def write(self, outfile, clobber=True): from galsim._pyfits import pyfits output = pyfits.HDUList() output.append(pyfits.PrimaryHDU()) for i, screen in enumerate(self.screens): output.append(pyfits.ImageHDU(np.array(screen))) output[-1].name = "Layer %i" % i output.writeto(outfile, clobber=clobber)
def WriteMEDS(obj_list, file_name, clobber=True): """ Writes a MEDS file from a list of MultiExposureObjects. Arguments: ---------- @param obj_list: List of MultiExposureObjects @param file_name: Name of meds file to be written @param clobber Setting `clobber=True` when `file_name` is given will silently overwrite existing files. (Default `clobber = True`.) """ from galsim._pyfits import pyfits # initialise the catalog cat = {} cat['id'] = [] cat['box_size'] = [] cat['ra'] = [] cat['dec'] = [] cat['ncutout'] = [] cat['start_row'] = [] cat['dudrow'] = [] cat['dudcol'] = [] cat['dvdrow'] = [] cat['dvdcol'] = [] cat['row0'] = [] cat['col0'] = [] cat['psf_box_size'] = [] cat['psf_start_row'] = [] # initialise the image vectors vec = {} vec['image'] = [] vec['seg'] = [] vec['weight'] = [] vec['psf'] = [] # initialise the image vector index n_vec = 0 psf_n_vec = 0 # get number of objects n_obj = len(obj_list) # loop over objects for obj in obj_list: # initialise the start indices for each image start_rows = np.ones(MAX_NCUTOUTS) * EMPTY_START_INDEX psf_start_rows = np.ones(MAX_NCUTOUTS) * EMPTY_START_INDEX dudrow = np.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag dudcol = np.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag dvdrow = np.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag dvdcol = np.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag row0 = np.ones(MAX_NCUTOUTS) * EMPTY_SHIFT col0 = np.ones(MAX_NCUTOUTS) * EMPTY_SHIFT # get the number of cutouts (exposures) n_cutout = obj.n_cutouts # append the catalog for this object cat['id'].append(obj.id) cat['box_size'].append(obj.box_size) # TODO: If the config defines a world position, get the right ra, dec here. cat['ra'].append(0.) cat['dec'].append(0.) cat['ncutout'].append(n_cutout) cat['psf_box_size'].append(obj.psf_box_size) # loop over cutouts for i in range(n_cutout): # assign the start row to the end of image vector start_rows[i] = n_vec psf_start_rows[i] = psf_n_vec # update n_vec to point to the end of image vector n_vec += len(obj.images[i].array.flatten()) if obj.psf is not None: psf_n_vec += len(obj.psf[i].array.flatten()) # append the image vectors vec['image'].append(obj.images[i].array.flatten()) vec['seg'].append(obj.seg[i].array.flatten()) vec['weight'].append(obj.weight[i].array.flatten()) vec['psf'].append(obj.psf[i].array.flatten()) # append the Jacobian # col == x # row == y dudcol[i] = obj.wcs[i].dudx dudrow[i] = obj.wcs[i].dudy dvdcol[i] = obj.wcs[i].dvdx dvdrow[i] = obj.wcs[i].dvdy col0[i] = obj.wcs[i].origin.x row0[i] = obj.wcs[i].origin.y # check if we are running out of memory if sys.getsizeof(vec) > MAX_MEMORY: raise MemoryError( 'Running out of memory > %1.0fGB ' % MAX_MEMORY / 1.e9 + '- you can increase the limit by changing MAX_MEMORY') # update the start rows fields in the catalog cat['start_row'].append(start_rows) cat['psf_start_row'].append(psf_start_rows) # add lists of Jacobians cat['dudrow'].append(dudrow) cat['dudcol'].append(dudcol) cat['dvdrow'].append(dvdrow) cat['dvdcol'].append(dvdcol) cat['row0'].append(row0) cat['col0'].append(col0) # concatenate list to one big vector vec['image'] = np.concatenate(vec['image']) vec['seg'] = np.concatenate(vec['seg']) vec['weight'] = np.concatenate(vec['weight']) vec['psf'] = np.concatenate(vec['psf']) # get the primary HDU primary = pyfits.PrimaryHDU() # second hdu is the object_data # cf. https://github.com/esheldon/meds/wiki/MEDS-Format cols = [] cols.append(pyfits.Column(name='id', format='K', array=cat['id'])) cols.append(pyfits.Column(name='number', format='K', array=cat['id'])) cols.append(pyfits.Column(name='ra', format='D', array=cat['ra'])) cols.append(pyfits.Column(name='dec', format='D', array=cat['dec'])) cols.append( pyfits.Column(name='box_size', format='K', array=cat['box_size'])) cols.append(pyfits.Column(name='ncutout', format='K', array=cat['ncutout'])) cols.append( pyfits.Column(name='file_id', format='%dK' % MAX_NCUTOUTS, array=[1] * n_obj)) cols.append( pyfits.Column(name='start_row', format='%dK' % MAX_NCUTOUTS, array=np.array(cat['start_row']))) cols.append( pyfits.Column(name='orig_row', format='%dD' % MAX_NCUTOUTS, array=[[0] * MAX_NCUTOUTS] * n_obj)) cols.append( pyfits.Column(name='orig_col', format='%dD' % MAX_NCUTOUTS, array=[[0] * MAX_NCUTOUTS] * n_obj)) cols.append( pyfits.Column(name='orig_start_row', format='%dK' % MAX_NCUTOUTS, array=[[0] * MAX_NCUTOUTS] * n_obj)) cols.append( pyfits.Column(name='orig_start_col', format='%dK' % MAX_NCUTOUTS, array=[[0] * MAX_NCUTOUTS] * n_obj)) cols.append( pyfits.Column(name='cutout_row', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['row0']))) cols.append( pyfits.Column(name='cutout_col', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['col0']))) cols.append( pyfits.Column(name='dudrow', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['dudrow']))) cols.append( pyfits.Column(name='dudcol', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['dudcol']))) cols.append( pyfits.Column(name='dvdrow', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['dvdrow']))) cols.append( pyfits.Column(name='dvdcol', format='%dD' % MAX_NCUTOUTS, array=np.array(cat['dvdcol']))) cols.append( pyfits.Column(name='psf_box_size', format='K', array=cat['psf_box_size'])) cols.append( pyfits.Column(name='psf_start_row', format='%dK' % MAX_NCUTOUTS, array=np.array(cat['psf_start_row']))) # Depending on the version of pyfits, one of these should work: try: object_data = pyfits.BinTableHDU.from_columns(cols) object_data.name = 'object_data' except: # pragma: no cover object_data = pyfits.new_table(pyfits.ColDefs(cols)) object_data.update_ext_name('object_data') # third hdu is image_info cols = [] cols.append( pyfits.Column(name='image_path', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='image_ext', format='I', array=[0])) cols.append( pyfits.Column(name='weight_path', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='weight_ext', format='I', array=[0])) cols.append( pyfits.Column(name='seg_path', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='seg_ext', format='I', array=[0])) cols.append( pyfits.Column(name='bmask_path', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='bmask_ext', format='I', array=[0])) cols.append( pyfits.Column(name='bkg_path', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='bkg_ext', format='I', array=[0])) cols.append(pyfits.Column(name='image_id', format='K', array=[-1])) cols.append(pyfits.Column(name='image_flags', format='K', array=[-1])) cols.append(pyfits.Column(name='magzp', format='E', array=[30.])) cols.append(pyfits.Column(name='scale', format='E', array=[1.])) # TODO: Not sure if this is right! cols.append(pyfits.Column(name='position_offset', format='D', array=[0.])) try: image_info = pyfits.BinTableHDU.from_columns(cols) image_info.name = 'image_info' except: # pragma: no cover image_info = pyfits.new_table(pyfits.ColDefs(cols)) image_info.update_ext_name('image_info') # fourth hdu is metadata # default values? cols = [] cols.append(pyfits.Column(name='magzp_ref', format='E', array=[30.])) cols.append( pyfits.Column(name='DESDATA', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='cat_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='coadd_image_id', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='coadd_file', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='coadd_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='coadd_seg_hdu', format='K', array=[9999])) cols.append( pyfits.Column(name='coadd_srclist', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='coadd_wt_hdu', format='K', array=[9999])) cols.append( pyfits.Column(name='coaddcat_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='coaddseg_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='cutout_file', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='max_boxsize', format='A3', array=['-1'])) cols.append(pyfits.Column(name='medsconf', format='A3', array=['x'])) cols.append(pyfits.Column(name='min_boxsize', format='A2', array=['-1'])) cols.append(pyfits.Column(name='se_badpix_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='se_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='se_wt_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='seg_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='psf_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='sky_hdu', format='K', array=[9999])) cols.append(pyfits.Column(name='fake_coadd_seg', format='K', array=[9999])) try: metadata = pyfits.BinTableHDU.from_columns(cols) metadata.name = 'metadata' except: # pragma: no cover metadata = pyfits.new_table(pyfits.ColDefs(cols)) metadata.update_ext_name('metadata') # rest of HDUs are image vectors image_cutouts = pyfits.ImageHDU(vec['image'], name='image_cutouts') weight_cutouts = pyfits.ImageHDU(vec['weight'], name='weight_cutouts') seg_cutouts = pyfits.ImageHDU(vec['seg'], name='seg_cutouts') psf_cutouts = pyfits.ImageHDU(vec['psf'], name='psf') # write all hdu_list = pyfits.HDUList([ primary, object_data, image_info, metadata, image_cutouts, weight_cutouts, seg_cutouts, psf_cutouts ]) hdu_list.writeto(file_name, clobber=clobber)
(np.cos(alpha_phase) + 1j * np.sin(alpha_phase))) except TypeError: # Just have a scalar for alpha_mag alpha.append(alpha_mag * (np.cos(alpha_phase) + 1j * np.sin(alpha_phase))) powerlaw = np.array(powerlaw) alpha = np.array(alpha) return powerlaw, alpha if __name__ == '__main__': from galsim._pyfits import pyfits n = 10 m = 100 pscale = 1 rate = 1 paramcube = np.array([(0.85, 23.2, 259, 7600), (1.08, 5.7, 320, 16000)]) alpha_mag = 0.99 powerlaw, alpha = create_multilayer_arbase(n, m, pscale, rate, paramcube, alpha_mag) pl_output = pyfits.HDUList() pl_output.append(pyfits.PrimaryHDU(data=powerlaw.transpose())) pl_output.writeto('powerlaw.fits', clobber=True) alpha_output = pyfits.HDUList() alpha_output.append(pyfits.PrimaryHDU(data=alpha.real)) alpha_output.writeto('alpha.fits', clobber=True)
def write_meds(file_name, obj_list, clobber=True): """ @brief Writes the galaxy, weights, segmaps images to a MEDS file. Arguments: ---------- @param file_name: Name of meds file to be written @param obj_list: List of MultiExposureObjects @param clobber Setting `clobber=True` when `file_name` is given will silently overwrite existing files. (Default `clobber = True`.) """ import numpy import sys from galsim._pyfits import pyfits # initialise the catalog cat = {} cat['ncutout'] = [] cat['box_size'] = [] cat['start_row'] = [] cat['id'] = [] cat['dudrow'] = [] cat['dudcol'] = [] cat['dvdrow'] = [] cat['dvdcol'] = [] cat['row0'] = [] cat['col0'] = [] # initialise the image vectors vec = {} vec['image'] = [] vec['seg'] = [] vec['weight'] = [] # initialise the image vector index n_vec = 0 # get number of objects n_obj = len(obj_list) # loop over objects for obj in obj_list: # initialise the start indices for each image start_rows = numpy.ones(MAX_NCUTOUTS) * EMPTY_START_INDEX dudrow = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag dudcol = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag dvdrow = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_offdiag dvdcol = numpy.ones(MAX_NCUTOUTS) * EMPTY_JAC_diag row0 = numpy.ones(MAX_NCUTOUTS) * EMPTY_SHIFT col0 = numpy.ones(MAX_NCUTOUTS) * EMPTY_SHIFT # get the number of cutouts (exposures) n_cutout = obj.n_cutouts # append the catalog for this object cat['ncutout'].append(n_cutout) cat['box_size'].append(obj.box_size) cat['id'].append(obj.id) # loop over cutouts for i in range(n_cutout): # assign the start row to the end of image vector start_rows[i] = n_vec # update n_vec to point to the end of image vector n_vec += len(obj.images[i].array.flatten()) # append the image vectors vec['image'].append(obj.images[i].array.flatten()) vec['seg'].append(obj.segs[i].array.flatten()) vec['weight'].append(obj.weights[i].array.flatten()) # append the Jacobian dudrow[i] = obj.wcs[i].dudx dudcol[i] = obj.wcs[i].dudy dvdrow[i] = obj.wcs[i].dvdx dvdcol[i] = obj.wcs[i].dvdy row0[i] = obj.wcs[i].origin.x col0[i] = obj.wcs[i].origin.y # check if we are running out of memory if sys.getsizeof(vec) > MAX_MEMORY: raise MemoryError( 'Running out of memory > %1.0fGB ' % MAX_MEMORY / 1.e9 + '- you can increase