diff --git a/GBI-16-2D.py b/GBI-16-2D.py index 846a6be6941ee28a5c04303ecaecf5123e91eec7..dfa3bd540476952d35192ba69922287a7d4b25f3 100644 --- a/GBI-16-2D.py +++ b/GBI-16-2D.py @@ -13,6 +13,8 @@ from huggingface_hub import hf_hub_download import datasets from datasets import DownloadManager +from utils import read_lris + _DESCRIPTION = ( """SBI-16-2D is a dataset which is part of the AstroCompress project. """ @@ -167,529 +169,4 @@ class GBI_16_2D(datasets.GeneratorBasedBuilder): else: data = hdul[0].data image_data = data[:, :] - yield task_instance_key, {**{"image": image_data}, **item} - - -def make_split_jsonl_files( - config_type="tiny", data_dir="./data", outdir="./splits", seed=42 -): - """ - Create jsonl files for the GBI-16-2D 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 files: - print(file, flush=True, end="...") - image_id = os.path.basename(file).split(".fits")[0] - with fits.open(file, memmap=False) as hdul: - if len(hdul) > 1: - # multiextension ... paste together - data, header = read_lris(file) - dim_1 = data.shape[0] - dim_2 = data.shape[1] - header = fits.header.Header(header) - else: - dim_1 = hdul[0].header.get("NAXIS1", 0) - dim_2 = hdul[0].header.get("NAXIS2", 0) - header = hdul[0].header - - ras = header.get("RA", "0") - ra = float( - Angle(f"{ras} hours").to_string(unit=u.degree, decimal=True) - ) - decs = header.get("DEC", "0") - dec = float( - Angle(f"{decs} degrees").to_string(unit=u.degree, decimal=True) - ) - pixscale = header.get("CD1_2", 0.135) - rotation = header.get("ROTPOSN", 0.0) - exposure_time = header.get("TTIME", 0.0) - item = { - "image_id": image_id, - "image": file, - "ra": ra, - "dec": dec, - "pixscale": pixscale, - "rotation_angle": rotation, - "dim_1": dim_1, - "dim_2": dim_2, - "exposure_time": exposure_time, - } - out_f.write(json.dumps(item) + "\n") - - create_jsonl(train_files, "train") - create_jsonl(test_files, "test") - - -def read_lris(raw_file, det=None, TRIM=False): - """ - Modified from pypeit.spectrographs.keck_lris.read_lris -- Jon Brown, Josh Bloom - cf. https://github.com/KerryPaterson/Imaging_pipelines - - Read a raw LRIS data frame (one or more detectors) - Packed in a multi-extension HDU - Based on readmhdufits.pro - - Parameters - ---------- - raw_file : str - Filename - det : int, optional - Detector number; Default = both - TRIM : bool, optional - Trim the image? - - Returns - ------- - array : ndarray - Combined image - header : FITS header - sections : list - List of datasec, oscansec, ampsec sections - """ - - hdu = fits.open(raw_file) - head0 = hdu[0].header - - # Get post, pre-pix values - precol = head0["PRECOL"] - postpix = head0["POSTPIX"] - preline = head0["PRELINE"] - postline = head0["POSTLINE"] - - # get the detector - # this just checks if its the blue one and assumes red if not - # note the red fits headers don't even have this keyword??? - if head0["INSTRUME"] == "LRISBLUE": - redchip = False - else: - redchip = True - - # Setup for datasec, oscansec - dsec = [] - osec = [] - nxdata_sum = 0 - - # get the x and y binning factors... - binning = head0["BINNING"] - xbin, ybin = [int(ibin) for ibin in binning.split(",")] - - # First read over the header info to determine the size of the output array... - n_ext = len(hdu) - 1 # Number of extensions (usually 4) - xcol = [] - xmax = 0 - ymax = 0 - xmin = 10000 - ymin = 10000 - for i in np.arange(1, n_ext + 1): - theader = hdu[i].header - detsec = theader["DETSEC"] - if detsec != "0": - # parse the DETSEC keyword to determine the size of the array. - x1, x2, y1, y2 = np.array(load_sections(detsec, fmt_iraf=False)).flatten() - - # find the range of detector space occupied by the data - # [xmin:xmax,ymin:ymax] - xt = max(x2, x1) - xmax = max(xt, xmax) - yt = max(y2, y1) - ymax = max(yt, ymax) - - # find the min size of the array - xt = min(x1, x2) - xmin = min(xmin, xt) - yt = min(y1, y2) - ymin = min(ymin, yt) - # Save - xcol.append(xt) - - # determine the output array size... - nx = xmax - xmin + 1 - ny = ymax - ymin + 1 - - # change size for binning... - nx = nx // xbin - ny = ny // ybin - - # Update PRECOL and POSTPIX - precol = precol // xbin - postpix = postpix // xbin - - # Deal with detectors - if det in [1, 