def test_zred_runpixels(self):
"""
Test redmapper.ZredRunPixels, computing zreds for all the galaxies
in a pixelized galaxy catalog.
"""
file_path = 'data_for_tests'
configfile = 'testconfig.yaml'
config = Configuration(os.path.join(file_path, configfile))
config.d.hpix = 2163
config.d.nside = 64
config.border = 0.0
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.zredfile = os.path.join(self.test_dir, 'zreds', 'testing_zreds_master_table.fit')
# FIXME: try an illegal one...
zredRunpix = ZredRunPixels(config)
zredRunpix.run()
# Check that the zred file has been built...
self.assertTrue(os.path.isfile(config.zredfile))
# Read in just the galaxies...
gals0 = GalaxyCatalog.from_galfile(config.galfile, nside=config.d.nside, hpix=config.d.hpix, border=config.border)
# And with the zreds...
gals = GalaxyCatalog.from_galfile(config.galfile, zredfile=config.zredfile, nside=config.d.nside, hpix=config.d.hpix, border=config.border)
# Confirm they're the same galaxies...
testing.assert_array_almost_equal(gals0.ra, gals.ra)
# Confirm the zreds are okay
self.assertGreater(np.min(gals.zred), 0.0)
self.assertGreater(np.min(gals.chisq), 0.0)
self.assertLess(np.max(gals.lkhd), 0.0)
zredfile = os.path.join(file_path, 'zreds_test', 'dr8_test_zreds_master_table.fit')
gals_compare = GalaxyCatalog.from_galfile(config.galfile, zredfile=zredfile,
nside=config.d.nside, hpix=config.d.hpix, border=config.border)
zredstr = RedSequenceColorPar(config.parfile)
mstar_input = zredstr.mstar(gals_compare.zred_uncorr)
mstar = zredstr.mstar(gals_compare.zred_uncorr)
ok, = np.where((gals_compare.refmag < (mstar_input - 2.5*np.log10(0.15))) |
(gals_compare.refmag < (mstar - 2.5*np.log10(0.15))))
delta_zred_uncorr = gals.zred_uncorr[ok] - gals_compare.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.98, float(use.size) / float(ok.size))
def test_zred_runcat(self):
"""
Test redmapper.ZredRunCatalog, computing zreds for all the galaxies in
a single catalog file.
"""
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
outfile = os.path.join(self.test_dir, 'test_zred_out.fits')
tab = fitsio.read(config.galfile, ext=1, lower=True)
galfile = os.path.join(os.path.dirname(config.galfile),
tab[0]['filenames'][0].decode())
zredRuncat = ZredRunCatalog(config)
zredRuncat.run(galfile, outfile)
# This exercises the reading code
gals = GalaxyCatalog.from_galfile(galfile, zredfile=outfile)
self.assertGreater(np.min(gals.zred), 0.0)
self.assertGreater(np.min(gals.chisq), 0.0)
self.assertLess(np.max(gals.lkhd), 0.0)
# And compare to the "official" run...
config.zredfile = os.path.join(file_path, 'zreds_test',
'dr8_test_zreds_master_table.fit')
ztab = fitsio.read(config.zredfile, ext=1, lower=True)
zredfile = os.path.join(os.path.dirname(config.zredfile),
ztab[0]['filenames'][0].decode())
gals_compare = GalaxyCatalog.from_galfile(galfile, zredfile=zredfile)
zredstr = RedSequenceColorPar(config.parfile)
mstar_input = zredstr.mstar(gals_compare.zred_uncorr)
mstar = zredstr.mstar(gals_compare.zred_uncorr)
ok, = np.where(
(gals_compare.refmag < (mstar_input - 2.5 * np.log10(0.15)))
| (gals_compare.refmag < (mstar - 2.5 * np.log10(0.15))))
delta_zred_uncorr = gals.zred_uncorr[ok] - gals_compare.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.98, float(use.size) / float(ok.size))
def _setup_cluster(self):
"""
Set up the cluster to run through the centering code.
"""
file_path = 'data_for_tests'
cluster = Cluster()
cluster.config = Configuration(
os.path.join(file_path, 'testconfig.yaml'))
tempcat = fitsio.read(
os.path.join(file_path, 'test_wcen_zred_data.fit'))
temp_neighbors = np.zeros(tempcat[0]['RAS'].size,
dtype=[('RA', 'f8'), ('DEC', 'f8'),
('DIST', 'f4'), ('R', 'f4'),
('P', 'f4'), ('PFREE', 'f4'),
('PMEM', 'f4'), ('MAG', 'f4', 5),
('MAG_ERR', 'f4', 5),
('REFMAG', 'f4'),
('REFMAG_ERR', 'f4'), ('CHISQ', 'f4'),
('ZRED', 'f4'), ('ZRED_E', 'f4'),
('ZRED_CHISQ', 'f4')])
temp_neighbors['RA'] = tempcat[0]['RAS']
temp_neighbors['DEC'] = tempcat[0]['DECS']
temp_neighbors['R'] = tempcat[0]['R']
temp_neighbors['P'] = tempcat[0]['PVALS']
temp_neighbors['PFREE'] = tempcat[0]['WVALS']
temp_neighbors['PMEM'] = tempcat[0]['WTVALS']
temp_neighbors['REFMAG'] = tempcat[0]['REFMAG_TOTAL']
temp_neighbors['ZRED'] = tempcat[0]['GZREDS']
temp_neighbors['ZRED_E'] = tempcat[0]['GZREDE']
temp_neighbors['ZRED_CHISQ'] = tempcat[0]['GCHISQ']
temp_neighbors['DIST'] = tempcat[0]['R'] / (
np.radians(1.) *
cluster.config.cosmo.Da(0, tempcat[0]['ZCLUSTER']))
neighbors = GalaxyCatalog(temp_neighbors)
cluster.set_neighbors(neighbors)
zred_filename = 'test_dr8_pars.fit'
cluster.zredstr = RedSequenceColorPar(os.path.join(
file_path, 'test_dr8_pars.fit'),
fine=True,
zrange=[0.25, 0.35])
cluster.bkg = Background(os.path.join(file_path, 'test_bkg.fit'))
cluster.zredbkg = ZredBackground(
os.path.join(file_path, 'test_bkg.fit'))
cluster.redshift = tempcat[0]['ZCLUSTER']
cluster.ra = tempcat[0]['RAC']
cluster.dec = tempcat[0]['DECC']
cluster.r_lambda = 1.0 * (tempcat[0]['LAMBDA'] / 100.0)**0.2
cluster.Lambda = tempcat[0]['LAMBDA']
cluster.scaleval = tempcat[0]['SCALEVAL']
return cluster
def test_zred(self):
"""
Test redmapper.ZredColor, looping over galaxies.
