tabdata = Table.read('TaipanCatalogues/wholehemisphere/'
'Taipan.2MASS_selected.fits')
print 'Generating targets...'
all_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2],
priority=random.randint(1,8)) for r in tabdata ]
# if r[1] > 40 and r[1] < 53 and r[2] > -34 and r[2] < -26]
no_targets = len(all_targets)
end = datetime.datetime.now()
delta = end - start
print 'Imported & generated %d targets in %d:%02.1f' % (
no_targets, delta.total_seconds()/60,
delta.total_seconds() % 60.)
print 'Computing target difficulties...'
start = datetime.datetime.now()
tp.compute_target_difficulties(all_targets, verbose=True,
leafsize=100000)
end = datetime.datetime.now()
delta = end - start
print 'Time to compute %d difficulties: %d:%02.1f' % (
no_targets, delta.total_seconds()/60,
delta.total_seconds() % 60.)
sys.exit()
# Test single-distance computations
# print 'Computing tile candidates via comprehension...'
# start = datetime.datetime.now()
# candidates = [t for t in all_targets if t.dist_point((40., -32.))
# < tp.TILE_RADIUS]
# end = datetime.datetime.now()
# delta = end - start
# print 'Time to compute candidates via comprehension: %d:%02.1f' % (
# if r[1] > 30 and r[1] < 50 and r[2] > -34 and r[2] < -26]
end = datetime.datetime.now()
delta = end - start
print(
'Imported & generated %d targets, %d guides and %d standards'
' in %d:%02.1f') % (len(all_targets), len(guide_targets),
len(standard_targets), delta.total_seconds() / 60,
delta.total_seconds() % 60.)
print 'Calculating target US positions...'
burn = [t.compute_usposn() for t in all_targets]
print 'Computing target difficulties...'
start = datetime.datetime.now()
no_targets = len(all_targets)
tp.compute_target_difficulties(all_targets)
end = datetime.datetime.now()
delta = end - start
print 'Computed %d target US posns. and difficulties in %d:%02.1f' % (
len(all_targets), delta.total_seconds() / 60, delta.total_seconds() % 60.)
# tp.compute_target_difficulties(all_targets, ncpu=4)
# sys.exit()
# sys.exit()
# Ensure the objects are re type-cast as new instances of TaipanTarget
for t in all_targets:
t.__class__ = tp.TaipanTarget
for t in guide_targets:
t.__class__ = tp.TaipanTarget
all_targets = [
tp.TaipanTarget(str(r['mainid']),
r['raj2000'],
r['dej2000'],
priority=1,
mag=r['imag']) for r in tabdata
]
# if r[1] > 20 and r[1] < 43 and r[2] > -34 and r[2] < -26]
no_targets = len(all_targets)
print "Targets: %d" % no_targets
start = datetime.datetime.now()
# KDTree calculation
print 'Computing target difficulties...'
start = datetime.datetime.now()
tp.compute_target_difficulties(all_targets, verbose=True)
end = datetime.datetime.now()
delta = end - start
print 'Mixed time: %d:%2.1f' % (delta.total_seconds() / 60,
delta.total_seconds() % 60)
# del all_targets
# sys.exit()
# Ensure the objects are re type-cast as new instances of TaipanTarget
for t in all_targets:
t.__class__ = tp.TaipanTarget
# Make a copy of all_targets list for use in assigning fibres
candidate_targets = all_targets[:]
random.shuffle(candidate_targets)
standdata = Table.read('TaipanCatalogues/southernstrip/'
'SCOSxAllWISE.photometry.KiDS.standards.fits')
print 'Generating targets...'
all_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2],
priority=random.randint(1,8)) for r in tabdata #]
if r[1] > 30 and r[1] < 50 and r[2] > -34 and r[2] < -26]
guide_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2],
priority=random.randint(1,8), guide=True) for r in guidedata #]
if r[1] > 30 and r[1] < 50 and r[2] > -34 and r[2] < -26]
standard_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2],
priority=random.randint(1,8), standard=True) for r in
standdata #]
if r[1] > 30 and r[1] < 50 and r[2] > -34 and r[2] < -26]
print 'Computing target difficulties...'
no_targets = len(all_targets)
tp.compute_target_difficulties(all_targets)
# tp.compute_target_difficulties(all_targets, ncpu=4)
# sys.exit()
# sys.exit()
# Ensure the objects are re type-cast as new instances of TaipanTarget
for t in all_targets:
t.__class__ = tp.TaipanTarget
for t in guide_targets:
t.__class__ = tp.TaipanTarget
for t in standard_targets:
t.__class__ = tp.TaipanTarget
# if r[1] > 40 and r[1] < 53 and r[2] > -34 and r[2] < -26] print 'Computing UC position for all targets...' for target in all_targets: target.compute_ucposn() no_targets = len(all_targets) end = datetime.datetime.now() delta = end - start print 'Imported & generated %d targets in %d:%02.1f' % ( no_targets, delta.total_seconds()/60, delta.total_seconds() % 60.) # sys.exit() print 'Computing target difficulties...' start = datetime.datetime.now() tp.compute_target_difficulties(all_targets, verbose=True) end = datetime.datetime.now() delta = end - start print 'Time to compute %d difficulties: %d:%02.1f' % ( no_targets, delta.total_seconds()/60, delta.total_seconds() % 60.) sys.exit() # Test single-distance computations # print 'Computing tile candidates via comprehension...' # start = datetime.datetime.now() # candidates = [t for t in all_targets if t.dist_point((40., -32.)) # < tp.TILE_RADIUS] # end = datetime.datetime.now() # delta = end - start # print 'Time to compute candidates via comprehension: %d:%02.1f' % (
all_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2], priority=random.randint(1,8)) for r in tabdata if r[1] > 40 and r[1] < 50 and r[2] > -34 and r[2] < -26] guide_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2], priority=random.randint(1,8), guide=True) for r in guidedata if r[1] > 40 and r[1] < 50 and r[2] > -34 and r[2] < -26] standard_targets = [tp.TaipanTarget(str(r[0]), r[1], r[2], priority=random.randint(1,8), standard=True) for r in standdata if r[1] > 40 and r[1] < 50 and r[2] > -34 and r[2] < -26] print 'Computing target difficulties...' no_targets = len(all_targets) # for i in range(no_targets): # all_targets[i].compute_difficulty(all_targets) # if i % 100 == 99: # print 'Completed %d / %d' % (i+1, no_targets, ) tp.compute_target_difficulties(all_targets, ncpu=4) # sys.exit() # Ensure the objects are re type-cast as new instances of TaipanTarget for t in all_targets: t.__class__ = tp.TaipanTarget for t in guide_targets: t.__class__ = tp.TaipanTarget for t in standard_targets: t.__class__ = tp.TaipanTarget # Make a copy of all_targets list for use in assigning fibres candidate_targets = all_targets[:] random.shuffle(candidate_targets)