2]: - nx = nx // 2 - n_ext = n_ext // 2 - det_idx = np.arange(n_ext, dtype=np.int) + (det - 1) * n_ext - elif det is None: - det_idx = np.arange(n_ext).astype(int) - else: - raise ValueError("Bad value for det") - - # change size for pre/postscan... - if not TRIM: - nx += n_ext * (precol + postpix) - ny += preline + postline - - # allocate output array... - array = np.zeros((nx, ny), dtype="uint16") - gain_array = np.zeros((nx, ny), dtype="uint16") - order = np.argsort(np.array(xcol)) - - # insert extensions into master image... - for kk, i in enumerate(order[det_idx]): - - # grab complete extension... - data, gaindata, predata, postdata, x1, y1 = lris_read_amp( - hdu, i + 1, redchip=redchip - ) - - # insert components into output array... - if not TRIM: - # insert predata... - buf = predata.shape - nxpre = buf[0] - xs = kk * precol - xe = xs + nxpre - - array[xs:xe, :] = predata - gain_array[xs:xe, :] = predata - - # insert data... - buf = data.shape - nxdata = buf[0] - nydata = buf[1] - - # JB: have to track the number of xpixels - xs = n_ext * precol + nxdata_sum - xe = xs + nxdata - - # now log how many pixels that was - nxdata_sum += nxdata - - # Data section - # section = '[{:d}:{:d},{:d}:{:d}]'.format(preline,nydata-postline, xs, xe) # Eliminate lines - section = "[{:d}:{:d},{:d}:{:d}]".format( - preline, nydata, xs, xe - ) # DONT eliminate lines - - dsec.append(section) - array[xs:xe, :] = data # Include postlines - gain_array[xs:xe, :] = gaindata # Include postlines - - # ; insert postdata... - buf = postdata.shape - nxpost = buf[0] - xs = nx - n_ext * postpix + kk * postpix - xe = xs + nxpost - section = "[:,{:d}:{:d}]".format(xs, xe) - osec.append(section) - - array[xs:xe, :] = postdata - gain_array[xs:xe, :] = postdata - - else: - buf = data.shape - nxdata = buf[0] - nydata = buf[1] - - xs = (x1 - xmin) // xbin - xe = xs + nxdata - ys = (y1 - ymin) // ybin - ye = ys + nydata - postline - - yin1 = preline - yin2 = nydata - postline - - array[xs:xe, ys:ye] = data[:, yin1:yin2] - gain_array[xs:xe, ys:ye] = gaindata[:, yin1:yin2] - - # make sure BZERO is a valid integer for IRAF - obzero = head0["BZERO"] - head0["O_BZERO"] = obzero - head0["BZERO"] = 32768 - obzero - - # Return, transposing array back to goofy Python indexing - return array.T, head0 - - -def lris_read_amp(inp, ext, redchip=False, applygain=True): - """ - Modified from pypeit.spectrographs.keck_lris.lris_read_amp -- Jon Brown, Josh Bloom - cf. https://github.com/KerryPaterson/Imaging_pipelines - Read one amplifier of an LRIS multi-extension FITS image - - Parameters - ---------- - inp: tuple - (str,int) filename, extension - (hdu,int) FITS hdu, extension - - Returns - ------- - data - predata - postdata - x1 - y1 - - ;------------------------------------------------------------------------ - function lris_read_amp, filename, ext, $ - linebias=linebias, nobias=nobias, $ - predata=predata, postdata=postdata, header=header, $ - x1=x1, x2=x2, y1=y1, y2=y2, GAINDATA=gaindata - ;------------------------------------------------------------------------ - ; Read one amp from LRIS mHDU image - ;------------------------------------------------------------------------ - """ - # Parse input - if isinstance(inp, str): - hdu = fits.open(inp) - else: - hdu = inp - - # Get the pre and post pix values - # for LRIS red POSTLINE = 20, POSTPIX = 80, PRELINE = 0, PRECOL = 12 - head0 = hdu[0].header - precol = head0["precol"] - postpix = head0["postpix"] - - # Deal with binning - binning = head0["BINNING"] - xbin, ybin = [int(ibin) for ibin in binning.split(",")] - precol = precol // xbin - postpix = postpix // xbin - - # get entire extension... - temp = hdu[ext].data.transpose() # Silly Python nrow,ncol formatting - tsize = temp.shape - nxt = tsize[0] - - # parse the DETSEC keyword to determine the size of the array. - header = hdu[ext].header - detsec = header["DETSEC"] - x1, x2, y1, y2 = np.array(load_sections(detsec, fmt_iraf=False)).flatten() - - # parse the DATASEC keyword to determine the size of the science region (unbinned) - datasec = header["DATASEC"] - xdata1, xdata2, ydata1, ydata2 = np.array( - load_sections(datasec, fmt_iraf=False) - ).flatten() - - # grab the components... - predata = temp[0:precol, :] - # datasec appears to have the x value for the keywords that are zero - # based. This is only true in the image header extensions - # not true in the main header. They also appear inconsistent between - # LRISr and LRISb! - # data = temp[xdata1-1:xdata2-1,*] - # data = temp[xdata1:xdata2+1, :] - - # JB: LRIS-R is windowed differently, so the default pypeit checks fail - # xshape is calculated from datasec. - # For blue, its 1024, - # For red, the chip dimensions are different AND the observations are windowed - # In windowed mode each amplifier has differently sized data sections - if not redchip: - xshape = 1024 // xbin # blue - else: - xshape = xdata2 - xdata1 + 1 // xbin # red - - # do some sanity checks - if (xdata1 - 1) != precol: - # msgs.error("Something wrong in LRIS datasec or precol") - errStr = "Something wrong in LRIS datasec or precol" - print(errStr) - - if (xshape + precol + postpix) != temp.shape[0]: - # msgs.error("Wrong size for in LRIS detector somewhere. Funny binning?") - errStr = "Wrong size for in LRIS detector somewhere. Funny binning?" - print(errStr) - - data = temp[precol : precol + xshape, :] - postdata = temp[nxt - postpix : nxt, :] - - # flip in X as needed... - if x1 > x2: - xt = x2 - x2 = x1 - x1 = xt - data = np.flipud(data) # reverse(temporary(data),1) - - # flip in Y as needed... - if y1 > y2: - yt = y2 - y2 = y1 - y1 = yt - data = np.fliplr(data) - predata = np.fliplr(predata) - postdata = np.fliplr(postdata) - - # dummy gain data since we're keeping as uint16 - gaindata = 0.0 * data + 1.0 - - return data, gaindata, predata, postdata, x1, y1 - - -def load_sections(string, fmt_iraf=True): - """ - Modified from pypit.core.parse.load_sections -- Jon Brown, Josh Bloom - cf. https://github.com/KerryPaterson/Imaging_pipelines - From the input string, return the coordinate sections - - Parameters - ---------- - string : str - character string of the form [x1:x2,y1:y2] - x1 = left pixel - x2 = right pixel - y1 = bottom pixel - y2 = top pixel - fmt_iraf : bool - Is the variable string in IRAF format (True) or - python format (False) - - Returns - ------- - sections : list (or None) - the detector sections - """ - xyrng = string.strip("[]()").split(",") - if xyrng[0] == ":": - xyarrx = [0, 0] - else: - xyarrx = xyrng[0].split(":") - # If a lower/upper limit on the array slicing is not given (e.g. [:100] has no lower index specified), - # set the lower/upper limit to be the first/last index. - if len(xyarrx[0]) == 0: - xyarrx[0] = 0 - if len(xyarrx[1]) == 0: - xyarrx[1] = -1 - if xyrng[1] == ":": - xyarry = [0, 0] - else: - xyarry = xyrng[1].split(":") - # If a lower/upper limit on the array slicing is not given (e.g. [5:] has no upper index specified), - # set the lower/upper limit to be the first/last index. - if len(xyarry[0]) == 0: - xyarry[0] = 0 - if len(xyarry[1]) == 0: - xyarry[1] = -1 - if fmt_iraf: - xmin = max(0, int(xyarry[0]) - 1) - xmax = int(xyarry[1]) - ymin = max(0, int(xyarrx[0]) - 1) - ymax = int(xyarrx[1]) - else: - xmin = max(0, int(xyarrx[0])) - xmax = int(xyarrx[1]) - ymin = max(0, int(xyarry[0])) - ymax = int(xyarry[1]) - return [[xmin, xmax], [ymin, ymax]] - - -def sec2slice( - subarray, one_indexed=False, include_end=False, require_dim=None, transpose=False -): - """ - Modified from pypit.core.parse.sec2slice -- Jon Brown - - Convert a string representation of an array subsection (slice) into - a list of slice objects. - - Args: - subarray (str): - The string to convert. Should have the form of normal slice - operation, 'start:stop:step'. The parser ignores whether or - not the string has the brackets '[]', but the string must - contain the appropriate ':' and ',' characters. - one_indexed (:obj:`bool`, optional): - The string should be interpreted as 1-indexed. Default - is to assume python indexing. - include_end (:obj:`bool`, optional): - **If** the end is defined, adjust the slice such that - the last element is included. Default is to exclude the - last element as with normal python slicing. - require_dim (:obj:`int`, optional): - Test if the string indicates the slice along the proper - number of dimensions. - transpose (:obj:`bool`, optional): - Transpose the