"""
file_path = 'data_for_tests'
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename)
galaxy_filename = 'test_dr8_gals_with_zred.fit'
galaxies = GalaxyCatalog.from_fits_file(file_path + '/' + galaxy_filename)
galaxies_input = copy.deepcopy(galaxies)
# start with the first one...
zredc = ZredColor(zredstr)
zredc.compute_zred(galaxies[0])
starttime = time.time()
zredc.compute_zreds(galaxies)
print("Ran %d galaxies in %.3f seconds" % (galaxies.size,
time.time() - starttime))
# Only compare galaxies that are brighter than 0.15L* in either old OR new
# Otherwise, we're just comparing how the codes handle "out-of-range"
# galaxies, and that does not matter
mstar_input = zredstr.mstar(galaxies_input.zred_uncorr)
mstar = zredstr.mstar(galaxies.zred_uncorr)
ok, = np.where((galaxies.refmag < (mstar_input - 2.5*np.log10(0.15))) |
(galaxies.refmag < (mstar - 2.5*np.log10(0.15))))
delta_zred_uncorr = galaxies.zred_uncorr[ok] - galaxies_input.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.9, float(use.size) / float(ok.size))
delta_zred = galaxies.zred[ok[use]] - galaxies_input.zred[ok[use]]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99, float(use2.size) / float(delta_zred.size))
delta_zred2 = galaxies.zred2[ok[use]] - galaxies_input.zred2[ok[use]]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99, float(use2.size) / float(delta_zred2.size))
delta_zred_uncorr_e = galaxies.zred_uncorr_e[ok[use]] - galaxies_input.zred_uncorr_e[ok[use]]
use2, = np.where(np.abs(delta_zred_uncorr_e) < 1e-3)
testing.assert_array_less(0.98, float(use2.size) / float(delta_zred_uncorr_e.size))
delta_zred_e = galaxies.zred_e[ok[use]] - galaxies_input.zred_e[ok[use]]
use2, = np.where(np.abs(delta_zred_e) < 1e-3)
testing.assert_array_less(0.98, float(use2.size) / float(delta_zred_e.size))
delta_zred2_e = galaxies.zred2_e[ok[use]] - galaxies_input.zred2_e[ok[use]]
use2, = np.where(np.abs(delta_zred2_e) < 1e-3)
testing.assert_array_less(0.98, float(use2.size) / float(delta_zred2_e.size))
def test_zred_runcat(self):
"""
Test redmapper.ZredRunCatalog, computing zreds for all the galaxies in
a single catalog file.
"""
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
outfile = os.path.join(self.test_dir, 'test_zred_out.fits')
tab = fitsio.read(config.galfile, ext=1, lower=True)
galfile = os.path.join(os.path.dirname(config.galfile), tab[0]['filenames'][0].decode())
zredRuncat = ZredRunCatalog(config)
zredRuncat.run(galfile, outfile)
# This exercises the reading code
gals = GalaxyCatalog.from_galfile(galfile, zredfile=outfile)
self.assertGreater(np.min(gals.zred), 0.0)
self.assertGreater(np.min(gals.chisq), 0.0)
self.assertLess(np.max(gals.lkhd), 0.0)
# And compare to the "official" run...
config.zredfile = os.path.join(file_path, 'zreds_test', 'dr8_test_zreds_master_table.fit')
ztab = fitsio.read(config.zredfile, ext=1, lower=True)
zredfile = os.path.join(os.path.dirname(config.zredfile), ztab[0]['filenames'][0].decode())
gals_compare = GalaxyCatalog.from_galfile(galfile, zredfile=zredfile)
zredstr = RedSequenceColorPar(config.parfile)
mstar_input = zredstr.mstar(gals_compare.zred_uncorr)
mstar = zredstr.mstar(gals_compare.zred_uncorr)
ok, = np.where((gals_compare.refmag < (mstar_input - 2.5*np.log10(0.15))) |
(gals_compare.refmag < (mstar - 2.5*np.log10(0.15))))
delta_zred_uncorr = gals.zred_uncorr[ok] - gals_compare.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.98, float(use.size) / float(ok.size))
def test_galaxycatalog_read(self):
"""
Run redmapper.GalaxyCatalog tests,
"""
file_path = 'data_for_tests'
galfile = 'pixelized_dr8_test/dr8_test_galaxies_master_table.fit'
gals_all = GalaxyCatalog.from_galfile(file_path + '/' + galfile)
# check that we got the expected number...
testing.assert_equal(gals_all.size, 14449)
# read in a subregion, no border
gals_sub = GalaxyCatalog.from_galfile(file_path + '/' + galfile,
hpix=2163, nside=64)
theta = (90.0 - gals_all.dec) * np.pi/180.
phi = gals_all.ra * np.pi/180.
