diff --git a/.gitattributes b/.gitattributes index 28df5f900b358436f0267334b3e3e9af33f917ba..92df97807b494859167e8b707de54f4eac6d5334 100644 --- a/.gitattributes +++ b/.gitattributes @@ -49,6 +49,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.gif filter=lfs diff=lfs merge=lfs -text *.png filter=lfs diff=lfs merge=lfs -text *.tiff filter=lfs diff=lfs merge=lfs -text +*.fits filter=lfs diff=lfs merge=lfs -text +*.fit filter=lfs diff=lfs merge=lfs -text # Image files - compressed *.jpg filter=lfs diff=lfs merge=lfs -text *.jpeg filter=lfs diff=lfs merge=lfs -text diff --git a/GBI-16-2D-Legacy.py b/GBI-16-2D-Legacy.py new file mode 100644 index 0000000000000000000000000000000000000000..1e5b5287f33f61aba4da916507e44bce7c4bc274 --- /dev/null +++ b/GBI-16-2D-Legacy.py @@ -0,0 +1,189 @@ +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 + +_DESCRIPTION = ( +"GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression " +"dataset assembled by Maireles-González et al. (Publications of the " +"Astronomical Society of the Pacific, 135:094502, 2023 September; doi: " +"[https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/" +"acf6e0)). It contains 226 FITS images from 5 different ground-based " +"telescope/cameras with a varying amount of entropy per image." +) + +_HOMEPAGE = "https://google.github.io/AstroCompress" + +_LICENSE = "CC BY 4.0" + +_URL = "https://huggingface.co/datasets/AstroCompress/GBI-16-2D-Legacy/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-16-2D-Legacy" + +class GBI_16_2D_Legacy(datasets.GeneratorBasedBuilder): + """GBI-16-2D-Legacy Dataset""" + + VERSION = datasets.Version("1.0.0") + + 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( + { + # Images are variable size across the dataset + # so use the Image type here, returning as + # numpy uint16 + "image": datasets.Image(decode=True, mode="I;16"), + "telescope": datasets.Value("string"), + "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({"telescope": item["telescope"], + "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-16-2D-Legacy 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) as hdul: + # 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") diff --git a/README.md b/README.md index 7da79263d0e701a53a0d662d245c4fc3db39b287..fbc9b2a7f7fb4f1f507afde5bdbc68dd857df3d6 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,90 @@ ---- -license: cc-by-4.0 ---- +--- +license: cc-by-4.0 +pretty_name: Ground-based 2d images assembled in Maireles-González et al. +tags: +- astronomy +- compression +- images +dataset_info: + config_name: tiny + features: + - name: image + dtype: + image: + mode: I;16 + - name: telescope + dtype: string + - name: image_id + dtype: string + splits: + - name: train + num_bytes: 307620692 + num_examples: 10 + - name: test + num_bytes: 168984694 + num_examples: 5 + download_size: 238361934 + dataset_size: 476605386 +--- + +# GBI-16-2D-Legacy Dataset + +GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression dataset assembled by Maireles-González et al. (Publications of the Astronomical Society of the Pacific, 135:094502, 2023 September; doi: [https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/acf6e0)). It contains 226 FITS images from 5 different ground-based telescope/cameras with a varying amount of entropy per image. + +# 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. + +## 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 +``` + +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-16-2D-Legacy", "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-16-2D-Legacy +``` + +Then `cd SBI-16-3D` and start python like: + +```python +from datasets import load_dataset +dataset = load_dataset("./GBI-16-2D-Legacy", "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 # -> (9, 2048, 2048) +``` + +Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. 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sha256:f6fe36d873cf7cf5f80e5230bc278f270be56706e9320065c4a016a1c1ddee5a +size 12847680 diff --git a/data/WHT/wht20191213_02902833_img0.fits b/data/WHT/wht20191213_02902833_img0.fits new file mode 100644 index 0000000000000000000000000000000000000000..7e55b1063b5fd07dc2fa9e1f538b1f873c7e5278 --- /dev/null +++ b/data/WHT/wht20191213_02902833_img0.fits @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:739841c60f15b706118917ac7483afbacdd383d175ad1b5292637a884412f4cc +size 10748160 diff --git a/raw_to_fits.py b/raw_to_fits.py new file mode 100644 index 0000000000000000000000000000000000000000..d4d86c87a730ad7d427c086e3fdb82d6cade65ed --- /dev/null +++ b/raw_to_fits.py @@ -0,0 +1,73 @@ +""" +Converts RAW files to FITS files + +adapted from: + https://github.com/OscarMaireles/experiment-notebook/tree/398832acf73c31edb97c3c5055a6394d89ed96a3/plugins + plugins/plugin_fits/raw_to_fits.py + + pip install enb astropy +""" + +import os +import numpy as np +import glob +from pathlib import Path + +from astropy.io import fits + +import enb.sets as sets + + +class RawToFits(sets.FileVersionTable, sets.FilePropertiesTable): + def __init__(self, original_base_dir, version_base_dir): + super().