the limit by changing MAX_MEMORY') # update the start rows fields in the catalog cat['start_row'].append(start_rows) # add lists of Jacobians cat['dudrow'].append(dudrow) cat['dudcol'].append(dudcol) cat['dvdrow'].append(dvdrow) cat['dvdcol'].append(dvdcol) cat['row0'].append(row0) cat['col0'].append(col0) # concatenate list to one big vector vec['image'] = numpy.concatenate(vec['image']) vec['seg'] = numpy.concatenate(vec['seg']) vec['weight'] = numpy.concatenate(vec['weight']) # get the primary HDU primary = pyfits.PrimaryHDU() # second hdu is the object_data cols = [] cols.append( pyfits.Column(name='ncutout', format='i4', array=cat['ncutout'])) cols.append(pyfits.Column(name='id', format='i4', array=cat['id'])) cols.append( pyfits.Column(name='box_size', format='i4', array=cat['box_size'])) cols.append(pyfits.Column(name='file_id', format='i4', array=[1] * n_obj)) cols.append( pyfits.Column(name='start_row', format='%di4' % MAX_NCUTOUTS, array=numpy.array(cat['start_row']))) cols.append(pyfits.Column(name='orig_row', format='f8', array=[1] * n_obj)) cols.append(pyfits.Column(name='orig_col', format='f8', array=[1] * n_obj)) cols.append( pyfits.Column(name='orig_start_row', format='i4', array=[1] * n_obj)) cols.append( pyfits.Column(name='orig_start_col', format='i4', array=[1] * n_obj)) cols.append( pyfits.Column(name='dudrow', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['dudrow']))) cols.append( pyfits.Column(name='dudcol', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['dudcol']))) cols.append( pyfits.Column(name='dvdrow', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['dvdrow']))) cols.append( pyfits.Column(name='dvdcol', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['dvdcol']))) cols.append( pyfits.Column(name='cutout_row', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['row0']))) cols.append( pyfits.Column(name='cutout_col', format='%df8' % MAX_NCUTOUTS, array=numpy.array(cat['col0']))) object_data = pyfits.new_table(pyfits.ColDefs(cols)) object_data.update_ext_name('object_data') # third hdu is image_info cols = [] cols.append( pyfits.Column(name='image_path', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='sky_path', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='seg_path', format='A256', array=['generated_by_galsim'])) image_info = pyfits.new_table(pyfits.ColDefs(cols)) image_info.update_ext_name('image_info') # fourth hdu is metadata cols = [] cols.append( pyfits.Column(name='cat_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='coadd_file', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='coadd_hdu', format='A1', array=['x'])) cols.append(pyfits.Column(name='coadd_seg_hdu', format='A1', array=['x'])) cols.append( pyfits.Column(name='coadd_srclist', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='coadd_wt_hdu', format='A1', array=['x'])) cols.append( pyfits.Column(name='coaddcat_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='coaddseg_file', format='A256', array=['generated_by_galsim'])) cols.append( pyfits.Column(name='cutout_file', format='A256', array=['generated_by_galsim'])) cols.append(pyfits.Column(name='max_boxsize', format='A3', array=['x'])) cols.append(pyfits.Column(name='medsconf', format='A3', array=['x'])) cols.append(pyfits.Column(name='min_boxsize', format='A2', array=['x'])) cols.append(pyfits.Column(name='se_badpix_hdu', format='A1', array=['x'])) cols.append(pyfits.Column(name='se_hdu', format='A1', array=['x'])) cols.append(pyfits.Column(name='se_wt_hdu', format='A1', array=['x'])) cols.append(pyfits.Column(name='seg_hdu', format='A1', array=['x'])) cols.append(pyfits.Column(name='sky_hdu', format='A1', array=['x'])) metadata = pyfits.new_table(pyfits.ColDefs(cols)) metadata.update_ext_name('metadata') # rest of HDUs are image vectors image_cutouts = pyfits.ImageHDU(vec['image'], name='image_cutouts') weight_cutouts = pyfits.ImageHDU(vec['weight'], name='weight_cutouts') seg_cutouts = pyfits.ImageHDU(vec['seg'], name='seg_cutouts') # write all hdu_list = pyfits.HDUList([ primary, object_data, image_info, metadata, image_cutouts, weight_cutouts, seg_cutouts ]) hdu_list.writeto(file_name, clobber=clobber)