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)
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)