def test_run_colormem(self):
"""
Run tests of redmapper.RunColormem
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
configfile = 'testconfig.yaml'
config = Configuration(os.path.join(file_path, configfile))
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.outpath = self.test_dir
# First, we need the red galaxy model
config.specfile_train = os.path.join(file_path, 'test_dr8_spec.fit')
config.zrange = [0.1, 0.2]
config.redgalfile = config.redmapper_filename('test_redgals')
config.redgalmodelfile = config.redmapper_filename('test_redgalmodel')
selred = SelectSpecRedGalaxies(config)
selred.run()
# Main test...
config.zmemfile = config.redmapper_filename('test_zmem')
rcm = RunColormem(config)
rcm.run()
rcm.output_training()
# Check that the files are there...
self.assertTrue(os.path.isfile(config.zmemfile))
mem = fitsio.read(config.zmemfile, ext=1)
testing.assert_equal(mem.size, 16)
testing.assert_array_almost_equal(
mem['pcol'][0:3], np.array([0.94829756, 0.83803916, 0.88315928]))
testing.assert_array_almost_equal(
mem['z'][0:3], np.array([0.191279, 0.188257, 0.186945]))
def test_run_colormem(self):
"""
Run tests of redmapper.RunColormem
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
configfile = 'testconfig.yaml'
config = Configuration(os.path.join(file_path, configfile))
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.outpath = self.test_dir
# First, we need the red galaxy model
config.specfile_train = os.path.join(file_path, 'test_dr8_spec.fit')
config.zrange = [0.1,0.2]
config.redgalfile = config.redmapper_filename('test_redgals')
config.redgalmodelfile = config.redmapper_filename('test_redgalmodel')
selred = SelectSpecRedGalaxies(config)
selred.run()
# Main test...
config.zmemfile = config.redmapper_filename('test_zmem')
rcm = RunColormem(config)
rcm.run()
rcm.output_training()
# Check that the files are there...
self.assertTrue(os.path.isfile(config.zmemfile))
mem = fitsio.read(config.zmemfile, ext=1)
testing.assert_equal(mem.size, 16)
testing.assert_array_almost_equal(mem['pcol'][0:3], np.array([0.94829756, 0.83803916, 0.88315928]))
testing.assert_array_almost_equal(mem['z'][0:3], np.array([0.191279, 0.188257, 0.186945]))
def test_redmagic_calibrate(self):
"""
"""
np.random.seed(12345)
file_path = 'data_for_tests'
conf_filename = 'testconfig_redmagic.yaml'
config = Configuration(os.path.join(file_path, conf_filename))
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.outpath = self.test_dir
testgals = GalaxyCatalog.from_fits_file(
os.path.join('data_for_tests', 'redmagic_test',
'redmagic_test_input_gals.fit'))
testgals.add_fields([('mag', 'f4', 5), ('mag_err', 'f4', 5)])
redmagic_cal = RedmagicCalibrator(config)
