def trim_throughput(indir, outdir):
'''downsample throughput files'''
assert os.path.basename(indir) == 'throughput'
if not os.path.exists(outdir):
os.makedirs(outdir)
for targettype in ('elg', 'lrg', 'perfect', 'qso', 'sky', 'star'):
filename = 'fiberloss-{}.dat'.format(targettype)
shutil.copy(os.path.join(indir, filename),
os.path.join(outdir, filename))
for filename in ['thru-b.fits', 'thru-r.fits', 'thru-z.fits']:
fx = fits.open(indir + '/' + filename)
hdus = HDUList()
hdus.append(fx[0])
hdus.append(BinTableHDU(fx[1].data[::20], header=fx[1].header))
hdus.append(BinTableHDU(fx[2].data[::20], header=fx[2].header))
hdus.writeto(outdir + '/' + filename)
fx.close()
for filename in [
'DESI-0347_blur.ecsv', 'DESI-0347_offset.ecsv',
'DESI-0347_random_offset_1.fits'
]:
shutil.copy(os.path.join(indir, filename),
os.path.join(outdir, filename))
def test_extract(tmpdir):
hdulist = HDUList()
image = ImageHDU(np.random.random((25, 25)))
hdulist.append(image)
tree = {
'some_words': 'These are some words',
'nested': {
'a': 100,
'b': 42
},
'list': [x for x in range(10)],
'image': image.data
}
asdf_in_fits = str(tmpdir.join('asdf.fits'))
with AsdfInFits(hdulist, tree) as aif:
aif.write_to(asdf_in_fits)
pure_asdf = str(tmpdir.join('extract.asdf'))
extract.extract_file(asdf_in_fits, pure_asdf)
assert os.path.exists(pure_asdf)
with asdf.open(pure_asdf) as af:
assert not isinstance(af, AsdfInFits)
assert_tree_match(tree, af.tree)
def to_hdu_list(self):
"""Convert to `~astropy.io.fits.HDUList`.
"""
hdu_list = HDUList()
for image in self:
hdu = image.to_image_hdu()
hdu_list.append(hdu)
return hdu_list
def to_hdu_list(self):
"""Convert to `~astropy.io.fits.HDUList`.
"""
hdu_list = HDUList()
for image in self:
hdu = image.to_image_hdu()
hdu_list.append(hdu)
return hdu_list
def trim_quickpsf(indir, outdir, filename):
assert os.path.abspath(indir) != os.path.abspath(outdir)
infile = os.path.join(indir, filename)
outfile = os.path.join(outdir, filename)
fx = fits.open(infile)
hdus = HDUList()
hdus.append(fx[0])
for i in [1,2,3]:
d = fx[i].data
hdus.append(BinTableHDU(d[::10], header=fx[i].header))
hdus.writeto(outfile, clobber=True)
fx.close()
def createFromScratch(self, phu, extensions=None):
"""
Creates an AstroData object from a collection of objects.
"""
lst = HDUList()
if phu is not None:
if isinstance(phu, PrimaryHDU):
lst.append(phu)
elif isinstance(phu, Header):
lst.append(PrimaryHDU(header=deepcopy(phu), data=DELAYED))
elif isinstance(phu, (dict, list, tuple)):
p = PrimaryHDU()
p.header.update(phu)
lst.append(p)
else:
raise ValueError(
"phu must be a PrimaryHDU or a valid header object")
# TODO: Verify the contents of extensions...
if extensions is not None:
for ext in extensions:
lst.append(ext)
return self.getAstroData(lst)
def get_stamps(self, oid, candid=None):
"""Download Stamps for an specific alert.
Parameters
----------
oid : :py:class:`str`
object ID in ALeRCE DBs.
candid : :py:class:`int`
Candid of the stamp to be displayed.
Returns
-------
:class:`astropy.io.fits.HDUList`
Science, Template and Difference stamps for an specific alert.
"""
if candid is None:
candid = self._get_first_detection(oid)
try:
hdulist = HDUList()
for stamp_type in ["science", "template", "difference"]:
tmp_hdulist = fits_open(
"%s?oid=%s&candid=%s&type=%s&format=fits"
% (
self.config["AVRO_URL"]
+ self.config["AVRO_ROUTES"]["get_stamp"],
oid,
candid,
stamp_type,
)
)
hdu = tmp_hdulist[0]
hdu.header["STAMP_TYPE"] = stamp_type
hdulist.append(hdu)
return hdulist
except HTTPError:
warnings.warn("AVRO File not found.", RuntimeWarning)
return None
def createFromScratch(self, phu, extensions=None):
"""
Creates an AstroData object from a collection of objects.
