def list_bricks(ns):
t0 = time()
if ns.filelist is not None:
d = dict([(parse_filename(fn.strip()), fn.strip())
for fn in open(ns.filelist, 'r').readlines()])
else:
d = dict(iter_tractor(ns.src))
if ns.verbose:
print('enumerated %d bricks in %g seconds' % (len(d), time() - t0))
if ns.bricksdesc is not None:
bricksdesc = fitsio.read(ns.bricksdesc, 1, upper=True)
# ADM convert from bytes_ to str_ type if fitsio version < 1.
if bricksdesc["BRICKNAME"].dtype.type == np.bytes_:
bricksdesc = dict([(item['BRICKNAME'].decode(), item)
for item in bricksdesc])
else:
bricksdesc = dict([(item['BRICKNAME'], item)
for item in bricksdesc])
else:
bricksdesc = None
#- Load list of bricknames to use
if ns.bricklist is not None:
bricklist = np.loadtxt(ns.bricklist, dtype='S8')
# TODO: skip unknown bricks?
d = dict([(brickname.decode(), d[brickname])
for brickname in bricklist])
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
chunksize = 1024
keys = list(d.keys())
def work(i):
return [(brickname, d[brickname],
read_region(brickname, d[brickname], bricksdesc))
for brickname in keys[i:i + chunksize]]
bricks = sum(pool.map(work, range(0, len(keys), chunksize)), [])
if ns.verbose:
print('read regions of %d bricks in %g seconds' %
(len(bricks), time() - t0))
return bricks
def make_sweep(sweep, bricks, ns):
data = [np.empty(0, dtype=SWEEP_DTYPE)]
ra1, dec1, ra2, dec2 = sweep
with sharedmem.MapReduce(np=0) as pool:
def filter(brickname, filename, region):
if not intersect(sweep, region):
return None
objects = fitsio.read(filename, 1, upper=True)
mask = objects['BRICK_PRIMARY'] != 0
objects = objects[mask]
mask = objects['RA'] >= ra1
mask &= objects['RA'] < ra2
mask &= objects['DEC'] >= dec1
mask &= objects['DEC'] < dec2
objects = objects[mask]
chunk = np.empty(len(objects), dtype=SWEEP_DTYPE)
for colname in chunk.dtype.names:
if colname not in objects.dtype.names:
# skip missing columns
continue
try:
chunk[colname][...] = objects[colname][...]
except ValueError:
print('failed on column `%s`' % colname)
raise
return chunk
def reduce(chunk):
if chunk is not None:
data.append(chunk)
pool.map(filter, bricks, star=True, reduce=reduce)
neff = len(data) - 1
data = np.concatenate(data, axis=0)
return data, neff
def make_sweep(sweep, bricks, ns):
data = [np.empty(0, dtype=SWEEP_DTYPE)]
header = {}
ra1, dec1, ra2, dec2 = sweep
def merge_header(header, header2):
for key, value in header2.items():
if key not in header:
header[key] = value
else:
if header[key] is NA:
pass
else:
if header[key] != value:
header[key] = NA
with sharedmem.MapReduce(np=0) as pool:
def filter(brickname, filename, region):
if not intersect(sweep, region):
return None, None
try:
objects = fitsio.read(filename, 1, upper=True)
chunkheader = fitsio.read_header(filename, 0, upper=True)
except:
if ns.ignore_errors:
print('IO error on %s' % filename)
return None, None
else:
raise
# ADM check all the column dtypes match.
if not ns.ignore_errors:
sflds = SWEEP_DTYPE.fields
tflds = objects.dtype.fields
for fld in sflds:
sdt, tdt = sflds[fld][0], tflds[fld][0]
if sdt != tdt:
msg = 'sweeps/Tractor dtypes differ for field '
msg += '{}. Sweeps: {}, Tractor: {}'.format(fld, sdt, tdt)
raise ValueError(msg)
mask = objects['BRICK_PRIMARY'] != 0
objects = objects[mask]
mask = objects['RA'] >= ra1
mask &= objects['RA'] < ra2
mask &= objects['DEC'] >= dec1
mask &= objects['DEC'] < dec2
objects = objects[mask]
chunk = np.empty(len(objects), dtype=SWEEP_DTYPE)
for colname in chunk.dtype.names:
if colname not in objects.dtype.names:
# skip missing columns
continue
try:
chunk[colname][...] = objects[colname][...]
