utils made, baselines uploaded

GBI-16-2D-Legacy.py

@@ -127,63 +127,4 @@ class GBI_16_2D_Legacy(datasets.GeneratorBasedBuilder):
                 # this data is natively formatted like (1, 4200, 2154)
                 # just use the 2D image
                 image_data = hdul[0].data[0,:,:].tolist()
-                yield task_instance_key, {**{"image": image_data}, **item}
-
-def make_split_jsonl_files(config_type="tiny", data_dir="./data", 
-                           telescope_subdirectories=["INT", "JKT","LCO", "TJO", "WHT"],
-                          outdir="./splits", seed=42):
-    """
-    Create jsonl files for the GBI-16-2D-Legacy 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.
-    telescope_subdirectories: list, default=["INT", "JKT","LCO", "TJO", "WHT"]
-        The subdirectories of the data_dir that contain the FITS files for each telescope.
-    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 = []
-    for subdir in telescope_subdirectories:
-        fits_files.extend(glob(os.path.join(data_dir, subdir, "*.fits")))
-
-    random.shuffle(fits_files)
-
-    if config_type == "tiny":
-        train_files = []
-        test_files = []
-        for subdir in telescope_subdirectories:
-            subdir_files = [f for f in fits_files if subdir in f]
-            train_files.extend(subdir_files[:2])
-            test_files.extend(subdir_files[2:3])
-    elif config_type == "full":
-        train_files = []
-        test_files = []
-        for subdir in telescope_subdirectories:
-            subdir_files = [f for f in fits_files if subdir in f]
-            split_idx = int(0.8 * len(subdir_files))
-            train_files.extend(subdir_files[:split_idx])
-            test_files.extend(subdir_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 files:
-                print(file, flush=True, end="...")
-                with fits.open(file, memmap=False) as hdul:
-                    image_id = os.path.basename(file).split(".fits")[0]
-                
-                    telescope = hdul[0].header.get('TELESCOP', 'UNKNOWN')
-                    item = {"image_id": image_id, "image": file, "telescope": telescope}
-                    out_f.write(json.dumps(item) + "\n")
-
-    create_jsonl(train_files, "train")
-    create_jsonl(test_files, "test")
+                yield task_instance_key, {**{"image": image_data}, **item}

baseline_results_2d.csv

@@ -0,0 +1,18 @@
+,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/LCO/ogg2m001.fits,,,,,,,,,,,,,,,
+./data/LCO/coj0m403.fits,,,,,,,,,,,,,,,
+./data/LCO/lsc0m409.fits,,,,,,,,,,,,,,,
+./data/LCO/cpt0m407.fits,,,,,,,,,,,,,,,
+./data/LCO/ogg0m404.fits,,,,,,,,,,,,,,,
+./data/LCO/coj2m002.fits,,,,,,,,,,,,,,,
+./data/LCO/coj0m405.fits,,,,,,,,,,,,,,,
+./data/LCO/lsc0m412.fits,,,,,,,,,,,,,,,
+./data/LCO/elp0m411.fits,,,,,,,,,,,,,,,
+./data/LCO/ogg2m001.fits_hdu0,0.7228008563701923,43.20007514953613,0.5066413879394531,0.7646298922025241,0.4445362091064453,0.2911219596862793,0.7561227651742789,0.7407228946685791,0.6194639205932617,0.7517606295072116,0.14260530471801758,0.22156763076782227,0.8046292818509615,0.07075810432434082,0.044094085693359375
+./data/LCO/coj0m403.fits_hdu0,0.6845067483669605,67.48599791526794,0.7721624374389648,0.7279621187963822,0.6539788246154785,0.4639432430267334,0.7265686903312462,1.1587002277374268,0.9693844318389893,0.7221424525065708,0.20934057235717773,0.1679387092590332,0.763859698709029,0.08583760261535645,0.06677007675170898
+./data/LCO/lsc0m409.fits_hdu0,0.6610539990143784,46.64720416069031,0.6685361862182617,0.7128655617076376,0.6489887237548828,0.44870448112487793,0.7006576245555041,1.0780272483825684,0.9041671752929688,0.7085028880160018,0.2137744426727295,0.17609882354736328,0.7456735623937075,0.08374571800231934,0.06576395034790039
+./data/LCO/cpt0m407.fits_hdu0,0.6880637730944651,47.97620415687561,0.7337470054626465,0.7368511324984539,0.6511118412017822,0.4521498680114746,0.7296890702303649,1.1380155086517334,0.9582910537719727,0.7261818701202072,0.20433974266052246,0.15947604179382324,0.7707059030998763,0.08529806137084961,0.06650400161743164
+./data/LCO/ogg0m404.fits_hdu0,0.7123647488114564,52.49390196800232,0.5467383861541748,0.7629273744298856,0.6397292613983154,0.4389636516571045,0.7612617223736086,1.2026004791259766,1.0056798458099365,0.7506706695462276,0.19949769973754883,0.1565086841583252,0.797979143717146,0.08756709098815918,0.06797599792480469
+./data/LCO/coj2m002.fits_hdu0,0.48990149864783655,31.05488395690918,0.4487757682800293,0.5265641432542068,0.3766806125640869,0.2681262493133545,0.5202474740835337,0.564016580581665,0.465839147567749,0.5321988619290865,0.11781430244445801,0.0965888500213623,0.5791156475360577,0.054184913635253906,0.04157567024230957
+./data/LCO/coj0m405.fits_hdu0,0.7249306682900433,59.124993562698364,0.736142635345459,0.772575383136209,0.6583375930786133,0.4584355354309082,0.7651275087449753,1.193568468093872,1.008777141571045,0.758441196080705,0.20306921005249023,0.16224908828735352,0.8085132756938003,0.09140729904174805,0.07058954238891602
+./data/LCO/lsc0m412.fits_hdu0,0.695435702690167,55.14425826072693,0.7698564529418945,0.7426116131822047,0.6599817276000977,0.44414687156677246,0.7424180520929963,1.2062599658966064,1.0164411067962646,0.7327258051658163,0.20291376113891602,0.1620502471923828,0.7762316041473407,0.08937621116638184,0.06872892379760742