order of the returned slices. The - following are equivalent:: - - tslices = parse_sec2slice('[:10,10:]')[::-1] - tslices = parse_sec2slice('[:10,10:]', transpose=True) - - Returns: - tuple: A tuple of slice objects, one per dimension of the - prospective array. - - Raises: - TypeError: - Raised if the input `subarray` is not a string. - ValueError: - Raised if the string does not match the required - dimensionality or if the string does not look like a - slice. - """ - # Check it's a string - if not isinstance(subarray, (str, bytes)): - raise TypeError("Can only parse string-based subarray sections.") - # Remove brackets if they're included - sections = subarray.strip("[]").split(",") - # Check the dimensionality - ndim = len(sections) - if require_dim is not None and ndim != require_dim: - raise ValueError( - "Number of slices ({0}) in {1} does not match ".format(ndim, subarray) - + "required dimensions ({0}).".format(require_dim) - ) - # Convert the slice of each dimension from a string to a slice - # object - slices = [] - for s in sections: - # Must be able to find the colon - if ":" not in s: - raise ValueError("Unrecognized slice string: {0}".format(s)) - # Initial conversion - _s = [None if x == "" else int(x) for x in s.split(":")] - if len(_s) > 3: - raise ValueError( - "String as too many sections. 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"LR.20101107.31322", "image": "./data/LR.20101107.31322.fits", "ra": 346.12, "dec": 3.91483, "pixscale": 0.135, "rotation_angle": -90.00073737, "dim_1": 2520, "dim_2": 3768, "exposure_time": 300} +{"image_id": "LR.20060725.44412", "image": "./data/LR.20060725.44412.fits", "ra": 331.769, "dec": -27.8327, "pixscale": 0.135, "rotation_angle": -89.99927794, "dim_1": 2248, "dim_2": 2048, "exposure_time": 30} +{"image_id": "LR.20090219.46509", "image": "./data/LR.20090219.46509.fits", "ra": 168.265, "dec": 53.83, "pixscale": 0.135, "rotation_angle": 270.0000776, "dim_1": 2248, "dim_2": 2048, "exposure_time": 60} +{"image_id": "LR.20071010.27833", "image": "./data/LR.20071010.27833.fits", "ra": 301.85, "dec": 10.9477, "pixscale": 0.135, "rotation_angle": -59.99971138, "dim_1": 2248, "dim_2": 2048, "exposure_time": 30} +{"image_id": "LR.20070718.43492", "image": "./data/LR.20070718.43492.fits", "ra": 297.698, "dec": -32.4042, "pixscale": 0.135, "rotation_angle": -90.00033622, "dim_1": 2248, "dim_2": 2048, "exposure_time": 300} +{"image_id": "LR.20070718.48242", "image": "./data/LR.20070718.48242.fits", "ra": 323.797, "dec": -24.8009, "pixscale": 0.135, "rotation_angle": -89.99993128, "dim_1": 2248, "dim_2": 2048, "exposure_time": 180} +{"image_id": "LR.20080212.28534", "image": "./data/LR.20080212.28534.fits", "ra": 64.2811, "dec": 18.189, "pixscale": 0.135, "rotation_angle": -90.00033671, "dim_1": 2248, "dim_2": 2048, "exposure_time": 180} +{"image_id": "LR.20051204.57105", "image": "./data/LR.20051204.57105.fits", "ra": 139.052, "dec": -16.3044, "pixscale": 0.135, "rotation_angle": -64.99967613, "dim_1": 2248, "dim_2": 2048, "exposure_time": 180} +{"image_id": "LR.20070416.24302", "image": "./data/LR.20070416.24302.fits", "ra": 107.344, "dec": 1.08406, "pixscale": 0.135, "rotation_angle": -0.0003293, "dim_1": 1124, "dim_2": 1024, "exposure_time": 180} +{"image_id": "LR.20070416.41356", "image": "./data/LR.20070416.41356.fits", "ra": 181.474, "dec": 40.1767, "pixscale": 0.135, "rotation_angle": -0.00095779, "dim_1": 2248, "dim_2": 2048, "exposure_time": 180} +{"image_id": "LR.20100708.40098", "image": "./data/LR.20100708.40098.fits", "ra": 263.508, "dec": 49.2332, "pixscale": 0.135, "rotation_angle": -89.99946212, "dim_1": 2520, "dim_2": 3768, "exposure_time": 280} +{"image_id": "LR.20070718.49274", "image": "./data/LR.20070718.49274.fits", "ra": 358.276, "dec": 46.5316, "pixscale": 0.135, "rotation_angle": 270.00021957, "dim_1": 2248, "dim_2": 2048, "exposure_time": 180} +{"image_id": "LR.20101107.42250", "image": "./data/LR.20101107.42250.fits", "ra": 41.0949, "dec": -2.15944, "pixscale": 0.135, "rotation_angle": -89.99976162, "dim_1": 2520, "dim_2": 3768, "exposure_time": 250} +{"image_id": "LR.20071213.52189", "image": "./data/LR.20071213.52189.fits", "ra": 181.104, "dec": -1.20378, "pixscale": 