except ValueError:
print('failed on column `%s`' % colname)
raise
chunkheader = dict([(key, chunkheader[key]) for key in chunkheader.keys()])
return chunk, chunkheader
def reduce(chunk, chunkheader):
if chunk is not None:
data.append(chunk)
merge_header(header, chunkheader)
pool.map(filter, bricks, star=True, reduce=reduce)
neff = len(data) - 1
data = np.concatenate(data, axis=0)
header = dict([(key, value) for key, value in header.items() if value is not NA])
return data, header, neff
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalogue files ***")
print(" *** Disable -I for final data product. ***")
# avoid each subprocess importing h5py again and again.
if 'hdf5' in ns.format:
import h5py
# this may take a while on a file system with slow meta-data
# access
# bricks = [(name, filepath, region), ...]
bricks = list_bricks(ns)
t0 = time()
try:
os.makedirs(ns.dest)
except OSError:
pass
# blocks or ra stripes?
schemas = {
'ra' : sweep_schema_ra(360),
'blocks' : sweep_schema_blocks(36, 36),
'dec' : sweep_schema_dec(180),
}
sweeps = schemas[ns.schema]
t0 = time()
nbricks_tot = np.zeros((), 'i8')
nobj_tot = np.zeros((), 'i8')
def work(sweep):
data, header, nbricks = make_sweep(sweep, bricks, ns)
header.update({
'RAMIN' : sweep[0],
'DECMIN' : sweep[1],
'RAMAX' : sweep[2],
'DECMAX' : sweep[3],
})
template = "sweep-%(ramin)s%(decmin)s-%(ramax)s%(decmax)s.%(format)s"
def formatdec(dec):
return ("%+04g" % dec).replace('-', 'm').replace('+', 'p')
def formatra(ra):
return ("%03g" % ra)
for format in ns.format:
filename = template % \
dict(ramin=formatra(sweep[0]),
decmin=formatdec(sweep[1]),
ramax=formatra(sweep[2]),
decmax=formatdec(sweep[3]),
format=format)
if len(data) > 0:
save_sweep_file(os.path.join(ns.dest, filename),
data, header, format)
return filename, nbricks, len(data)
def reduce(filename, nbricks, nobj):
nbricks_tot[...] += nbricks
nobj_tot[...] += nobj
if ns.verbose and nobj > 0:
print (
'%s : %d bricks %d primary objects, %g bricks / sec %g objs / sec' %
( filename, nbricks, nobj,
nbricks_tot / (time() - t0),
nobj_tot / (time() - t0),
)
)
with sharedmem.MapReduce(np=ns.numproc) as pool:
pool.map(work, sweeps, reduce=reduce)
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalog files ***")
print(" *** Disable -I for final data product. ***")
bricks = list_bricks(ns)
# ADM grab a {FIELD: unit} dict from the first Tractor file.
unitdict = get_units(bricks[0][1])
tree, nobj, morecols = read_external(ns.external, ns)
# get the data type of the match
brickname, path = bricks[0]
peek = fitsio.read(path, 1, upper=True)
matched_catalog = sharedmem.empty(nobj, dtype=peek.dtype)
matched_catalog['OBJID'] = -1
matched_distance = sharedmem.empty(nobj, dtype='f4')
# convert to radian
tol = ns.tolerance / (60. * 60.) * (np.pi / 180)
matched_distance[:] = tol
nprocessed = np.zeros((), dtype='i8')
nmatched = np.zeros((), dtype='i8')
ntotal = np.zeros((), dtype='i8')
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
def work(brickname, path):
try:
objects = fitsio.read(path, 1, upper=True)
except:
if ns.ignore_errors:
print ("IO Error on %s" %path)
return None, None, None
else:
raise
pos = radec2pos(objects['RA'], objects['DEC'])
d, i = tree.query(pos, 1)
assert (objects['OBJID'] != -1).all()
with pool.critical:
mask = d < matched_distance[i]
mask &= objects['BRICK_PRIMARY']
i = i[mask]
matched_catalog[i] = objects[mask][list(matched_catalog.dtype.names)]
matched_distance[i] = d[mask]
matched = mask.sum()
return brickname, matched, len(objects)
def reduce(brickname, matched, total):
if brickname is None:
return
nprocessed[...] += 1
nmatched[...] += matched
ntotal[...] += total
if ns.verbose:
if nprocessed % 1000 == 0:
print("Processed %d files, %g / second, matched %d / %d objects."
