diff --git a/Dockerfile b/Dockerfile index 4486fd9..55754df 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.10-slim as base +FROM python:3.11-slim as base LABEL maintainer="jnation@lco.global" # use bash diff --git a/datalab/datalab_session/data_operations/stacking.py b/datalab/datalab_session/data_operations/stacking.py index bf462a4..26b2475 100644 --- a/datalab/datalab_session/data_operations/stacking.py +++ b/datalab/datalab_session/data_operations/stacking.py @@ -9,6 +9,10 @@ from datalab.datalab_session.exceptions import ClientAlertException from datalab.datalab_session.utils.format import Format from datalab.datalab_session.utils.file_utils import crop_arrays +from reproject import reproject_adaptive +from astropy.io import fits +from reproject.mosaicking import find_optimal_celestial_wcs +from astropy.wcs import WCS log = logging.getLogger() log.setLevel(logging.INFO) @@ -46,12 +50,137 @@ def wizard_description(): 'type': Format.FITS, 'minimum': Stack.MINIMUM_NUMBER_OF_INPUTS, 'maximum': Stack.MAXIMUM_NUMBER_OF_INPUTS, + }, + 'stacking_mode': { + 'name': 'Stacking Mode', + 'description': 'Choose simple stacking or reprojection before stacking', + 'type': 'select', + 'options': ['simple', 'reproject'], + 'default': 'simple' } } } return description + + def find_optimal_reference(self, images): + """ + images: list of InputDataHandler + returns: optimized_wcs, optimized_shape + """ + + image_hdus = [img.sci_hdu for img in images] + + wcs_opt, shape_out = find_optimal_celestial_wcs(image_hdus) + return wcs_opt, shape_out + + def crop_bbox_from_footprint(self, footprint: np.ndarray): + """ + Return a bbox (r0,r1,c0,c1) such that the bbox edges contain no invalid pixels. + footprint: 2D array where non-zero means covered. + """ + mask = footprint != 0 + if not mask.any(): + raise ValueError("Empty footprint") + + # start with the loose envelope (any valid somewhere) + rows = np.where(mask.any(axis=1))[0] + cols = np.where(mask.any(axis=0))[0] + r0, r1 = rows[0], rows[-1] + 1 + c0, c1 = cols[0], cols[-1] + 1 + + # now shrink until the *edges* are fully valid + changed = True + while changed: + changed = False + + # top edge + if r0 < r1 and not mask[r0, c0:c1].all(): + r0 += 1 + changed = True + + # bottom edge + if r0 < r1 and not mask[r1 - 1, c0:c1].all(): + r1 -= 1 + changed = True + + # left edge + if c0 < c1 and not mask[r0:r1, c0].all(): + c0 += 1 + changed = True + + # right edge + if c0 < c1 and not mask[r0:r1, c1 - 1].all(): + c1 -= 1 + changed = True + + if r0 >= r1 or c0 >= c1: + raise ValueError("No rectangular region without invalid pixels on the edges") + + if not changed: + break + + log.info(f'crop bbox from footprint (shrunk): {r0}, {r1}, {c0}, {c1}') + return r0, r1, c0, c1 + + def intersect_bboxes(self, bboxes): + """ + bboxes: iterable of (r0,r1,c0,c1). Returns intersection bbox. + """ + r0 = max(b[0] for b in bboxes) + r1 = min(b[1] for b in bboxes) + c0 = max(b[2] for b in bboxes) + c1 = min(b[3] for b in bboxes) + if r0 >= r1 or c0 >= c1: + raise ValueError("No overlapping valid region across images") + log.info(f'intersection bbox: {r0}, {r1}, {c0}, {c1}') + return r0, r1, c0, c1 + + def crop(self, img, bbox): + r0, r1, c0, c1 = bbox + return np.ascontiguousarray(img[r0:r1, c0:c1]) + + def prepare_for_sum(self, images, footprints): + """ + images: list of 2D float arrays (len=3) + footprints: list of 2D {0,1} arrays (len=3) + Returns: cropped_images, common_bbox + """ + + + per_bbox = [self.crop_bbox_from_footprint(fp) for fp in footprints] + common_bbox = self.intersect_bboxes(per_bbox) + cropped = [self.crop(im, common_bbox) for im in images] + + log.info(f'cropped: {cropped[0].shape}, common_bbox: {common_bbox}') + return cropped, common_bbox + + def reproject_images_to_reference(self, input_fits, optimized_wcs, optimized_shape): + """ + input_fits: list of InputDataHandler + optimized_wcs: WCS object for the optimal reference frame + optimized_shape: (ny, nx) shape + + returns: + reprojected_arrays: list of 2D float arrays reprojected to the optimal reference frame + footprints: list of 2D numpy arrays indicating valid data regions in + """ + reprojected_arrays = [] + footprints = [] + for img in input_fits: + array, footprint = reproject_adaptive( + img.sci_hdu, + optimized_wcs, + shape_out=optimized_shape, + return_footprint=True, + conserve_flux=True + ) + reprojected_arrays.append(array) + footprints.append(footprint) + + return reprojected_arrays, footprints def operate(self, submitter: User): + stacking_mode = self.input_data.get("stacking_mode") input_files = self._validate_inputs( input_key='input_files', minimum_inputs=self.MINIMUM_NUMBER_OF_INPUTS @@ -65,14 +194,29 @@ def operate(self, submitter: User): log.info(f'input fits list in normalization: {input_fits_list}') self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_MIDPOINT'] * (index / len(input_files))) - cropped_data, _ = crop_arrays([image.sci_data for image in input_fits_list]) + if stacking_mode == "reproject": + optimized_wcs, optimized_shape = self.find_optimal_reference(input_fits_list) + arrays, footprints = self.reproject_images_to_reference(input_fits_list, optimized_wcs, optimized_shape) + cropped_data, _ = self.prepare_for_sum(arrays, footprints) + + optimized_header = optimized_wcs.to_header() + header = input_fits_list[0].sci_hdu.header.copy() + header.update(optimized_header) + + else: + arrays = [image.sci_data for image in input_fits_list] + cropped_data, _ = crop_arrays(arrays) + header = input_fits_list[0].sci_hdu.header.copy() + self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_PERCENTAGE_COMPLETION']) - stacked_sum = np.sum(cropped_data, axis=0) - self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_OUTPUT_PERCENTAGE_COMPLETION']) + + stacked_sum = np.nansum(np.stack(cropped_data), axis=0) - output = FITSOutputHandler(self.cache_key, stacked_sum, self.temp, comment, data_header=input_fits_list[0].sci_hdu.header.copy()).create_and_save_data_products(Format.FITS) + self.set_operation_progress(Stack.PROGRESS_STEPS['STACKING_OUTPUT_PERCENTAGE_COMPLETION']) + output = FITSOutputHandler(self.cache_key, stacked_sum, self.temp, comment, data_header=header).create_and_save_data_products(Format.FITS) log.info(f'Stacked output: {output}') + self.set_output(output) self.set_operation_progress(Stack.PROGRESS_STEPS['OUTPUT_PERCENTAGE_COMPLETION']) self.set_status('COMPLETED') diff --git a/poetry.lock b/poetry.lock index e9664d2..1fb9f3c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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