+    "\n",
+    "print(len(df_train))\n",
+    "print(len(df_test))\n",
+    "\n",
+    "# Save the train and test DataFrames to CSV files\n",
+    "df_train.to_csv('tiny_train.csv', index=False)\n",
+    "df_test.to_csv('tiny_test.csv', index=False)\n",
+    "\n",
+    "print(\"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "dd0209ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV file has been converted and saved as JSONL at ./splits/tiny_train.jsonl\n",
+      "CSV file has been converted and saved as JSONL at ./splits/tiny_test.jsonl\n",
+      "CSV file has been converted and saved as JSONL at ./splits/full_train.jsonl\n",
+      "CSV file has been converted and saved as JSONL at ./splits/full_test.jsonl\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "names = [\"./splits/tiny_train\", \"./splits/tiny_test\", \"./splits/full_train\", \"./splits/full_test\"]\n",
+    "\n",
+    "for name in names:\n",
+    "\n",
+    "    # Step 1: Load the CSV file into a DataFrame\n",
+    "    csv_file_path = f'{name}.csv'  # Replace with your actual CSV file path\n",
+    "    df = pd.read_csv(csv_file_path)\n",
+    "\n",
+    "    # Step 2: Save the DataFrame as a JSONL file\n",
+    "    jsonl_file_path = f'{name}.jsonl'  # Replace with your desired output file path\n",
+    "    df.to_json(jsonl_file_path, orient='records', lines=True)\n",
+    "\n",
+    "    print(f\"CSV file has been converted and saved as JSONL at {jsonl_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfafd26c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}