def test_shapenoise():
"""
Test that the shape noise distribution is Gaussian around the shear and does not produce unphysical ellipticities.
"""
np.random.seed(285713)
# Verify that shape noise does not produce unrealistic ellipticities
data = mock.generate_galaxy_catalog(10**15., 0.3, 4, cosmo, 0.8, ngals=50000,shapenoise=0.5)
# Check that there are no galaxies with |e|>1
assert_equal(np.count_nonzero((data['e1']>1) | (data['e1']<-1)),0)
assert_equal(np.count_nonzero((data['e2']>1) | (data['e2']<-1)),0)
# Verify that the shape noise is Gaussian around 0 (for the very small shear here)
sigma=0.25
data = mock.generate_galaxy_catalog(10**12., 0.3, 4, cosmo, 0.8, ngals=50000,shapenoise=sigma)
# Check that there are no galaxies with |e|>1
assert_equal(np.count_nonzero((data['e1']>1) | (data['e1']<-1)),0)
assert_equal(np.count_nonzero((data['e2']>1) | (data['e2']<-1)),0)
# Check that shape noise is Guassian with correct std dev
bins=np.arange(-1,1.1,0.1)
gauss = 5000*np.exp(-0.5*(bins[:-1]+0.05)**2/sigma**2)/(sigma*np.sqrt(2*np.pi))
assert_allclose(np.histogram(data['e1'],bins=bins)[0],gauss,atol=50,rtol=0.05)
assert_allclose(np.histogram(data['e2'],bins=bins)[0],gauss,atol=50,rtol=0.05)
def test_z_distr():
"""
Test the redshift distribution options: single plan, uniform, Chang13.
"""
np.random.seed(256429)
# Set up mock cluster
ngals=50000; mass=15.
zmin=0.4; zmax=3.0
bins = np.arange(zmin,zmax+0.1,0.1)
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 0.8, ngals=ngals)
# Check that all galaxies are at z=0.8
assert_equal(np.count_nonzero(data['z']!=0.8),0)
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 'uniform', ngals=260000,
zsrc_min=zmin, zsrc_max=zmax)
# Check that all galaxies are within the given limits
assert_equal(np.count_nonzero((data['z']<zmin)|(data['z']>zmax)),0)
# Check that the z distribution is uniform
hist = np.histogram(data['z'],bins=bins)
assert_allclose(hist[0],10000*np.ones(len(hist[0])),atol=200,rtol=0.02)
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 'chang13', ngals=ngals,
zsrc_min=zmin, zsrc_max=zmax)
# Check that there all galaxies are within the given limits
assert_equal(np.count_nonzero((data['z']<zmin)|(data['z']>zmax)),0)
# Check that the z distribution follows Chang13 distribution
hist = np.histogram(data['z'],bins=bins)
chang = np.array([mock._chang_z_distrib(z) for z in bins[:-1]+0.05])
assert_allclose(hist[0],chang*ngals/2,atol=100,rtol=0.1)
def test_z_distr():
"""
Test the redshift distribution options: single plan, uniform, Chang13, DESC SRD
"""
np.random.seed(256429)
# Set up mock cluster
ngals=50000; mass=15.
zmin=0.4; zmax=3.0
bins = np.arange(zmin,zmax+0.1,0.1)
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 0.8, ngals=ngals)
# Check that all galaxies are at z=0.8
assert_equal(np.count_nonzero(data['z']!=0.8),0)
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 'uniform', ngals=260000,
zsrc_min=zmin, zsrc_max=zmax)
# Check that all galaxies are within the given limits
assert_equal(np.count_nonzero((data['z']<zmin)|(data['z']>zmax)),0)
# Check that the z distribution is uniform
hist = np.histogram(data['z'],bins=bins)
assert_allclose(hist[0],10000*np.ones(len(hist[0])),atol=200,rtol=0.02)
# Check the Chang et al. (2013) redshift distribution
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 'chang13', ngals=ngals,
zsrc_min=zmin, zsrc_max=zmax)
# Check that there all galaxies are within the given limits
assert_equal(np.count_nonzero((data['z']<zmin)|(data['z']>zmax)),0)
# Check that the z distribution follows Chang13 distribution
hist = np.histogram(data['z'],bins=bins)
norm,_ = quad(mock._chang_z_distrib, zmin, zmax)
# Expected number of galaxies in bin i = Ntot*distrib(z_{bin center})*binsize/norm
chang = np.array([mock._chang_z_distrib(z)*ngals*0.1/norm for z in bins[:-1]+0.05])
assert_allclose(hist[0],chang,atol=100,rtol=0.1)
# Check the DESC SRD (2018) redshift distribution
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 'desc_srd', ngals=ngals,
zsrc_min=zmin, zsrc_max=zmax)
# Check that there all galaxies are within the given limits
assert_equal(np.count_nonzero((data['z']<zmin)|(data['z']>zmax)),0)
# Check that the z distribution follows Chang13 distribution
hist = np.histogram(data['z'],bins=bins)
norm,_ = quad(mock._srd_z_distrib, zmin, zmax)
# Expected number of galaxies in bin i = Ntot*distrib(z_{bin center})*binsize/norm
srd = np.array([mock._srd_z_distrib(z)*ngals*0.1/norm for z in bins[:-1]+0.05])
assert_allclose(hist[0],srd,atol=100,rtol=0.1)
def mass_mock_cluster(mass=15.,guess=15.):
# Set up mock cluster
ngals=5000
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 0.8, ngals=ngals)
# Check whether the given ngals is the retrieved ngals
assert_equal(len(data['ra']),ngals)
# Check that there are no galaxies with |e|>1
assert_equal(np.count_nonzero((data['e1']>1) | (data['e1']<-1)),0)
assert_equal(np.count_nonzero((data['e2']>1) | (data['e2']<-1)),0)
# Create shear profile
cl = clmm.GalaxyCluster("test_cluster", 0.0, 0.0, 0.3, data)
theta, g_t, g_x = cl.compute_tangential_and_cross_components(geometry="flat")
binned = cl.make_radial_profile("Mpc", bins=da.make_bins(0.5, 5.0, 100),
cosmo=cosmo, include_empty_bins=False)
popt,pcov = fitters['curve_fit'](lambda r, logm: nfw_shear_profile(r, logm, 0.8),
binned['radius'],
binned['gt'],
np.ones_like(binned['gt'])*1.e-5,
bounds=[13.,17.],p0=guess)
return popt[0]
Omega_dm0=0.27 - 0.045,
Omega_b0=0.045,
Omega_k0=0.0)
cluster_m = 1.e15 # M200,m [Msun]
logm = np.log(cluster_m) / np.log(10)
concentration = 4
cluster_ra = 0.