% (nprocessed, nprocessed / (time() - t0), nmatched, ntotal)
)
pool.map(work, bricks, star=True, reduce=reduce)
nrealmatched = (matched_catalog['OBJID'] != -1).sum()
if ns.verbose:
print("Processed %d files, %g / second, matched %d / %d objects into %d slots."
% (nprocessed, nprocessed / (time() - t0),
nmatched, ntotal,
nrealmatched)
)
try:
os.makedirs(os.path.dirname(ns.dest))
except OSError:
pass
hdr = fitsio.FITSHDR()
hdr.add_record(dict(name='NMATCHED', value=nrealmatched,
comment='Number of unique matches.'))
hdr.add_record(dict(name='NCOLL', value=nmatched - nrealmatched,
comment='Total number of matches.'))
hdr.add_record(dict(name='NCOLL', value=nrealmatched,
comment='Total number of matches.'))
hdr.add_record(dict(name='RADIUS', value=ns.tolerance,
comment='Search radius (arcsec).'))
value = ns.external
if len(value) > 68:
hdr.add_record(dict(name='EXTERNAL', value=value[:67]+'&'))
while len(value):
value = value[67:]
if len(value) == 0:
break
hdr.add_record(dict(name='CONTINUE', value=" '%s%s'" % (
value[:67], '&' if len(value) > 67 else '')))
added_long = True
else:
added_long = False
if added_long:
hdr.add_record(dict(name='LONGSTRN', value='OGIP 1.0',
comment='CONTINUE cards are used'))
# Optionally add the new columns
if len(morecols) > 0:
newdtype = matched_catalog.dtype.descr
for coldata, col in zip( morecols, ns.copycols ):
newdtype = newdtype + [(col, coldata.dtype)]
newdtype = np.dtype(newdtype)
_matched_catalog = np.empty(matched_catalog.shape, dtype=newdtype)
for field in matched_catalog.dtype.fields:
_matched_catalog[field] = matched_catalog[field]
for coldata, col in zip( morecols, ns.copycols ):
_matched_catalog[col] = coldata
matched_catalog = _matched_catalog.copy()
del _matched_catalog
for format in ns.format:
save_file(ns.dest, matched_catalog, hdr, format, unitdict=unitdict)
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalog files ***")
print(" *** Disable -I for final data product. ***")
bricks = list_bricks(ns)
tree, nobj, morecols = read_external(ns.external, ns)
# get the data type of the match
brickname, path = bricks[0]
peek = fitsio.read(path, 1, upper=True)
matched_catalog = sharedmem.empty(nobj, dtype=peek.dtype)
matched_catalog['OBJID'] = -1
matched_distance = sharedmem.empty(nobj, dtype='f4')
# convert to radian
tol = ns.tolerance / (60. * 60.) * (np.pi / 180)
matched_distance[:] = tol
nprocessed = np.zeros((), dtype='i8')
nmatched = np.zeros((), dtype='i8')
ntotal = np.zeros((), dtype='i8')
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
def work(brickname, path):
try:
objects = fitsio.read(path, 1, upper=True)
except:
if ns.ignore_errors:
print ("IO Error on %s" %path)
return None, None, None
else:
raise
pos = radec2pos(objects['RA'], objects['DEC'])
d, i = tree.query(pos, 1)
assert (objects['OBJID'] != -1).all()
with pool.critical:
mask = d < matched_distance[i]
mask &= objects['BRICK_PRIMARY']
i = i[mask]
matched_catalog[i] = objects[mask]
matched_distance[i] = d[mask]
matched = mask.sum()
return brickname, matched, len(objects)
def reduce(brickname, matched, total):
if brickname is None:
return
nprocessed[...] += 1
nmatched[...] += matched
ntotal[...] += total
if ns.verbose:
if nprocessed % 1000 == 0:
print("Processed %d files, %g / second, matched %d / %d objects."