# We have to have do_run=False here because we don't have a real
# galaxy training set with associated zreds!
redmagic_cal.run(gals=testgals, do_run=False)
# Read in the calibrated parameters
self.assertTrue(os.path.isfile(config.redmagicfile))
cal = fitsio.read(config.redmagicfile, ext=1)
# Check that they are what we think they should be
# (these checks are arbitrary, just to make sure nothing has changed)
testing.assert_almost_equal(
cal['cmax'][0, :], np.array([1.14588386, 3.89420298, -0.36792211]))
testing.assert_almost_equal(
cal['bias'][0, :], np.array([-0.09999912, -0.04537928,
0.01599778]))
testing.assert_almost_equal(
cal['eratio'][0, :], np.array([1.4999998, 1.48021495, 0.50000003]))
pngs = glob.glob(os.path.join(self.test_dir, '*.png'))
self.assertEqual(len(pngs), 3)
# This is a hack of the volume limit mask to change from the one used for
# calibration to the one used for the run (which uses a different footprint
# because of reasons)
config_regular = Configuration(
os.path.join(file_path, 'testconfig.yaml'))
maskfile = config_regular.maskfile
config = Configuration(redmagic_cal.runfile)
cal, hdr = fitsio.read(config.redmagicfile, ext=1, header=True)
config.maskfile = maskfile
os.remove(cal['vmaskfile'][0].decode().rstrip())
mask = VolumeLimitMask(config, cal['etamin'], use_geometry=True)
# Now test the running, using the output file which has valid galaxies/zreds
run_redmagic = RunRedmagicTask(redmagic_cal.runfile)
run_redmagic.run()
# check that we have a redmagic catalog
rmcatfile = config.redmapper_filename('redmagic_%s' % ('highdens'))
self.assertTrue(os.path.isfile(rmcatfile))
# And a random catalog
rmrandfile = config.redmapper_filename('redmagic_%s_randoms' %
('highdens'))
self.assertTrue(
os.path.isfile(
config.redmapper_filename('redmagic_%s_randoms' %
('highdens'))))
# And check that the plot is there
pngs = glob.glob(os.path.join(self.test_dir, '*.png'))
self.assertEqual(len(pngs), 4)
# And that we have the desired number of redmagic and randoms
red_cat = GalaxyCatalog.from_fits_file(rmcatfile)
rand_cat = GalaxyCatalog.from_fits_file(rmrandfile)
self.assertEqual(rand_cat.size, red_cat.size * 10)
# And confirm that all the randoms are in the footprint
zmax = mask.calc_zmax(rand_cat.ra, rand_cat.dec)
self.assertTrue(np.all(rand_cat.z < zmax))
def test_redmagic_calibrate(self):
"""
"""
np.random.seed(12345)
file_path = 'data_for_tests'
conf_filename = 'testconfig_redmagic.yaml'
config = Configuration(os.path.join(file_path, conf_filename))
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.outpath = self.test_dir
testgals = GalaxyCatalog.from_fits_file(os.path.join('data_for_tests', 'redmagic_test', 'redmagic_test_input_gals.fit'))
testgals.add_fields([('mag', 'f4', 5), ('mag_err', 'f4', 5)])
redmagic_cal = RedmagicCalibrator(config)
# We have to have do_run=False here because we don't have a real
# galaxy training set with associated zreds!
redmagic_cal.run(gals=testgals, do_run=False)
# Read in the calibrated parameters
self.assertTrue(os.path.isfile(config.redmagicfile))
cal = fitsio.read(config.redmagicfile, ext=1)
# Check that they are what we think they should be
# (these checks are arbitrary, just to make sure nothing has changed)
testing.assert_almost_equal(cal['cmax'][0, :], np.array([1.14588386, 3.89420298, -0.36792211]))
testing.assert_almost_equal(cal['bias'][0, :], np.array([-0.09999912, -0.04537928, 0.01599778]))
testing.assert_almost_equal(cal['eratio'][0, :], np.array([1.4999998, 1.48021495, 0.50000003]))
pngs = glob.glob(os.path.join(self.test_dir, '*.png'))
self.assertEqual(len(pngs), 3)
# This is a hack of the volume limit mask to change from the one used for
# calibration to the one used for the run (which uses a different footprint
# because of reasons)
config_regular = Configuration(os.path.join(file_path, 'testconfig.yaml'))
maskfile = config_regular.maskfile
config = Configuration(redmagic_cal.runfile)
cal, hdr = fitsio.read(config.redmagicfile, ext=1, header=True)
config.maskfile = maskfile
os.remove(cal['vmaskfile'][0].decode().rstrip())
mask = VolumeLimitMask(config, cal['etamin'], use_geometry=True)
# Now test the running, using the output file which has valid galaxies/zreds
run_redmagic = RunRedmagicTask(redmagic_cal.runfile)
run_redmagic.run()
# check that we have a redmagic catalog
rmcatfile = config.redmapper_filename('redmagic_%s' % ('highdens'))
self.assertTrue(os.path.isfile(rmcatfile))
# And a random catalog
rmrandfile = config.redmapper_filename('redmagic_%s_randoms' % ('highdens'))
self.assertTrue(os.path.isfile(config.redmapper_filename('redmagic_%s_randoms' % ('highdens'))))
# And check that the plot is there
pngs = glob.glob(os.path.join(self.test_dir, '*.png'))
self.assertEqual(len(pngs), 4)
# And that we have the desired number of redmagic and randoms
red_cat = GalaxyCatalog.from_fits_file(rmcatfile)
rand_cat = GalaxyCatalog.from_fits_file(rmrandfile)
self.assertEqual(rand_cat.size, red_cat.size * 10)
# And confirm that all the randoms are in the footprint
zmax = mask.calc_zmax(rand_cat.ra, rand_cat.dec)
self.assertTrue(np.all(rand_cat.z < zmax))
def runTest(self):
"""
Test redmapper.pipeline.RedmapperConsolidateTask
"""
file_path = "data_for_tests"
conf_filename = "testconfig.yaml"
config = Configuration(file_path + "/" + conf_filename)
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.outpath = self.test_dir
config.consolidate_lambda_cuts = np.array([5.0, 20.0])
config.consolidate_vlim_lstars = np.array([])