ipring_all = hp.ang2pix(64, theta, phi)
use, = np.where(ipring_all == 2163)
testing.assert_equal(gals_sub.size, use.size)
# read in a subregion, with border
gals_sub = GalaxyCatalog.from_galfile(file_path + '/' + galfile,
hpix=9218, nside=128, border=0.1)
# this isn't really a big enough sample catalog to fully test...
testing.assert_equal(gals_sub.size, 2511)
# and test the matching...
indices, dists = gals_all.match_one(140.5, 65.0, 0.2)
testing.assert_equal(indices.size, 521)
testing.assert_array_less(dists, 0.2)
i0, i1, dists = gals_all.match_many([140.5,141.2],
[65.0, 65.2], [0.2,0.1])
testing.assert_equal(i0.size, 666)
test, = np.where(i0 == 0)
testing.assert_equal(test.size, 521)
testing.assert_array_less(dists[test], 0.2)
test, = np.where(i0 == 1)
testing.assert_equal(test.size, 666 - 521)
testing.assert_array_less(dists[test], 0.1)
def test_depthfit(self):
"""
Test depth fitting with redmapper.DepthLim
"""
file_path = "data_for_tests"
conf_filename = "testconfig.yaml"
config = Configuration(file_path + "/" + conf_filename)
gals = GalaxyCatalog.from_galfile(config.galfile)
np.random.seed(seed=12345)
# First creation of depth object
dlim = DepthLim(gals.refmag, gals.refmag_err)
# This has been inspected that it makes sense.
# Also, one should really be using a depth map
testing.assert_almost_equal(dlim.initpars['EXPTIME'], 104.782066, 0)
testing.assert_almost_equal(dlim.initpars['LIMMAG'], 20.64819717, 0)
# And take a subpixel
gals = GalaxyCatalog.from_galfile(config.galfile, hpix=8421, nside=128)
config.mask_mode = 0
mask = get_mask(config)
maskgal_index = mask.select_maskgals_sample()
mask.read_maskgals(config.maskgalfile)
dlim.calc_maskdepth(mask.maskgals, gals.refmag, gals.refmag_err)
pars, fail = calcErrorModel(gals.refmag, gals.refmag_err, calcErr=False)
testing.assert_almost_equal(pars['EXPTIME'], 63.73879623, 0)
testing.assert_almost_equal(pars['LIMMAG'], 20.68231583, 0)
# And make sure the maskgals are getting the right constant value
testing.assert_almost_equal(pars['EXPTIME'], mask.maskgals.exptime.min())
testing.assert_almost_equal(pars['EXPTIME'], mask.maskgals.exptime.max())
testing.assert_almost_equal(pars['LIMMAG'], mask.maskgals.limmag.min())
testing.assert_almost_equal(pars['LIMMAG'], mask.maskgals.limmag.max())
def runTest(self):
"""
Run the ClusterFit test.
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
conf_filename = 'testconfig.yaml'
config = Configuration(file_path + '/' + conf_filename)
gals = GalaxyCatalog.from_galfile(config.galfile)
# temporary hack...
dist = esutil.coords.sphdist(142.12752, 65.103898, gals.ra, gals.dec)
mpc_scale = np.radians(1.) * config.cosmo.Da(0, 0.227865)
r = np.clip(mpc_scale * dist, 1e-6, None)
use, = np.where(r < 0.75)
st = np.argsort(r[use])
cbkg = ColorBackground(config.bkgfile_color, usehdrarea=True)
zredstr = RedSequenceColorPar(None, config=config)
cluster = Cluster(r0=0.5,
beta=0.0,
config=config,
cbkg=cbkg,
neighbors=gals[use[st]],
zredstr=zredstr)
cluster.ra = 142.12752
cluster.dec = 65.103898
cluster.redshift = 0.227865
cluster.update_neighbors_dist()
mask = HPMask(cluster.config)
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
depthstr = DepthMap(cluster.config)
mask.set_radmask(cluster)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec,
cluster.mpc_scale)
lam = cluster.calc_richness_fit(mask,
1,
centcolor_in=1.36503,
calc_err=False)
testing.assert_almost_equal(lam, 16.174486160, decimal=5)
testing.assert_almost_equal(cluster.neighbors.pcol[0:4],
np.array([0.94243, 0., 0.06338, 0.16077]),
5)
lam = cluster.calc_richness_fit(mask, 1, calc_err=False)
testing.assert_almost_equal(lam, 16.2049961, decimal=5)
def runTest(self):
"""
Run the ClusterFit test.
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
conf_filename = 'testconfig.yaml'
config = Configuration(file_path + '/' + conf_filename)
gals = GalaxyCatalog.from_galfile(config.galfile)
# temporary hack...
dist = esutil.coords.sphdist(142.12752, 65.103898, gals.ra, gals.dec)
mpc_scale = np.radians(1.) * config.cosmo.Da(0, 0.227865)
r = np.clip(mpc_scale * dist, 1e-6, None)
use, = np.where(r < 0.75)
st = np.argsort(r[use])
cbkg = ColorBackground(config.bkgfile_color, usehdrarea=True)
zredstr = RedSequenceColorPar(None, config=config)
cluster = Cluster(r0=0.5, beta=0.0, config=config, cbkg=cbkg, neighbors=gals[use[st]], zredstr=zredstr)
cluster.ra = 142.12752
cluster.dec = 65.103898
cluster.redshift = 0.227865
cluster.update_neighbors_dist()
mask = HPMask(cluster.config)
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
depthstr = DepthMap(cluster.config)
mask.set_radmask(cluster)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec, cluster.mpc_scale)