% (nprocessed, nprocessed / (time() - t0), nmatched, ntotal)
)
pool.map(work, bricks, star=True, reduce=reduce)
nrealmatched = (matched_catalog['OBJID'] != -1).sum()
if ns.verbose:
print("Processed %d files, %g / second, matched %d / %d objects into %d slots."
% (nprocessed, nprocessed / (time() - t0),
nmatched, ntotal,
nrealmatched)
)
try:
os.makedirs(os.path.dirname(ns.dest))
except OSError:
pass
header = {}
header['NMATCHED'] = nrealmatched
header['NCOLLISION'] = nmatched - nrealmatched
header['TOL_ARCSEC'] = ns.tolerance
# Optionally add the new columns
if len(morecols) > 0:
newdtype = matched_catalog.dtype.descr
for coldata, col in zip( morecols, ns.copycols ):
newdtype = newdtype + [(col, coldata.dtype)]
newdtype = np.dtype(newdtype)
_matched_catalog = np.empty(matched_catalog.shape, dtype=newdtype)
for field in matched_catalog.dtype.fields:
_matched_catalog[field] = matched_catalog[field]
for coldata, col in zip( morecols, ns.copycols ):
_matched_catalog[col] = coldata
matched_catalog = _matched_catalog.copy()
del _matched_catalog
for format in ns.format:
save_file(ns.dest, matched_catalog, header, format)
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalog files ***")
print(" *** Disable -I for final data product. ***")
bricks = list_bricks(ns)
# ADM grab a {FIELD: unit} dict from the first Tractor file.
unitdict = get_units(bricks[0][1])
# convert to radian
tol = ns.tolerance / (60. * 60.) * (np.pi / 180)
tree, nobj, morecols, maxdups = read_external(ns.external, tol, ns)
# get the data type of the match
brickname, path = bricks[0]
peek = fitsio.read(path, 1, upper=True)
matched_catalog = sharedmem.empty(nobj, dtype=peek.dtype)
matched_catalog['OBJID'] = -1
matched_distance = sharedmem.empty(nobj, dtype='f4')
matched_distance[:] = tol
nprocessed = np.zeros((), dtype='i8')
nmatched = np.zeros((), dtype='i8')
ntotal = np.zeros((), dtype='i8')
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
def work(brickname, path):
try:
objects = fitsio.read(path, 1, upper=True)
except:
if ns.ignore_errors:
print ("IO Error on %s" %path)
return None, None, None
else:
raise
# ADM limit to just PRIMARY objects from imaging.
bp = objects["BRICK_PRIMARY"]
objects = objects[bp]
pos = radec2pos(objects['RA'], objects['DEC'])
# ADM query tree allowing duplicates.
dd, ii = tree.query(pos, maxdups, distance_upper_bound=tol)
# ADM collect relevant information (retaining duplicates).
_s = ii[dd < tol] # ADM the spec object indices.
_p = np.where(dd < tol)[0] # ADM the imaging object indices.
_d = dd[dd < tol] # ADM the matching distances.
# ADM bail if there are no matches.
if len(_s) == 0:
return brickname, 0, len(objects)
# ADM look-up dictionaries of the relevant distances and
# ADM imaging object indices for each spec object index.
ddict, pdict = {s: [] for s in _s}, {s: [] for s in _s}
_ = [ddict[s].append(d) for s, d in zip(_s, _d)]
_ = [pdict[s].append(p) for s, p in zip(_s, _p)]
# ADM collapse the lookup dict based on minimum distances.
sdp = [[s, d[np.argmin(d)], p[np.argmin(d)]] for s, d, p in
zip(ddict.keys(), ddict.values(), pdict.values())]
# ADM we're left with the spectroscopic and photometric indexes
# ADM distances and indexes contingent on the minimum distances.
i = np.array(sdp, dtype='i4')[:,0]
d = np.array(sdp, dtype='f4')[:,1]
iphot = np.array(sdp, dtype='i4')[:,2]
assert (objects['OBJID'] != -1).all()
with pool.critical:
mask = d < matched_distance[i]
i = i[mask]
iphot = iphot[mask]
matched_catalog[i] = objects[iphot][list(matched_catalog.dtype.names)]
matched_distance[i] = d[mask]
matched = mask.sum()
return brickname, matched, len(objects)
def reduce(brickname, matched, total):
if brickname is None:
return
nprocessed[...] += 1
nmatched[...] += matched
ntotal[...] += total
if ns.verbose:
if nprocessed % 1000 == 0:
print("Processed %d files, %g / second, matched %d / %d brick primary objects."