# Make 3 fake simple catalogs...
random.seed(seed=12345)
nside = 4
test_arr = np.zeros(5, dtype=[('mem_match_id', 'i4'),
('ra', 'f8'),
('dec', 'f8'),
('lambda', 'f4'),
('lnlamlike', 'f4')])
# Do cat0, pixel 0
theta, phi = hp.pix2ang(nside, 0)
test_arr['mem_match_id'] = np.arange(test_arr.size) + 1
test_arr['lambda'][:] = 100.0
test_arr['lnlamlike'] = random.random(size=test_arr.size) * 100
test_arr['ra'][:] = np.degrees(phi)
test_arr['dec'][:] = 90.0 - np.degrees(theta)
cat0 = ClusterCatalog(test_arr)
cat0.to_fits_file(config.redmapper_filename('cat_4_00000_final'))
cat0.to_fits_file(config.redmapper_filename('cat_4_00000_final_members'))
# Do cat1, pixel 1
theta, phi = hp.pix2ang(nside, 1)
test_arr['mem_match_id'] = np.arange(test_arr.size) + 1
test_arr['lambda'][:] = 100.0
test_arr['lnlamlike'] = random.random(size=test_arr.size) * 100
test_arr['ra'][:] = np.degrees(phi)
test_arr['dec'][:] = 90.0 - np.degrees(theta)
cat1 = ClusterCatalog(test_arr)
cat1.to_fits_file(config.redmapper_filename('cat_4_00001_final'))
cat1.to_fits_file(config.redmapper_filename('cat_4_00001_final_members'))
# Do cat2, pixel 2
theta, phi = hp.pix2ang(nside, 2)
test_arr['mem_match_id'] = np.arange(test_arr.size) + 1
test_arr['lambda'][:] = 100.0
test_arr['lnlamlike'] = random.random(size=test_arr.size) * 100
test_arr['ra'][:] = np.degrees(phi)
test_arr['dec'][:] = 90.0 - np.degrees(theta)
cat2 = ClusterCatalog(test_arr)
cat2.to_fits_file(config.redmapper_filename('cat_4_00002_final'))
cat2.to_fits_file(config.redmapper_filename('cat_4_00002_final_members'))
# need to write config out in test directory...
config_file = config.redmapper_filename('testconfig', filetype='yaml')
config.output_yaml(config_file)
# Consolidate them together...
consol = RedmapperConsolidateTask(config_file)
consol.run(match_spec=False, do_plots=False)
# Check that the ordering is correct, etc.
catfile = config.redmapper_filename('redmapper_v%s_lgt20_catalog' % (config.version))
memfile = config.redmapper_filename('redmapper_v%s_lgt20_catalog_members' % (config.version))
self.assertTrue(os.path.isfile(catfile))
self.assertTrue(os.path.isfile(memfile))
cat = fitsio.read(catfile, ext=1)
self.assertEqual(cat.size, cat0.size + cat1.size + cat2.size)
# Sort by mem_match_id, these should be reverse sorted in lnlamlike
st = np.argsort(cat['mem_match_id'])
self.assertTrue(np.all(np.diff(cat['lnlamlike'][st[::-1]]) >= 0))
# And check that the ra/dec/lnlamlike match...
test0 = {}
for cluster in cat0:
test0[cluster.lnlamlike] = cluster.ra
ctr = 0
for i in range(cat.size):
if cat['lnlamlike'][i] in test0:
if cat['ra'][i] == test0[cat['lnlamlike'][i]]:
ctr += 1
self.assertEqual(ctr, cat0.size)