lam = cluster.calc_richness_fit(mask, 1, centcolor_in=1.36503, calc_err=False)
testing.assert_almost_equal(lam, 16.174486160, decimal=5)
testing.assert_almost_equal(cluster.neighbors.pcol[0:4], np.array([0.94243, 0., 0.06338, 0.16077]), 5)
lam = cluster.calc_richness_fit(mask, 1, calc_err=False)
testing.assert_almost_equal(lam, 16.2049961, decimal=5)
def runTest(self):
file_path = 'data_for_tests'
#The configuration
#Used by the mask, background and richness calculation
conf_filename = 'testconfig.yaml'
config = Configuration(file_path + '/' + conf_filename)
filename = 'test_cluster_members.fit'
neighbors = GalaxyCatalog.from_fits_file(file_path + '/' + filename)
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename,
fine=True)
bkg_filename = 'test_bkg.fit'
bkg = Background('%s/%s' % (file_path, bkg_filename))
cluster = Cluster(config=config,
zredstr=zredstr,
bkg=bkg,
neighbors=neighbors)
hdr = fitsio.read_header(file_path + '/' + filename, ext=1)
#cluster.z = hdr['Z']
#cluster.update_z(hdr['Z'])
cluster.redshift = hdr['Z']
richness_compare = hdr['LAMBDA']
richness_compare_err = hdr['LAMBDA_E']
cluster.ra = hdr['RA']
cluster.dec = hdr['DEC']
#Set up the mask
mask = HPMask(cluster.config) #Create the mask
mask.set_radmask(cluster)
#depthstr
depthstr = DepthMap(cluster.config)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec,
cluster.mpc_scale)
cluster.neighbors.dist = np.degrees(
cluster.neighbors.r / cluster.cosmo.Dl(0, cluster.redshift))
#set seed
seed = 0
random.seed(seed=seed)
# make a zlambda object
zlam = Zlambda(cluster)
z_lambda, z_lambda_e = zlam.calc_zlambda(cluster.redshift,
mask,
calc_err=True,
calcpz=True)
testing.assert_almost_equal(cluster.z_lambda, 0.22700983)
testing.assert_almost_equal(cluster.z_lambda_err, 0.00448909596)
# zlambda_err test
z_lambda_err = zlam._zlambda_calc_gaussian_err(cluster.z_lambda)
testing.assert_almost_equal(z_lambda_err, 0.006303830)
# and test the correction on its own
corr_filename = 'test_dr8_zlambdacorr.fit'
zlambda_corr = ZlambdaCorrectionPar(file_path + '/' + corr_filename,
30.0)
zlam_in = 0.227865
zlam_e_in = 0.00629995
zlam_out = 0.228654
zlam_e_out = 0.00840213
zlam_new, zlam_e_new = zlambda_corr.apply_correction(
24.5, zlam_in, zlam_e_in)
testing.assert_almost_equal(zlam_new, zlam_out, 5)
testing.assert_almost_equal(zlam_e_new, zlam_e_out, 5)
def runTest(self):
"""
First test the filters:
nfw, lum, and bkg
"""
# all new...
file_path = 'data_for_tests'
cluster = Cluster()
conf_filename = 'testconfig.yaml'
cluster.config = Configuration(file_path + '/' + conf_filename)
filename = 'test_cluster_members.fit'
neighbors = GalaxyCatalog.from_fits_file(file_path + '/' + filename)
cluster.set_neighbors(neighbors)
zred_filename = 'test_dr8_pars.fit'
cluster.zredstr = RedSequenceColorPar(file_path + '/' + zred_filename, fine=True)
bkg_filename = 'test_bkg.fit'
cluster.bkg = Background('%s/%s' % (file_path, bkg_filename))
hdr=fitsio.read_header(file_path+'/'+filename,ext=1)
#cluster.z = hdr['Z']
#cluster.update_z(hdr['Z'])
cluster.redshift = hdr['Z']
richness_compare = hdr['LAMBDA']
richness_compare_err = hdr['LAMBDA_E']
scaleval_compare = hdr['SCALEVAL']
cpars_compare = np.array([hdr['CPARS0'], hdr['CPARS1'], hdr['CPARS2'], hdr['CPARS3']])
cval_compare = hdr['CVAL']
mstar_compare = hdr['MSTAR']
cluster.ra = hdr['RA']
cluster.dec = hdr['DEC']
mask = HPMask(cluster.config)
mask.set_radmask(cluster)
depthstr = DepthMap(cluster.config)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec, cluster.mpc_scale)
# Test the NFW profile on its own
# (this works to 5 decimal places because of the 2*pi*r scaling)
nfw_python = cluster._calc_radial_profile()
testing.assert_almost_equal(nfw_python, neighbors.nfw/(2.*np.pi*neighbors.r),5)
# Test the luminosity
# The problem is that the IDL code has multiple ways of computing the index.
# And this should be fixed here. I don't want to duplicate dumb ideas.
#mstar = cluster.zredstr.mstar(cluster.z)
#testing.assert_almost_equal(mstar, mstar_compare, 3)
#maxmag = mstar - 2.5*np.log10(cluster.config.lval_reference)
#lum_python = cluster._calc_luminosity(maxmag)
#testing.assert_almost_equal(lum_python, neighbors.lumwt, 3)
# Test theta_i
#theta_i_python = calc_theta_i(neighbors.refmag, neighbors.refmag_err,
# maxmag, cluster.zredstr.limmag)
#testing.assert_almost_equal(theta_i_python, neighbors.theta_i, 3)
# Test the background
# Note that this uses the input chisq values
bkg_python = cluster.calc_bkg_density(cluster.neighbors.r,
cluster.neighbors.chisq,
cluster.neighbors.refmag)
# this is cheating here...
to_test, = np.where((cluster.neighbors.refmag < cluster.bkg.refmagbins[-1]))
#testing.assert_almost_equal(bkg_python[to_test], neighbors.bcounts[to_test], 3)