0.135, "rotation_angle": -90.0003472, "dim_1": 2248, "dim_2": 2048, "exposure_time": 270} +{"image_id": "LR.20060530.36483", "image": "./data/LR.20060530.36483.fits", "ra": 232.826, "dec": 0.281917, "pixscale": 0.135, "rotation_angle": -90.00084097, "dim_1": 2248, "dim_2": 1000, "exposure_time": 300} diff --git a/splits/tiny_test.jsonl b/splits/tiny_test.jsonl index c0d49fd97d6c0c5d1dbfa22ac8aaa2138c192557..20ae56cb6f5521625b6cc6cef2b41e164db11dc1 100644 --- a/splits/tiny_test.jsonl +++ b/splits/tiny_test.jsonl @@ -1 +1 @@ -{"image_id": "LR.20100708.41739", "image": "./data/LR.20100708.41739.fits", "ra": 264.942, "dec": 27.3245, "pixscale": 0.135, "rotation_angle": -89.9999583, "dim_1": 2520, "dim_2": 3768, "exposure_time": 270} +{"image_id": "LR.20060921.21065", "image": "./data/LR.20060921.21065.fits", "ra": 235.394, "dec": 62.0243, "pixscale": 0.135, "rotation_angle": 269.99938225, "dim_1": 2248, "dim_2": 2048, "exposure_time": 600} diff --git a/splits/tiny_train.jsonl b/splits/tiny_train.jsonl index dcb37a70cd0b3bf9dc47899444b6cb14f64fd07a..789dea30a1ae77b4c627d8c191aecb04c569b93f 100644 --- a/splits/tiny_train.jsonl +++ b/splits/tiny_train.jsonl @@ -1,2 +1,2 @@ -{"image_id": "LR.20090219.53662", "image": "./data/LR.20090219.53662.fits", "ra": 258.967, "dec": 55.3849, "pixscale": 0.135, "rotation_angle": 270.00076907, "dim_1": 2248, "dim_2": 2048, "exposure_time": 270} -{"image_id": "LR.20071010.27305", "image": "./data/LR.20071010.27305.fits", "ra": 288.061, "dec": -32.4109, "pixscale": 0.135, "rotation_angle": -89.99965647, "dim_1": 600, "dim_2": 400, "exposure_time": 30} +{"image_id": "LR.20071213.50289", "image": "./data/LR.20071213.50289.fits", "ra": 173.263, "dec": 25.8968, "pixscale": 0.135, "rotation_angle": 269.99959316, "dim_1": 2248, "dim_2": 2048, "exposure_time": 270} +{"image_id": "LR.20101107.35717", "image": "./data/LR.20101107.35717.fits", "ra": 20.704, "dec": 5.83428, "pixscale": 0.135, "rotation_angle": -89.9997708, "dim_1": 2520, "dim_2": 3768, "exposure_time": 30} 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..2f8c01c69ff6a70bd35a63fb8b872c1c97e74ac0 --- /dev/null +++ b/utils/create_splits.py @@ -0,0 +1,544 @@ +import os +import random +from glob import glob +import json + +import numpy as np +from astropy.io import fits +from astropy.coordinates import Angle +from astropy import units as u +from fsspec.core import url_to_fs + +from huggingface_hub import hf_hub_download +import datasets +from datasets import DownloadManager + +from tqdm import tqdm + +def lris_read_amp(inp, ext, redchip=False, applygain=True): + """ + Modified from pypeit.spectrographs.keck_lris.lris_read_amp -- Jon Brown, Josh Bloom + cf. https://github.com/KerryPaterson/Imaging_pipelines + Read one amplifier of an LRIS multi-extension FITS image + + Parameters + ---------- + inp: tuple + (str,int) filename, extension + (hdu,int) FITS hdu, extension + + Returns + ------- + data + predata + postdata + x1 + y1 + + ;------------------------------------------------------------------------ + function lris_read_amp, filename, ext, $ + linebias=linebias, nobias=nobias, $ + predata=predata, postdata=postdata, header=header, $ + x1=x1, x2=x2, y1=y1, y2=y2, GAINDATA=gaindata + ;------------------------------------------------------------------------ + ; Read one amp from LRIS mHDU image + ;------------------------------------------------------------------------ + """ + # Parse input + if isinstance(inp, str): + hdu = fits.open(inp) + else: + hdu = inp + + # Get the pre and post pix values + # for LRIS red POSTLINE = 20, POSTPIX = 80, PRELINE = 0, PRECOL = 12 + head0 = hdu[0].header + precol = head0["precol"] + postpix = head0["postpix"] + + # Deal with binning + binning = head0["BINNING"] + xbin, ybin = [int(ibin) for ibin in binning.split(",")] + precol = precol // xbin + postpix = postpix // xbin + + # get entire extension... + temp = hdu[ext].data.transpose() # Silly Python nrow,ncol formatting + tsize = temp.shape + nxt = tsize[0] + + # parse the DETSEC keyword to determine the size of the array. + header = hdu[ext].header + detsec = header["DETSEC"] + x1, x2, y1, y2 = np.array(load_sections(detsec, fmt_iraf=False)).flatten() + + # parse the DATASEC