__init__( + original_base_dir=original_base_dir, + version_base_dir=version_base_dir, + original_properties_table=sets.FilePropertiesTable(), + version_name=self.version_name) + + def Version(input_path): + RAWS=glob.glob(str(input_path / "*.raw")) + + for i in range(len(RAWS)): + file=RAWS[i] + fname = file.split("/")[-1] + print(file) + + params = fname[0:-4].split("-") + dimensions=params[-1].split('x') + telescope = file.split("/")[2] + + frames=int(dimensions[-3]) + columns=int(dimensions[-2]) + rows=int(dimensions[-1]) + astype=params[-2] + swap = False + if astype == "u16be": + astype = "uint16" + swap = True + else: + raise ValueError(f"Unknown data type: {astype}") + name=params[0] + if os.path.exists(f'./fits_data/{name}.fits'): + continue + img=np.fromfile(open(file), dtype = f'{astype}', count = -1) + if swap: + img.byteswap(inplace=True) + array=np.reshape(img,(frames,columns,rows)) + + hdr = fits.Header() + hdr['NAME'] = name + hdr['TELESCOP'] = telescope + hdr['COMMENT'] = "made from RAW data from Maireles-Gonzalez et al." + hdu = fits.PrimaryHDU(array, header=hdr) + hdu.writeto(f'./fits_data/{telescope}/{name}.fits', overwrite=True) + + +if __name__ == '__main__': + + for telescope in ["INT", "WHT", "TJO", "LCO", "JKT"]: + print(f"Converting {telescope}...") + input_path=Path(f'./data/RAW/{telescope}/u16be/') + RawToFits.Version(input_path) + diff --git a/splits/full_test.jsonl b/splits/full_test.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..93a63573ac063ac869e93c6851dfcfadfdef2c3c --- /dev/null +++ b/splits/full_test.jsonl @@ -0,0 +1,32 @@ +{"image_id": "int20130929_01017179_img0", "image": "./data/INT/int20130929_01017179_img0.fits", "telescope": "INT"} +{"image_id": "int20041114_00431659_img0", "image": "./data/INT/int20041114_00431659_img0.fits", "telescope": "INT"} +{"image_id": "int20070715_00576093_img0", "image": "./data/INT/int20070715_00576093_img0.fits", "telescope": "INT"} +{"image_id": "int20141225_01103344_img0", "image": "./data/INT/int20141225_01103344_img0.fits", "telescope": "INT"} +{"image_id": "int20121204_00951952_img0", "image": "./data/INT/int20121204_00951952_img0.fits", "telescope": "INT"} +{"image_id": "int20121204_00951960_img0", "image": "./data/INT/int20121204_00951960_img0.fits", "telescope": "INT"} +{"image_id": "int20100601_00735654_img0", "image": "./data/INT/int20100601_00735654_img0.fits", "telescope": "INT"} +{"image_id": "int20040907_00422051_img0", "image": "./data/INT/int20040907_00422051_img0.fits", "telescope": "INT"} +{"image_id": "jkt20001125_00150413_img0", "image": "./data/JKT/jkt20001125_00150413_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20001128_00150535_img0", "image": "./data/JKT/jkt20001128_00150535_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20020118_00203367_img0", "image": "./data/JKT/jkt20020118_00203367_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20020121_00203630_img0", "image": "./data/JKT/jkt20020121_00203630_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20011227_00202663_img0", "image": "./data/JKT/jkt20011227_00202663_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20020824_00249138_img0", "image": "./data/JKT/jkt20020824_00249138_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20010714_00182613_img0", "image": "./data/JKT/jkt20010714_00182613_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20010930_00194717_img0", "image": "./data/JKT/jkt20010930_00194717_img0.fits", "telescope": "JKT"} +{"image_id": "jkt20000619_00124825_img0", "image": "./data/JKT/jkt20000619_00124825_img0.fits", "telescope": "JKT"} +{"image_id": "cpt0m407", "image": "./data/LCO/cpt0m407.fits", "telescope": "LCO"} +{"image_id": "elp0m411", "image": "./data/LCO/elp0m411.fits", "telescope": "LCO"} +{"image_id": "TJO2459273.28285_V_imr_img0", "image": "./data/TJO/TJO2459273.28285_V_imr_img0.fits", "telescope": "TJO"} +{"image_id": "M45_NO_B_002_60s_img0", "image": "./data/TJO/M45_NO_B_002_60s_img0.fits", "telescope": "TJO"} +{"image_id": "M45_E_B_001_60s_img0", "image": "./data/TJO/M45_E_B_001_60s_img0.fits", "telescope": "TJO"} +{"image_id": "M45_SO_B_003_60s_img0", "image": "./data/TJO/M45_SO_B_003_60s_img0.fits", "telescope": "TJO"} +{"image_id": "M45_SO_V_002_60s_img0", "image": "./data/TJO/M45_SO_V_002_60s_img0.fits", "telescope": "TJO"} +{"image_id": "wht20070421_00958626_img0", "image": "./data/WHT/wht20070421_00958626_img0.fits", "telescope": "WHT"} +{"image_id": "wht20190902_02873539_img0", "image": "./data/WHT/wht20190902_02873539_img0.fits", "telescope": "WHT"} +{"image_id": "wht20170324_02518209_img0", "image": "./data/WHT/wht20170324_02518209_img0.fits", "telescope": "WHT"} +{"image_id": "wht20120123_01754958_img0", "image": "./data/WHT/wht20120123_01754958_img0.fits", "telescope": "WHT"} +{"image_id": "wht20170320_02517561_img0", "image": "./data/WHT/wht20170320_02517561_img0.fits", "telescope": "WHT"} +{"image_id": "wht20170312_02516142_img0", "image": "./data/WHT/wht20170312_02516142_img0.fits", "telescope": "WHT"} +{"image_id": "wht20170329_02519088_img0", "image": "./data/WHT/wht20170329_02519088_img0.fits", "telescope": "WHT"} +{"image_id": "wht20191213_02902833_img0", "image": "./data/WHT/wht20191213_02902833_img0.fits", "telescope": "WHT"} diff --git a/splits/full_train.jsonl b/splits/full_train.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cf69cfa7b114210aa81c72e453346766a3453da4 --- /dev/null +++ b/splits/full_train.jsonl @@ -0,0 +1,120 @@ +{"image_id": "int20141225_01103345_img0", "image": "./data/INT/int20141225_01103345_img0.fits", "telescope": "INT"} +{"image_id": "int20040907_00422050_img0", "image": 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