#testing.assert_allclose(bkg_python[to_test], neighbors.bcounts[to_test],
# rtol=1e-4, atol=0)
# skip this test for now. Blah.
# Now the cluster tests
# cluster.neighbors.dist = np.degrees(cluster.neighbors.r / cluster.cosmo.Da(0, cluster.redshift))
seed = 0
random.seed(seed = 0)
richness = cluster.calc_richness(mask)
# these are regression tests. Various mask issues make the matching
# to idl for the time being
testing.assert_almost_equal(cluster.Lambda, 23.86299324)
testing.assert_almost_equal(cluster.lambda_e, 2.4780307)
#testing.assert_almost_equal(cluster.neighbors.theta_i,
# neighbors.theta_i, 3)
#testing.assert_almost_equal(cluster.neighbors.theta_r,
# neighbors.theta_r, 3)
#testing.assert_almost_equal(cluster.neighbors.p,
# neighbors.p, 3)
return
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):
file_path = 'data_for_tests'
cluster = Cluster()
conf_filename = 'testconfig.yaml'
cluster.config = Configuration(file_path + '/' + conf_filename)
filename = 'test_wcen_zred_data.fit'
tempcat = fitsio.read(file_path + '/' + filename, ext=1)
temp_neighbors = np.zeros(tempcat[0]['RAS'].size,
dtype=[('RA', 'f8'), ('DEC', 'f8'),
('DIST', 'f4'), ('R', 'f4'),
('P', 'f4'), ('PFREE', 'f4'),
('PMEM', 'f4'), ('MAG', 'f4', 5),
('MAG_ERR', 'f4', 5),
('REFMAG', 'f4'),
('REFMAG_ERR', 'f4'), ('CHISQ', 'f4'),
('ZRED', 'f4'), ('ZRED_E', 'f4'),
('ZRED_CHISQ', 'f4')])
temp_neighbors['RA'] = tempcat[0]['RAS']
temp_neighbors['DEC'] = tempcat[0]['DECS']
temp_neighbors['R'] = tempcat[0]['R']
temp_neighbors['P'] = tempcat[0]['PVALS']
temp_neighbors['PFREE'] = tempcat[0]['WVALS']
temp_neighbors['PMEM'] = tempcat[0]['WTVALS']
temp_neighbors['REFMAG'] = tempcat[0]['REFMAG_TOTAL']
temp_neighbors['ZRED'] = tempcat[0]['GZREDS']
temp_neighbors['ZRED_E'] = tempcat[0]['GZREDE']
temp_neighbors['ZRED_CHISQ'] = tempcat[0]['GCHISQ']
temp_neighbors['DIST'] = tempcat[0]['R'] / (
np.radians(1.) *
cluster.config.cosmo.Da(0, tempcat[0]['ZCLUSTER']))
neighbors = GalaxyCatalog(temp_neighbors)
cluster.set_neighbors(neighbors)
zred_filename = 'test_dr8_pars.fit'
cluster.zredstr = RedSequenceColorPar(file_path + '/' + zred_filename,
fine=True)
bkg_filename = 'test_bkg.fit'
cluster.bkg = Background('%s/%s' % (file_path, bkg_filename))
cluster.zredbkg = ZredBackground('%s/%s' % (file_path, bkg_filename))
cluster.redshift = tempcat[0]['ZCLUSTER']
cluster.ra = tempcat[0]['RAC']
cluster.dec = tempcat[0]['DECC']
cluster.r_lambda = 1.0 * (tempcat[0]['LAMBDA'] / 100.0)**0.2
cluster.Lambda = tempcat[0]['LAMBDA']
cluster.scaleval = tempcat[0]['SCALEVAL']
corr_filename = 'test_dr8_zlambdacorr.fit'
zlambda_corr = ZlambdaCorrectionPar(file_path + '/' + corr_filename,
30.0)
# And the meat of it...
cent = CenteringWcenZred(cluster, zlambda_corr=zlambda_corr)
cent.find_center()
testing.assert_almost_equal(cent.p_cen,
tempcat[0]['PCEN'][tempcat[0]['GOOD']], 5)
testing.assert_almost_equal(cent.q_cen,
tempcat[0]['QCEN'][tempcat[0]['GOOD']], 4)
testing.assert_almost_equal(cent.p_sat, tempcat[0]['PSAT'], 4)
testing.assert_almost_equal(cent.p_fg, tempcat[0]['PFG'], 4)
testing.assert_array_equal(cent.index,
tempcat[0]['USE'][tempcat[0]['GOOD']])
def runTest(self):
file_path = 'data_for_tests'
conffile = 'testconfig.yaml'
config = Configuration(file_path + '/' + conffile)
gals_all = GalaxyCatalog.from_galfile(config.galfile)
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename, fine=True)
bkg_filename = 'test_bkg.fit'
bkg = Background('%s/%s' % (file_path, bkg_filename))
mask = HPMask(config)
depthstr = DepthMap(config)
#cosmo = Cosmo()
testcatfile = 'test_cluster_pos.fit'
cat = ClusterCatalog.from_catfile(file_path + '/' + testcatfile,
zredstr=zredstr,
config=config,
bkg=bkg)
# test single neighbors...
c0 = cat[0]
c0.find_neighbors(0.2, gals_all)
c1 = cat[1]
c1.find_neighbors(0.2, gals_all)
testing.assert_equal(c0.neighbors.size, 580)
testing.assert_equal(c1.neighbors.size, 298)
testing.assert_array_less(c0.neighbors.dist, 0.2)
testing.assert_array_less(c1.neighbors.dist, 0.2)
# and multi-match...
i0, i1, dist = gals_all.match_many(cat.ra, cat.dec, 0.2)
u0, = np.where(i0 == 0)
testing.assert_equal(c0.neighbors.size, u0.size)
u1, = np.where(i0 == 1)
testing.assert_equal(c1.neighbors.size, u1.size)
# and compute the richness on the first one...
mask.set_radmask(c0)
depthstr.calc_maskdepth(mask.maskgals, c0.ra, c0.dec, c0.mpc_scale)
richness = c0.calc_richness(mask)
# Make sure the numbers were propagated to the parent catalog
testing.assert_equal(richness, cat.Lambda[0])
testing.assert_equal(c0.Lambda_e, cat.Lambda_e[0])
testing.assert_equal(c0.scaleval, cat.scaleval[0])
# And make sure the numbers are correct
testing.assert_almost_equal(richness, 23.86299324)
def runTest(self):
"""
Run the ClusterTest
"""