% (nprocessed, nprocessed / (time() - t0), nmatched, ntotal)
)
pool.map(work, bricks, star=True, reduce=reduce)
nrealmatched = (matched_catalog['OBJID'] != -1).sum()
if ns.verbose:
print("Processed %d files, %g / second, matched %d / %d objects into %d slots."
% (nprocessed, nprocessed / (time() - t0),
nmatched, ntotal,
nrealmatched)
)
try:
os.makedirs(os.path.dirname(ns.dest))
except OSError:
pass
hdr = fitsio.FITSHDR()
hdr.add_record(dict(name='NMATCHED', value=nrealmatched,
comment='Number of unique matches.'))
hdr.add_record(dict(name='NCOLL', value=nmatched - nrealmatched,
comment='Total number of matches.'))
hdr.add_record(dict(name='NCOLL', value=nrealmatched,
comment='Total number of matches.'))
hdr.add_record(dict(name='RADIUS', value=ns.tolerance,
comment='Search radius (arcsec).'))
value = ns.external
if len(value) > 68:
hdr.add_record(dict(name='EXTERNAL', value=value[:67]+'&'))
while len(value):
value = value[67:]
if len(value) == 0:
break
hdr.add_record(dict(name='CONTINUE', value=" '%s%s'" % (
value[:67], '&' if len(value) > 67 else '')))
added_long = True
else:
added_long = False
if added_long:
hdr.add_record(dict(name='LONGSTRN', value='OGIP 1.0',
comment='CONTINUE cards are used'))
# Optionally add the new columns
if len(morecols) > 0:
newdtype = matched_catalog.dtype.descr
for coldata, col in zip( morecols, ns.copycols ):
newdtype = newdtype + [(col, coldata.dtype)]
newdtype = np.dtype(newdtype)
_matched_catalog = np.empty(matched_catalog.shape, dtype=newdtype)
for field in matched_catalog.dtype.fields:
_matched_catalog[field] = matched_catalog[field]
for coldata, col in zip( morecols, ns.copycols ):
_matched_catalog[col] = coldata
matched_catalog = _matched_catalog.copy()
del _matched_catalog
for format in ns.format:
save_file(ns.dest, matched_catalog, hdr, format, unitdict=unitdict)
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalogue files ***")
print(" *** Disable -I for final data product. ***")
# avoid each subprocess importing h5py again and again.
if 'hdf5' in ns.format:
import h5py
# this may take a while on a file system with slow meta-data
# access
# bricks = [(name, filepath, region), ...]
bricks = list_bricks(ns)
# ADM get a {FIELD: unit} dictionary from one of the Tractor files.
fn = bricks[0][1]
unitdict = get_units(fn)
# ADM read in a small amount of information from one of the Tractor
# ADM files to establish the full dtype.
testdata = fitsio.read(fn, rows=[0], upper=True)
ALL_DTYPE = testdata.dtype
t0 = time()
for odn in outdirnames:
try:
os.makedirs(os.path.join(ns.dest, odn))
except OSError:
pass
# blocks or ra stripes?
schemas = {
'ra': sweep_schema_ra(360),
'blocks': sweep_schema_blocks(36, 36),
'dec': sweep_schema_dec(180),
}
sweeps = schemas[ns.schema]
t0 = time()
nbricks_tot = np.zeros((), 'i8')
nobj_tot = np.zeros((), 'i8')
def work(sweep):
data, header, nbricks = make_sweep(sweep,
bricks,
ns,
ALL_DTYPE=ALL_DTYPE)
header.update({
'RAMIN': sweep[0],
'DECMIN': sweep[1],
'RAMAX': sweep[2],
'DECMAX': sweep[3],
})
template = "sweep-%(ramin)s%(decmin)s-%(ramax)s%(decmax)s.%(format)s"
def formatdec(dec):
return ("%+04g" % dec).replace('-', 'm').replace('+', 'p')
def formatra(ra):
return ("%03g" % ra)
for format in ns.format:
filename = template % \
dict(ramin=formatra(sweep[0]),
decmin=formatdec(sweep[1]),
ramax=formatra(sweep[2]),
decmax=formatdec(sweep[3]),
format=format)
if len(data) > 0:
# ADM the columns to always include to form a unique ID.
uniqid = [
dt for dt in SWEEP_DTYPE.descr if dt[0] == "RELEASE"
or dt[0] == "BRICKID" or dt[0] == "OBJID"
]
# ADM write out separate sweeps for:
# ADM the SWEEP_DTYPE columns (without light-curves).
sweepdt = [dt for dt in SWEEP_DTYPE.descr if 'LC' not in dt[0]]
# ADM the SWEEP_DTYPE columns (just light-curves).
lcdt = uniqid + [
dt for dt in SWEEP_DTYPE.descr if 'LC' in dt[0]
]
# ADM the remaining "extra" columns.
alldt = uniqid + [
dt
for dt in ALL_DTYPE.descr if dt[0] not in SWEEP_DTYPE.names
]
ender = [".fits", "-lc.fits", "-ex.fits"]
for dt, odn, end in zip([sweepdt, lcdt, alldt], outdirnames,
ender):
fn = filename.replace(".fits", end)
dest = os.path.join(ns.dest, odn, fn)
if len(dt) > 0:
newdata = np.empty(len(data), dtype=dt)
for col in newdata.dtype.names:
newdata[col] = data[col]
save_sweep_file(dest,
newdata,
header,
format,
unitdict=unitdict)
return filename, nbricks, len(data)
def reduce(filename, nbricks, nobj):
nbricks_tot[...] += nbricks
nobj_tot[...] += nobj
if ns.verbose and nobj > 0:
print(
'%s : %d bricks %d primary objects, %g bricks / sec %g objs / sec'
% (
filename,
nbricks,
nobj,
nbricks_tot / (time() - t0),
nobj_tot / (time() - t0),
))
with sharedmem.MapReduce(np=ns.numproc) as pool:
pool.map(work, sweeps, reduce=reduce)
def main():
ns = parse_args()
bricks = list_bricks(ns)
tree, boss = read_boss(ns.boss, ns)
# get the data type of the match
brickname, path = bricks[0]
peek = fitsio.read(path, 1, upper=True)
matched_catalogue = sharedmem.empty(len(boss), dtype=peek.dtype)
matched_catalogue['OBJID'] = -1
matched_distance = sharedmem.empty(len(boss), dtype='f4')
# convert to radian
tol = ns.tolerance / (60. * 60.) * (np.pi / 180)
matched_distance[:] = tol
nprocessed = np.zeros((), dtype='i8')
nmatched = np.zeros((), dtype='i8')
ntotal = np.zeros((), dtype='i8')
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
def work(brickname, path):
objects = fitsio.read(path, 1, upper=True)
pos = radec2pos(objects['RA'], objects['DEC'])
d, i = tree.query(pos, 1)
assert (objects['OBJID'] != -1).all()
with pool.critical:
mask = d < matched_distance[i]
mask &= objects['BRICK_PRIMARY']
i = i[mask]
matched_catalogue[i] = objects[mask]
matched_distance[i] = d[mask]
matched = mask.sum()
return brickname, matched, len(objects)
def reduce(brickname, matched, total):
nprocessed[...] += 1
nmatched[...] += matched
ntotal[...] += total
if ns.verbose:
if nprocessed % 50 == 0:
print("Processed %d files, %g / second, matched %d / %d objects."
% (nprocessed, nprocessed / (time() - t0), nmatched, ntotal)
)
pool.map(work, bricks, star=True, reduce=reduce)
nrealmatched = (matched_catalogue['OBJID'] != -1).sum()
if ns.verbose:
print("Processed %d files, %g / second, matched %d / %d objects into %d slots."
% (nprocessed, nprocessed / (time() - t0),
nmatched, ntotal,
nrealmatched)
)
try:
os.makedirs(os.path.dirname(ns.dest))
except OSError:
pass
header = {}
header['NMATCHED'] = nrealmatched
header['NCOLLISION'] = nmatched - nrealmatched
header['TOL_ARCSEC'] = ns.tolerance
for format in ns.format:
save_file(ns.dest, matched_catalogue, header, format)