utils/__init__.py

@@ -0,0 +1 @@
+from .create_splits import *

utils/create_splits.py

@@ -0,0 +1,77 @@
+import os
+import random
+from glob import glob
+import json
+from huggingface_hub import hf_hub_download
+
+
+from astropy.io import fits
+import datasets
+from datasets import DownloadManager
+from fsspec.core import url_to_fs
+from tqdm import tqdm
+
+
+def make_split_jsonl_files(config_type="tiny", data_dir="./data", 
+                           telescope_subdirectories=["INT", "JKT","LCO", "TJO", "WHT"],
+                          outdir="./splits", seed=42):
+    """
+    Create jsonl files for the GBI-16-2D-Legacy 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.
+    telescope_subdirectories: list, default=["INT", "JKT","LCO", "TJO", "WHT"]
+        The subdirectories of the data_dir that contain the FITS files for each telescope.
+    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 = []
+    for subdir in telescope_subdirectories:
+        fits_files.extend(glob(os.path.join(data_dir, subdir, "*.fits")))
+
+    random.shuffle(fits_files)
+
+    if config_type == "tiny":
+        train_files = []
+        test_files = []
+        for subdir in telescope_subdirectories:
+            subdir_files = [f for f in fits_files if subdir in f]
+            train_files.extend(subdir_files[:2])
+            test_files.extend(subdir_files[2:3])
+    elif config_type == "full":
+        train_files = []
+        test_files = []
+        for subdir in telescope_subdirectories:
+            subdir_files = [f for f in fits_files if subdir in f]
+            split_idx = int(0.8 * len(subdir_files))
+            train_files.extend(subdir_files[:split_idx])
+            test_files.extend(subdir_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 files:
+                print(file, flush=True, end="...")
+                with fits.open(file, memmap=False) as hdul:
+                    image_id = os.path.basename(file).split(".fits")[0]
+                
+                    telescope = hdul[0].header.get('TELESCOP', 'UNKNOWN')
+                    item = {"image_id": image_id, "image": file, "telescope": telescope}
+                    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")

utils/eval_baselines.py

@@ -0,0 +1,136 @@
+"""
+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.
+
+Only runs on LCO data.
+"""
+
+
+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
+import glob
+
+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 find_matching_files(root_dir='./data/LCO'):
+    # Use glob to recursively find all .fits files
+    pattern = os.path.join(root_dir, '**', '*.fits')
+    fits_files = glob.glob(pattern, recursive=True)
+    return fits_files
+
+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):
+        for hdu_idx in [0]:
+            with fits.open(path) as hdul:
+                if dim == '2d':
+                    arr = hdul[hdu_idx].data[0]
+                else:
+                    raise RuntimeError(f"{dim} not applicable.")
+
+            ct += 1
+            if ct % 1 == 0:
+                print(df.mean())
+                df.to_csv(save_path)
+
+            for algo in ALL_CODECS.keys():
+                try:
+                    if algo == "JPEG_2K" and dim != '2d':
+                        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"_hdu{hdu_idx}", column] = value
+
+                except Exception as e:
+                    print(f"Failed at {path} under exception {e}.")
+            
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
+    parser.add_argument(
+        "dimension", 
+        choices=['2d'],
+        help="Specify whether the data is 2d, or; not applicable here: 3dt (3d time dimension), or 3dw (3d wavelength dimension)."
+    )
+    args = parser.parse_args()
+    dim = args.dimension.lower()
+    
+    # RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER
+
+    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)