# all new...
random.seed(seed=12345)
file_path = 'data_for_tests'
cluster = Cluster()
conf_filename = 'testconfig.yaml'
cluster.config = Configuration(file_path + '/' + conf_filename)
filename = 'test_cluster_members.fit'
neighbors = GalaxyCatalog.from_fits_file(file_path + '/' + filename)
cluster.set_neighbors(neighbors)
zred_filename = 'test_dr8_pars.fit'
cluster.zredstr = RedSequenceColorPar(file_path + '/' + zred_filename,
fine=True)
bkg_filename = 'test_bkg.fit'
cluster.bkg = Background('%s/%s' % (file_path, bkg_filename))
hdr = fitsio.read_header(file_path + '/' + filename, ext=1)
cluster.redshift = hdr['Z']
richness_compare = hdr['LAMBDA']
richness_compare_err = hdr['LAMBDA_E']
scaleval_compare = hdr['SCALEVAL']
cpars_compare = np.array(
[hdr['CPARS0'], hdr['CPARS1'], hdr['CPARS2'], hdr['CPARS3']])
cval_compare = hdr['CVAL']
mstar_compare = hdr['MSTAR']
cluster.ra = hdr['RA']
cluster.dec = hdr['DEC']
mask = HPMask(cluster.config)
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
mask.set_radmask(cluster)
depthstr = DepthMap(cluster.config)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec,
cluster.mpc_scale)
# Test the NFW profile on its own
# (this works to 5 decimal places because of the 2*pi*r scaling)
nfw_python = cluster._calc_radial_profile()
testing.assert_almost_equal(nfw_python,
neighbors.nfw / (2. * np.pi * neighbors.r),
5)
# Test the background
# Note that this uses the input chisq values
bkg_python = cluster.calc_bkg_density(cluster.neighbors.r,
cluster.neighbors.chisq,
cluster.neighbors.refmag)
# this is cheating here...
to_test, = np.where(
(cluster.neighbors.refmag < cluster.bkg.refmagbins[-1]))
seed = 0
random.seed(seed=0)
richness = cluster.calc_richness(mask)
# these are regression tests. Various mask issues make the matching
# to idl for the time being
testing.assert_almost_equal(cluster.Lambda, 24.366407, 5)
testing.assert_almost_equal(cluster.lambda_e, 2.5137918, 5)
return
def runTest(self):
"""
Run tests on redmapper.Zlambda
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
conf_filename = 'testconfig.yaml'
config = Configuration(file_path + '/' + conf_filename)
filename = 'test_cluster_members.fit'
neighbors = GalaxyCatalog.from_fits_file(file_path + '/' + filename)
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename,fine = True)
bkg_filename = 'test_bkg.fit'
bkg = Background('%s/%s' % (file_path, bkg_filename))
cluster = Cluster(config=config, zredstr=zredstr, bkg=bkg, neighbors=neighbors)
hdr=fitsio.read_header(file_path+'/'+filename,ext=1)
cluster.redshift = hdr['Z']
richness_compare = hdr['LAMBDA']
richness_compare_err = hdr['LAMBDA_E']
cluster.ra = hdr['RA']
cluster.dec = hdr['DEC']
#Set up the mask
mask = HPMask(cluster.config) #Create the mask
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
mask.set_radmask(cluster)
#depthstr
depthstr = DepthMap(cluster.config)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec, cluster.mpc_scale)
cluster.neighbors.dist = np.degrees(cluster.neighbors.r/cluster.cosmo.Dl(0,cluster.redshift))
# make a zlambda object
zlam = Zlambda(cluster)
z_lambda, z_lambda_e = zlam.calc_zlambda(cluster.redshift, mask, calc_err=True, calcpz=True)
# I am not sure why this isn't repeatable better than this
testing.assert_almost_equal(cluster.z_lambda, 0.22666427, 6)
testing.assert_almost_equal(cluster.z_lambda_err, 0.00443601, 4)
# zlambda_err test
z_lambda_err = zlam._zlambda_calc_gaussian_err(cluster.z_lambda)
testing.assert_almost_equal(z_lambda_err, 0.0063738347, 5)
# and test the correction on its own
corr_filename = 'test_dr8_zlambdacorr.fit'
zlambda_corr = ZlambdaCorrectionPar(file_path + '/' + corr_filename, zlambda_pivot=30.0)
zlam_in = 0.227865
zlam_e_in = 0.00629995
zlam_out = 0.228654
zlam_e_out = 0.00840213
zlam_new, zlam_e_new = zlambda_corr.apply_correction(24.5, zlam_in, zlam_e_in)
testing.assert_almost_equal(zlam_new, zlam_out, 5)
testing.assert_almost_equal(zlam_e_new, zlam_e_out, 5)
def runTest(self):
"""
Run the ClusterCatalog tests.
"""
random.seed(seed=12345)
file_path = 'data_for_tests'
conffile = 'testconfig.yaml'
config = Configuration(file_path + '/' + conffile)
gals_all = GalaxyCatalog.from_galfile(config.galfile)
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename, fine=True)
bkg_filename = 'test_bkg.fit'
bkg = Background('%s/%s' % (file_path, bkg_filename))
mask = HPMask(config)
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
depthstr = DepthMap(config)
testcatfile = 'test_cluster_pos.fit'
cat = ClusterCatalog.from_catfile(file_path + '/' + testcatfile,
zredstr=zredstr,
config=config,
bkg=bkg)
# test single neighbors...
c0 = cat[0]
c0.find_neighbors(0.2, gals_all)
c1 = cat[1]
c1.find_neighbors(0.2, gals_all)
testing.assert_equal(c0.neighbors.size, 580)
testing.assert_equal(c1.neighbors.size, 298)
testing.assert_array_less(c0.neighbors.dist, 0.2)
testing.assert_array_less(c1.neighbors.dist, 0.2)
# and multi-match...
i0, i1, dist = gals_all.match_many(cat.ra, cat.dec, 0.2)
u0, = np.where(i0 == 0)
testing.assert_equal(c0.neighbors.size, u0.size)
u1, = np.where(i0 == 1)
testing.assert_equal(c1.neighbors.size, u1.size)
# and compute the richness on the first one...
mask.set_radmask(c0)
depthstr.calc_maskdepth(mask.maskgals, c0.ra, c0.dec, c0.mpc_scale)
richness = c0.calc_richness(mask)
# Make sure the numbers were propagated to the parent catalog
testing.assert_equal(richness, cat.Lambda[0])
testing.assert_equal(c0.Lambda_e, cat.Lambda_e[0])
testing.assert_equal(c0.scaleval, cat.scaleval[0])
# And make sure the numbers are correct
testing.assert_almost_equal(richness, 24.4121723)
def test_galaxycatalog_create(self):
"""
Run `redmapper.GalaxyCatalogMaker` tests.