keyword to determine the size of the science region (unbinned) + datasec = header["DATASEC"] + xdata1, xdata2, ydata1, ydata2 = np.array( + load_sections(datasec, fmt_iraf=False) + ).flatten() + + # grab the components... + predata = temp[0:precol, :] + # datasec appears to have the x value for the keywords that are zero + # based. This is only true in the image header extensions + # not true in the main header. They also appear inconsistent between + # LRISr and LRISb! + # data = temp[xdata1-1:xdata2-1,*] + # data = temp[xdata1:xdata2+1, :] + + # JB: LRIS-R is windowed differently, so the default pypeit checks fail + # xshape is calculated from datasec. + # For blue, its 1024, + # For red, the chip dimensions are different AND the observations are windowed + # In windowed mode each amplifier has differently sized data sections + if not redchip: + xshape = 1024 // xbin # blue + else: + xshape = xdata2 - xdata1 + 1 // xbin # red + + # do some sanity checks + if (xdata1 - 1) != precol: + # msgs.error("Something wrong in LRIS datasec or precol") + errStr = "Something wrong in LRIS datasec or precol" + print(errStr) + + if (xshape + precol + postpix) != temp.shape[0]: + # msgs.error("Wrong size for in LRIS detector somewhere. Funny binning?") + errStr = "Wrong size for in LRIS detector somewhere. Funny binning?" + print(errStr) + + data = temp[precol : precol + xshape, :] + postdata = temp[nxt - postpix : nxt, :] + + # flip in X as needed... + if x1 > x2: + xt = x2 + x2 = x1 + x1 = xt + data = np.flipud(data) # reverse(temporary(data),1) + + # flip in Y as needed... + if y1 > y2: + yt = y2 + y2 = y1 + y1 = yt + data = np.fliplr(data) + predata = np.fliplr(predata) + postdata = np.fliplr(postdata) + + # dummy gain data since we're keeping as uint16 + gaindata = 0.0 * data + 1.0 + + return data, gaindata, predata, postdata, x1, y1 + + +def load_sections(string, fmt_iraf=True): + """ + Modified from pypit.core.parse.load_sections -- Jon Brown, Josh Bloom + cf. https://github.com/KerryPaterson/Imaging_pipelines + From the input string, return the coordinate sections + + Parameters + ---------- + string : str + character string of the form [x1:x2,y1:y2] + x1 = left pixel + x2 = right pixel + y1 = bottom pixel + y2 = top pixel + fmt_iraf : bool + Is the variable string in IRAF format (True) or + python format (False) + + Returns + ------- + sections : list (or None) + the detector sections + """ + xyrng = string.strip("[]()").split(",") + if xyrng[0] == ":": + xyarrx = [0, 0] + else: + xyarrx = xyrng[0].split(":") + # If a lower/upper limit on the array slicing is not given (e.g. [:100] has no lower index specified), + # set the lower/upper limit to be the first/last index. + if len(xyarrx[0]) == 0: + xyarrx[0] = 0 + if len(xyarrx[1]) == 0: + xyarrx[1] = -1 + if xyrng[1] == ":": + xyarry = [0, 0] + else: + xyarry = xyrng[1].split(":") + # If a lower/upper limit on the array slicing is not given (e.g. [5:] has no upper index specified), + # set the lower/upper limit to be the first/last index. + if len(xyarry[0]) == 0: + xyarry[0] = 0 + if len(xyarry[1]) == 0: + xyarry[1] = -1 + if fmt_iraf: + xmin = max(0, int(xyarry[0]) - 1) + xmax = int(xyarry[1]) + ymin = max(0, int(xyarrx[0]) - 1) + ymax = int(xyarrx[1]) + else: + xmin = max(0, int(xyarrx[0])) + xmax = int(xyarrx[1]) + ymin = max(0, int(xyarry[0])) + ymax = int(xyarry[1]) + return [[xmin, xmax], [ymin, ymax]] + + +def sec2slice( + subarray, one_indexed=False, include_end=False, require_dim=None, transpose=False +): + """ + Modified from pypit.core.parse.sec2slice -- Jon Brown + + Convert a string representation of an array subsection (slice) into + a list of slice objects. + + Args: + subarray (str): + The string to convert. Should have the form of normal slice + operation, 'start:stop:step'. The parser ignores whether or + not the string has the brackets '[]', but the string must + contain the appropriate ':' and ',' characters. + one_indexed (:obj:`bool`, optional): + The string should be interpreted as 1-indexed. Default + is to assume python indexing. + include_end (:obj:`bool`, optional): + **If** the end is defined, adjust the slice such that + the last element is included. Default is to