cluster_dec = 0.
cluster_z = 0.4
ngals = 10000
ideal_data = mock.generate_galaxy_catalog(cluster_m,
cluster_z,
concentration,
cosmo,
'chang13',
zsrc_min=cluster_z + 0.1,
ngals=ngals)
noisy_data = mock.generate_galaxy_catalog(cluster_m,
cluster_z,
concentration,
cosmo,
'chang13',
zsrc_min=cluster_z + 0.1,
shapenoise=0.05,
photoz_sigma_unscaled=0.05,
ngals=ngals)
#galaxy catalogs
cluster_id = "CL_ideal"
def test_mock_data():
""" Run generate_galaxy_catalog 1000 times and assert that retrieved mass is always consistent with input
"""
# Basic raise tests
assert_raises(ValueError, mock.generate_galaxy_catalog, 1e15, 0.3, 4, cosmo, 0.8, ngals=None)
assert_raises(ValueError, mock.generate_galaxy_catalog, 1e15, 0.3, 4, cosmo, 0.8, ngals=1, ngal_density=1)
assert_raises(ValueError, mock.generate_galaxy_catalog, 1e15, 0.3, 4, cosmo, 'unknown_src', ngals=10)
# Test warning if bad gals
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# Trigger a warning.
np.random.seed(314)
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 0.30001, ngals=1000, nretry=0)
# Verify some things
assert len(w) == 1
# Simple test to check if option with ngal_density is working
# A proper test should be implemented
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 0.8, ngals=None, ngal_density=1)
# Simple test to check if option with zsrc=chang13 is working
# A proper test should be implemented
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 'chang13', ngals=100)
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 'chang13', ngal_density=1)
# Simple test to check if option with zsrc=desc_src is working
# A proper test should be implemented
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 'desc_srd', ngals=100)
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 'desc_srd', ngal_density=1)
# Simple test to check if option with pdz is working
# A proper test should be implemented
mock.generate_galaxy_catalog(1e15, 0.3, 4, cosmo, 0.8, ngals=100, photoz_sigma_unscaled=.1)
def nfw_shear_profile(r, logm, z_src):
m = 10.**logm
gt_model = clmm.compute_reduced_tangential_shear(r,
m, 4, 0.3, z_src, cosmo,
delta_mdef=200,
halo_profile_model='nfw')
return gt_model
def mass_mock_cluster(mass=15.,guess=15.):
# Set up mock cluster
ngals=5000
data = mock.generate_galaxy_catalog(10**mass, 0.3, 4, cosmo, 0.8, ngals=ngals)
# Check whether the given ngals is the retrieved ngals
assert_equal(len(data['ra']),ngals)
# Check that there are no galaxies with |e|>1
assert_equal(np.count_nonzero((data['e1']>1) | (data['e1']<-1)),0)
assert_equal(np.count_nonzero((data['e2']>1) | (data['e2']<-1)),0)
# Create shear profile
cl = clmm.GalaxyCluster("test_cluster", 0.0, 0.0, 0.3, data)
theta, g_t, g_x = cl.compute_tangential_and_cross_components(geometry="flat")
binned = cl.make_radial_profile("Mpc", bins=da.make_bins(0.5, 5.0, 100),
cosmo=cosmo, include_empty_bins=False)
popt,pcov = fitters['curve_fit'](lambda r, logm: nfw_shear_profile(r, logm, 0.8),
binned['radius'],
binned['gt'],
np.ones_like(binned['gt'])*1.e-5,
bounds=[13.,17.],p0=guess)
return popt[0]
#input_masses = np.random.uniform(13.5, 16., 25)
#logm_0 = np.random.uniform(13.5, 16., 25)
input_masses = np.array([13.82,15.94,13.97,14.17,14.95,15.49,14.93,15.59])
guess_masses = np.array([15.70,15.10,15.08,14.31,13.61,13.90,15.07,13.82])
meas_masses = [mass_mock_cluster(m,g) for m,g in zip(input_masses,guess_masses)]
assert_allclose(meas_masses,input_masses, **TOLERANCE)