"""
# Make a test directory
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
info_dict = {'LIM_REF': 21.0,
'REF_IND': 3,
'AREA': 25.0,
'NMAG': 5,
'MODE': 'SDSS',
'ZP': 22.5,
'U_IND': 0,
'G_IND': 1,
'R_IND': 2,
'I_IND': 3,
'Z_IND': 3}
# Test 1: read in catalog, write it to a single file, and then
# try to split it up
# make sure that it makes it properly, and that the output number
# of files is the same as input number of files.
configfile = os.path.join('data_for_tests', 'testconfig.yaml')
config = Configuration(configfile)
gals = GalaxyCatalog.from_galfile(config.galfile)
tab = Entry.from_fits_file(config.galfile)
maker = GalaxyCatalogMaker(os.path.join(self.test_dir, 'test_working'), info_dict, nside=tab.nside)
maker.append_galaxies(gals._ndarray)
maker.finalize_catalog()
tab2 = Entry.from_fits_file(os.path.join(self.test_dir, 'test_working_master_table.fit'))
self.assertEqual(tab.nside, tab2.nside)
self.assertEqual(tab.filenames.size, tab2.filenames.size)
for filename in tab2.filenames:
self.assertTrue(os.path.isfile(os.path.join(self.test_dir, filename.decode())))
# Test 2: Make a catalog that has an incomplete dtype
dtype = [('id', 'i8'),
('ra', 'f8')]
maker = GalaxyCatalogMaker(os.path.join(self.test_dir, 'test'), info_dict)
testgals = np.zeros(10, dtype=dtype)
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
# Test 3: make a catalog that has the wrong number of magnitudes
dtype = GalaxyCatalogMaker.get_galaxy_dtype(3)
testgals = np.zeros(10, dtype=dtype)
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
# Test 4: make a catalog that has some NaNs
dtype = GalaxyCatalogMaker.get_galaxy_dtype(info_dict['NMAG'])
testgals = np.ones(10, dtype=dtype)
testgals['mag'][0, 1] = np.nan
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
# Test 5: make a catalog that has ra/dec out of range
testgals = np.ones(10, dtype=dtype)
testgals['ra'][1] = -1.0
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
testgals = np.ones(10, dtype=dtype)
testgals['dec'][1] = -100.0
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
# Test 6: make a catalog that has mag > 90.0
testgals = np.ones(10, dtype=dtype)
testgals['mag'][0, 1] = 100.0
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
# Test 7: make a catalog that has mag_err == 0.0
testgals = np.ones(10, dtype=dtype)
testgals['mag_err'][0, 1] = 0.0
self.assertRaises(RuntimeError, maker.append_galaxies, testgals)
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_zred_runpixels(self):
"""
Test redmapper.ZredRunPixels, computing zreds for all the galaxies
in a pixelized galaxy catalog.
"""
file_path = 'data_for_tests'
configfile = 'testconfig.yaml'
config = Configuration(os.path.join(file_path, configfile))
config.d.hpix = 2163
config.d.nside = 64
config.border = 0.0
self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestRedmapper-')
config.zredfile = os.path.join(self.test_dir, 'zreds',
'testing_zreds_master_table.fit')
# FIXME: try an illegal one...
zredRunpix = ZredRunPixels(config)
zredRunpix.run()
# Check that the zred file has been built...
self.assertTrue(os.path.isfile(config.zredfile))
# Read in just the galaxies...
gals0 = GalaxyCatalog.from_galfile(config.galfile,
nside=config.d.nside,
hpix=config.d.hpix,
border=config.border)
# And with the zreds...
gals = GalaxyCatalog.from_galfile(config.galfile,
zredfile=config.zredfile,
nside=config.d.nside,
hpix=config.d.hpix,
border=config.border)
# Confirm they're the same galaxies...
testing.assert_array_almost_equal(gals0.ra, gals.ra)
# Confirm the zreds are okay
self.assertGreater(np.min(gals.zred), 0.0)
self.assertGreater(np.min(gals.chisq), 0.0)
self.assertLess(np.max(gals.lkhd), 0.0)
zredfile = os.path.join(file_path, 'zreds_test',
'dr8_test_zreds_master_table.fit')
gals_compare = GalaxyCatalog.from_galfile(config.galfile,
zredfile=zredfile,
nside=config.d.nside,
hpix=config.d.hpix,
border=config.border)
zredstr = RedSequenceColorPar(config.parfile)
mstar_input = zredstr.mstar(gals_compare.zred_uncorr)
mstar = zredstr.mstar(gals_compare.zred_uncorr)
ok, = np.where(
(gals_compare.refmag < (mstar_input - 2.5 * np.log10(0.15)))
| (gals_compare.refmag < (mstar - 2.5 * np.log10(0.15))))
delta_zred_uncorr = gals.zred_uncorr[ok] - gals_compare.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.98, float(use.size) / float(ok.size))
def test_zred(self):
"""
Test redmapper.ZredColor, looping over galaxies.
"""
file_path = 'data_for_tests'
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename)
galaxy_filename = 'test_dr8_gals_with_zred.fit'
galaxies = GalaxyCatalog.from_fits_file(file_path + '/' +
galaxy_filename)
galaxies_input = copy.deepcopy(galaxies)