exclude the + last element as with normal python slicing. + require_dim (:obj:`int`, optional): + Test if the string indicates the slice along the proper + number of dimensions. + transpose (:obj:`bool`, optional): + Transpose the order of the returned slices. The + following are equivalent:: + + tslices = parse_sec2slice('[:10,10:]')[::-1] + tslices = parse_sec2slice('[:10,10:]', transpose=True) + + Returns: + tuple: A tuple of slice objects, one per dimension of the + prospective array. + + Raises: + TypeError: + Raised if the input `subarray` is not a string. + ValueError: + Raised if the string does not match the required + dimensionality or if the string does not look like a + slice. + """ + # Check it's a string + if not isinstance(subarray, (str, bytes)): + raise TypeError("Can only parse string-based subarray sections.") + # Remove brackets if they're included + sections = subarray.strip("[]").split(",") + # Check the dimensionality + ndim = len(sections) + if require_dim is not None and ndim != require_dim: + raise ValueError( + "Number of slices ({0}) in {1} does not match ".format(ndim, subarray) + + "required dimensions ({0}).".format(require_dim) + ) + # Convert the slice of each dimension from a string to a slice + # object + slices = [] + for s in sections: + # Must be able to find the colon + if ":" not in s: + raise ValueError("Unrecognized slice string: {0}".format(s)) + # Initial conversion + _s = [None if x == "" else int(x) for x in s.split(":")] + if len(_s) > 3: + raise ValueError( + "String as too many sections. Must have format 'start:stop:step'." + ) + if len(_s) < 3: + # Include step + _s += [None] + if one_indexed: + # Decrement to convert from 1- to 0-indexing + _s = [None if x is None else x - 1 for x in _s] + if include_end and _s[1] is not None: + # Increment to include last + _s[1] += 1 + # Append the new slice + slices += [slice(*_s)] + return tuple(slices[::-1] if transpose else slices) + +def read_lris(raw_file, det=None, TRIM=False): + """ + Modified from pypeit.spectrographs.keck_lris.read_lris -- Jon Brown, Josh Bloom + cf. https://github.com/KerryPaterson/Imaging_pipelines + + Read a raw LRIS data frame (one or more detectors) + Packed in a multi-extension HDU + Based on readmhdufits.pro + + Parameters + ---------- + raw_file : str + Filename + det : int, optional + Detector number; Default = both + TRIM : bool, optional + Trim the image? + + Returns + ------- + array : ndarray + Combined image + header : FITS header + sections : list + List of datasec, oscansec, ampsec sections + """ + + hdu = fits.open(raw_file) + head0 = hdu[0].header + + # Get post, pre-pix values + precol = head0["PRECOL"] + postpix = head0["POSTPIX"] + preline = head0["PRELINE"] + postline = head0["POSTLINE"] + + # get the detector + # this just checks if its the blue one and assumes red if not + # note the red fits headers don't even have this keyword??? + if head0["INSTRUME"] == "LRISBLUE": + redchip = False + else: + redchip = True + + # Setup for datasec, oscansec + dsec = [] + osec = [] + nxdata_sum = 0 + + # get the x and y binning factors... + binning = head0["BINNING"] + xbin, ybin = [int(ibin) for ibin in binning.split(",")] + + # First read over the header info to determine the size of the output array... + n_ext = len(hdu) - 1 # Number of extensions (usually 4) + xcol = [] + xmax = 0 + ymax = 0 + xmin = 10000 + ymin = 10000 + for i in np.arange(1, n_ext + 1): + theader = hdu[i].header + detsec = theader["DETSEC"] + if detsec != "0": + # parse the DETSEC keyword to determine the size of the array. + x1, x2, y1, y2 = np.array(load_sections(detsec, fmt_iraf=False)).flatten() + + # find the range of detector space occupied by the data + # [xmin:xmax,ymin:ymax] + xt = max(x2, x1) + xmax = max(xt, xmax) + yt = max(y2, y1) + ymax = max(yt, ymax) + + # find the min size of the array + xt = min(x1, x2) + xmin = min(xmin, xt) + yt = min(y1, y2) + ymin = min(ymin, yt) + # Save + xcol.append(xt) + + # determine the output array size... + nx = xmax - xmin + 1 + ny = ymax - ymin + 1 + + # change size for binning... + nx = nx // xbin + ny = ny // ybin + + # Update PRECOL and POSTPIX + precol = precol // xbin + postpix = postpix // xbin + + # Deal with detectors + if det in [1, 2]: + nx = nx // 2 + n_ext = n_ext // 2 + det_idx = np.arange(n_ext, dtype=np.int) + (det - 1) * n_ext + elif det is None: + det_idx = np.arange(n_ext).astype(int) + else: + raise ValueError("Bad value for det") + + # change size for pre/postscan... + if not TRIM: + nx += n_ext * (precol + postpix) + ny += preline + postline + + # allocate output array... + array = np.zeros((nx, ny), dtype="uint16") + gain_array = np.zeros((nx, ny), dtype="uint16") + order = np.argsort(np.array(xcol)) + + # insert extensions into master image... + for kk, i in enumerate(order[det_idx]): + + # grab complete extension... + data, gaindata, predata, postdata, x1, y1 = lris_read_amp( + hdu, i + 1, redchip=redchip + ) + + # insert components into output array... + if not TRIM: + # insert predata... + buf = predata.shape + nxpre = buf[0] + xs = kk * precol + xe = xs + nxpre + + array[xs:xe, :] = predata + gain_array[xs:xe, :] = predata + + # insert data... + buf = data.shape + nxdata = buf[0] + nydata = buf[1] + + # JB: have to track the number of xpixels + xs = n_ext * precol + nxdata_sum + xe = xs + nxdata + + # now log how many pixels that was + nxdata_sum += nxdata + + # Data section + # section = '[{:d}:{:d},{:d}:{:d}]'.format(preline,nydata-postline, xs, xe) # Eliminate lines + section = "[{:d}:{:d},{:d}:{:d}]".format( + preline, nydata, xs, xe + ) # DONT eliminate lines + + dsec.append(section) + array[xs:xe, :] = data # Include postlines + gain_array[xs:xe, :] = gaindata # Include postlines + + # ; insert postdata... + buf = postdata.shape + nxpost = buf[0] + xs = nx - n_ext * postpix + kk * postpix + xe = xs + nxpost + section = "[:,{:d}:{:d}]".format(xs, xe) + osec.append(section) + + array[xs:xe, :] = postdata + gain_array[xs:xe, :] = postdata + + else: + buf = data.shape + nxdata = buf[0] + nydata = buf[1] + + xs = (x1 - xmin) // xbin + xe = xs + nxdata + ys = (y1 - ymin) // ybin + ye = ys + nydata - postline + + yin1 = preline + yin2 = nydata - postline + + array[xs:xe, ys:ye] = data[:, yin1:yin2] + gain_array[xs:xe, ys:ye] = gaindata[:, yin1:yin2] + + # make sure BZERO is a valid integer for IRAF + obzero = head0["BZERO"] + head0["O_BZERO"] = obzero + head0["BZERO"] = 32768 - obzero + + # Return, transposing array back to goofy Python indexing + return array.T, head0 + + +def make_split_jsonl_files( + config_type="tiny", data_dir="./data", outdir="./splits", seed=42 +): + """ + Create jsonl files for the GBI-16-2D 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="...") + image_id = os.path.basename(file).split(".fits")[0] + with fits.open(file, memmap=False) as hdul: + if len(hdul) > 1: + # multiextension ... paste together + data, header = read_lris(file) + dim_1 = data.shape[0] + dim_2 = data.shape[1] + header = fits.header.Header(header) + else: + dim_1 = hdul[0].header.get("NAXIS1", 0) + dim_2 = hdul[0].header.get("NAXIS2", 0) + header = hdul[0].header + + ras = header.get("RA", "0") + ra = float( + Angle(f"{ras} hours").to_string(unit=u.degree, decimal=True) + ) + decs = header.get("DEC", "0") + dec = float( + Angle(f"{decs} degrees").to_string(unit=u.degree, decimal=True) + ) + pixscale = header.get("CD1_2", 0.135) + rotation = header.get("ROTPOSN", 0.0) + exposure_time = header.get("TTIME", 0.0) + item = { + "image_id": image_id, + "image": file, + "ra": ra, + "dec": dec, + "pixscale": pixscale, + "rotation_angle": rotation, + "dim_1": dim_1, + "dim_2": dim_2, + "exposure_time": exposure_time, + } + 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..fc67ae1471d0c70dd5aa220e8df178f12589c2b4 --- /dev/null +++ b/utils/eval_baselines.py @@ -0,0 +1,139 @@ +""" +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 +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'): + # 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): + with fits.open(path) as hdul: + if len(hdul) == 1: + hdu_indices = [0] + else: + hdu_indices = [1, 2, 3, 4] + for hdu_idx in hdu_indices: + with fits.open(path) as hdul: + if dim == '2d': + arr = hdul[hdu_idx].data + else: + raise RuntimeError(f"{dim} not applicable.") + + ct += 1 + if ct % 10 == 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) \ No newline at end of file