"""
lst = HDUList()
if phu is not None:
if isinstance(phu, PrimaryHDU):
lst.append(phu)
elif isinstance(phu, Header):
lst.append(PrimaryHDU(header=deepcopy(phu), data=DELAYED))
elif isinstance(phu, (dict, list, tuple)):
p = PrimaryHDU()
p.header.update(phu)
lst.append(p)
else:
raise ValueError("phu must be a PrimaryHDU or a valid header object")
# TODO: Verify the contents of extensions...
if extensions is not None:
for ext in extensions:
lst.append(ext)
return self.getAstroData(lst)
def trim_psf(indir, outdir, filename):
assert os.path.abspath(indir) != os.path.abspath(outdir)
infile = os.path.join(indir, filename)
outfile = os.path.join(outdir, filename)
fx = fits.open(infile)
hdus = HDUList()
#- HDU 0 XCOEFF - data unchanged but update keywords for less samples
xcoeff = fx[0].data
hdr = fx[0].header
hdr['NWAVE'] = 3 #- down from 11
hdr['CRPIX1'] = 23 #- 23=45//2+1, down from 113=225//2+1
hdr['CRPIX1'] = 23
hdr['CDELT1'] = 0.005 #- 5mm instead of 1mm
hdr['CDELT2'] = 0.005 #- 5mm instead of 1mm
hdr['PIXSIZE'] = 0.005 #- 5mm instead of 1mm
hdus.append(PrimaryHDU(xcoeff, header=hdr))
hdus.append(fx['YCOEFF'])
#- subsample spots
inspots = fx['SPOTS'].data
spots = np.zeros((3,3,45,45))
spots[0,0] = rebin_image(inspots[0,0], 5)
spots[1,0] = rebin_image(inspots[5,0], 5)
spots[2,0] = rebin_image(inspots[10,0], 5)
spots[0,1] = rebin_image(inspots[0,5], 5)
spots[1,1] = rebin_image(inspots[5,5], 5)
spots[2,1] = rebin_image(inspots[10,5], 5)
spots[0,2] = rebin_image(inspots[0,10], 5)
spots[1,2] = rebin_image(inspots[5,10], 5)
spots[2,2] = rebin_image(inspots[10,10], 5)
hdus.append(ImageHDU(spots, header=fx['SPOTS'].header))
#- subsample spots x,y locations
dx = fx['SPOTX'].data
hdus.append(ImageHDU(dx[::5, ::5], header=fx['SPOTX'].header))
dy = fx['SPOTY'].data
hdus.append(ImageHDU(dy[::5, ::5], header=fx['SPOTY'].header))
#- Fiberpos unchanged
hdus.append(fx['FIBERPOS'])
#- Subsample SPOTPOS and SPOTWAVE
d = fx['SPOTPOS'].data
hdus.append(ImageHDU(d[::5], header=fx['SPOTPOS'].header))
d = fx['SPOTWAVE'].data
hdus.append(ImageHDU(d[::5], header=fx['SPOTWAVE'].header))
hdus.writeto(outfile, clobber=True)
fx.close()
def ad_to_hdulist(ad):
"""Creates an HDUList from an AstroData object."""