# start with the first one...
zredc = ZredColor(zredstr)
zredc.compute_zred(galaxies[0])
starttime = time.time()
zredc.compute_zreds(galaxies)
print("Ran %d galaxies in %.3f seconds" %
(galaxies.size, time.time() - starttime))
# Only compare galaxies that are brighter than 0.15L* in either old OR new
# Otherwise, we're just comparing how the codes handle "out-of-range"
# galaxies, and that does not matter
mstar_input = zredstr.mstar(galaxies_input.zred_uncorr)
mstar = zredstr.mstar(galaxies.zred_uncorr)
ok, = np.where((galaxies.refmag < (mstar_input - 2.5 * np.log10(0.15)))
| (galaxies.refmag < (mstar - 2.5 * np.log10(0.15))))
delta_zred_uncorr = galaxies.zred_uncorr[
ok] - galaxies_input.zred_uncorr[ok]
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.9, float(use.size) / float(ok.size))
delta_zred = galaxies.zred[ok[use]] - galaxies_input.zred[ok[use]]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99,
float(use2.size) / float(delta_zred.size))
delta_zred2 = galaxies.zred2[ok[use]] - galaxies_input.zred2[ok[use]]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99,
float(use2.size) / float(delta_zred2.size))
delta_zred_uncorr_e = galaxies.zred_uncorr_e[
ok[use]] - galaxies_input.zred_uncorr_e[ok[use]]
use2, = np.where(np.abs(delta_zred_uncorr_e) < 1e-3)
testing.assert_array_less(
0.98,
float(use2.size) / float(delta_zred_uncorr_e.size))
delta_zred_e = galaxies.zred_e[ok[use]] - galaxies_input.zred_e[
ok[use]]
use2, = np.where(np.abs(delta_zred_e) < 1e-3)
testing.assert_array_less(0.98,
float(use2.size) / float(delta_zred_e.size))
delta_zred2_e = galaxies.zred2_e[ok[use]] - galaxies_input.zred2_e[
ok[use]]
use2, = np.where(np.abs(delta_zred2_e) < 1e-3)
testing.assert_array_less(0.98,
float(use2.size) / float(delta_zred2_e.size))
def runTest(self):
file_path = 'data_for_tests'
zred_filename = 'test_dr8_pars.fit'
zredstr = RedSequenceColorPar(file_path + '/' + zred_filename)
galaxy_filename = 'test_dr8_gals_with_zred.fit'
galaxies = GalaxyCatalog.from_fits_file(file_path + '/' +
galaxy_filename)
galaxies_input = copy.deepcopy(galaxies)
# start with the first one...
zredc = ZredColor(zredstr, adaptive=True)
zredc.compute_zred(galaxies[0])
starttime = time.time()
for i, g in enumerate(galaxies):
try:
zredc.compute_zred(g)
except:
print("Crashed on %d" % (i))
print("Ran %d galaxies in %.3f seconds" %
(galaxies.size, time.time() - starttime))
# make sure we have reasonable consistency...
# It seems that at least some of the discrepancies are caused by
# slight bugs in the IDL implementation.
delta_zred_uncorr = galaxies.zred_uncorr - galaxies_input.zred_uncorr
use, = np.where(np.abs(delta_zred_uncorr) < 1e-3)
testing.assert_array_less(0.9, float(use.size) / float(galaxies.size))
delta_zred = galaxies.zred[use] - galaxies_input.zred[use]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99,
float(use2.size) / float(delta_zred.size))
delta_zred2 = galaxies.zred2[use] - galaxies_input.zred2[use]
use2, = np.where(np.abs(delta_zred) < 1e-3)
testing.assert_array_less(0.99,
float(use2.size) / float(delta_zred2.size))
delta_zred_uncorr_e = galaxies.zred_uncorr_e[
use] - galaxies_input.zred_uncorr_e[use]
use2, = np.where(np.abs(delta_zred_uncorr_e) < 1e-3)
testing.assert_array_less(
0.98,
float(use2.size) / float(delta_zred_uncorr_e.size))
delta_zred_e = galaxies.zred_e[use] - galaxies_input.zred_e[use]
use2, = np.where(np.abs(delta_zred_e) < 1e-3)
testing.assert_array_less(0.98,
float(use2.size) / float(delta_zred_e.size))
delta_zred2_e = galaxies.zred2_e[use] - galaxies_input.zred2_e[use]
use2, = np.where(np.abs(delta_zred2_e) < 1e-3)
testing.assert_array_less(0.98,
float(use2.size) / float(delta_zred2_e.size))
def runTest(self):
"""
Run the ClusterTest
"""
# all new...
random.seed(seed=12345)
file_path = 'data_for_tests'
cluster = Cluster()
conf_filename = 'testconfig.yaml'
cluster.config = Configuration(file_path + '/' + conf_filename)
filename = 'test_cluster_members.fit'
neighbors = GalaxyCatalog.from_fits_file(file_path + '/' + filename)
cluster.set_neighbors(neighbors)
zred_filename = 'test_dr8_pars.fit'
cluster.zredstr = RedSequenceColorPar(file_path + '/' + zred_filename, fine=True)
bkg_filename = 'test_bkg.fit'
cluster.bkg = Background('%s/%s' % (file_path, bkg_filename))
hdr=fitsio.read_header(file_path+'/'+filename,ext=1)
cluster.redshift = hdr['Z']
richness_compare = hdr['LAMBDA']
richness_compare_err = hdr['LAMBDA_E']
scaleval_compare = hdr['SCALEVAL']
cpars_compare = np.array([hdr['CPARS0'], hdr['CPARS1'], hdr['CPARS2'], hdr['CPARS3']])
cval_compare = hdr['CVAL']
mstar_compare = hdr['MSTAR']
cluster.ra = hdr['RA']
cluster.dec = hdr['DEC']
mask = HPMask(cluster.config)
maskgal_index = mask.select_maskgals_sample(maskgal_index=0)
mask.set_radmask(cluster)
depthstr = DepthMap(cluster.config)
depthstr.calc_maskdepth(mask.maskgals, cluster.ra, cluster.dec, cluster.mpc_scale)
# Test the NFW profile on its own
# (this works to 5 decimal places because of the 2*pi*r scaling)
nfw_python = cluster._calc_radial_profile()
testing.assert_almost_equal(nfw_python, neighbors.nfw/(2.*np.pi*neighbors.r),5)
# Test the background
# Note that this uses the input chisq values
bkg_python = cluster.calc_bkg_density(cluster.neighbors.r,
cluster.neighbors.chisq,
cluster.neighbors.refmag)
# this is cheating here...
to_test, = np.where((cluster.neighbors.refmag < cluster.bkg.refmagbins[-1]))
seed = 0
random.seed(seed = 0)
richness = cluster.calc_richness(mask)
# these are regression tests. Various mask issues make the matching
# to idl for the time being
testing.assert_almost_equal(cluster.Lambda, 24.366407, 5)
testing.assert_almost_equal(cluster.lambda_e, 2.5137918, 5)
return