hdul = HDUList()
hdul.append(PrimaryHDU(header=ad.phu, data=DELAYED))
for ext in ad._nddata:
meta = ext.meta
header, ver = meta['header'], meta['ver']
wcs = ext.wcs
if isinstance(wcs, gWCS):
# We don't have access to the AD tags so see if it's an image
# Catch ValueError as any sort of failure
try:
wcs_dict = adwcs.gwcs_to_fits(ext, ad.phu)
except (ValueError, NotImplementedError) as e:
LOGGER.warning(e)
else:
# Must delete keywords if image WCS has been downscaled
# from a higher number of dimensions
for i in range(1, 5):
for kw in (f'CDELT{i}', f'CRVAL{i}', f'CUNIT{i}',
f'CTYPE{i}'):
if kw in header:
del header[kw]
for j in range(1, 5):
for kw in (f'CD{i}_{j}', f'PC{i}_{j}', f'CRPIX{j}'):
if kw in header:
del header[kw]
header.update(wcs_dict)
# Use "in" here as the dict entry may be (value, comment)
if 'APPROXIMATE' not in wcs_dict.get('FITS-WCS', ''):
wcs = None # There's no need to create a WCS extension
hdul.append(new_imagehdu(ext.data, header, 'SCI'))
if ext.uncertainty is not None:
hdul.append(new_imagehdu(ext.uncertainty.array, header, 'VAR'))
if ext.mask is not None:
hdul.append(new_imagehdu(ext.mask, header, 'DQ'))
if isinstance(wcs, gWCS):
hdul.append(wcs_to_asdftablehdu(ext.wcs, extver=ver))
for name, other in meta.get('other', {}).items():
if isinstance(other, Table):
hdul.append(table_to_bintablehdu(other))
elif isinstance(other, np.ndarray):
header = meta['other_header'].get(name, meta['header'])
hdul.append(new_imagehdu(other, header, name=name))
elif isinstance(other, NDDataObject):
hdul.append(new_imagehdu(other.data, meta['header']))
else:
raise ValueError("I don't know how to write back an object "
"of type {}".format(type(other)))
if ad._tables is not None:
for name, table in sorted(ad._tables.items()):
hdul.append(table_to_bintablehdu(table, extname=name))
return hdul
def ad_to_hdulist(ad):
"""Creates an HDUList from an AstroData object."""
hdul = HDUList()
hdul.append(PrimaryHDU(header=ad.phu, data=DELAYED))
# Find the maximum EXTVER for extensions that belonged with this
# object if it was read from a FITS file
maxver = max((nd.meta['header'].get('EXTVER', 0)
for nd in ad._nddata if nd.meta.get('parent_ad') == id(ad)),
default=0)
for ext in ad._nddata:
header = ext.meta['header']
if not isinstance(header, fits.Header):
header = fits.Header(header)
if ext.meta.get('parent_ad') == id(ad):
# If the extension belonged with this object, use its
# original EXTVER
ver = header['EXTVER']
else:
# Otherwise renumber the extension
ver = header['EXTVER'] = maxver + 1
maxver += 1
wcs = ext.wcs
if isinstance(wcs, gWCS):
# We don't have access to the AD tags so see if it's an image
# Catch ValueError as any sort of failure
try:
wcs_dict = gwcs_to_fits(ext, ad.phu)
except (ValueError, NotImplementedError) as e:
LOGGER.warning(e)
else:
# Must delete keywords if image WCS has been downscaled
# from a higher number of dimensions
for i in range(1, 5):
for kw in (f'CDELT{i}', f'CRVAL{i}', f'CUNIT{i}',
f'CTYPE{i}'):
if kw in header:
del header[kw]
for j in range(1, 5):
for kw in (f'CD{i}_{j}', f'PC{i}_{j}', f'CRPIX{j}'):
if kw in header:
del header[kw]
header.update(wcs_dict)
# Use "in" here as the dict entry may be (value, comment)
if 'APPROXIMATE' not in wcs_dict.get('FITS-WCS', ''):
wcs = None # There's no need to create a WCS extension
hdul.append(new_imagehdu(ext.data, header, 'SCI'))
if ext.uncertainty is not None:
hdul.append(new_imagehdu(ext.uncertainty.array, header, 'VAR'))
if ext.mask is not None:
hdul.append(new_imagehdu(ext.mask, header, 'DQ'))
if isinstance(wcs, gWCS):
hdul.append(wcs_to_asdftablehdu(ext.wcs, extver=ver))
for name, other in ext.meta.get('other', {}).items():
if isinstance(other, Table):
hdu = table_to_bintablehdu(other, extname=name)
elif isinstance(other, np.ndarray):
hdu = new_imagehdu(other, header, name=name)
elif isinstance(other, NDDataObject):
hdu = new_imagehdu(other.data, ext.meta['header'])
else:
raise ValueError("I don't know how to write back an object "
f"of type {type(other)}")
hdu.ver = ver
hdul.append(hdu)
if ad._tables is not None:
for name, table in sorted(ad._tables.items()):
hdul.append(table_to_bintablehdu(table, extname=name))
return hdul