def _draw_source_redshifts(zsrc, zsrc_min, zsrc_max, ngals):
"""Set source galaxy redshifts either set to a fixed value or draw from a predefined
distribution. Return a table (GCData) of the source galaxies
Uses a sampling technique found in Numerical Recipes in C, Chap 7.2: Transformation Method.
Pulling out random values from a given probability distribution.
Parameters
----------
ngals : float
Number of galaxies to generate
zsrc : float or str
Choose the source galaxy distribution to be fixed or drawn from a predefined distribution.
float : All sources galaxies at this fixed redshift
str : Draws individual source gal redshifts from predefined distribution. Options
are: chang13 or desc_srd
zsrc_min : float
The minimum source redshift allowed.
zsrc_max : float, optional
If source redshifts are drawn, the maximum source redshift
Returns
-------
galaxy_catalog : clmm.GCData
Table of true and 'measured' photometric redshifts, which here the same. Redshift photometric errors
are then added using _compute_photoz_pdfs.
Notes
-----
Much of this code in this function was adapted from the Dallas group
"""
# Set zsrc to constant value
if isinstance(zsrc, float):
zsrc_list = np.ones(ngals) * zsrc
# Draw zsrc from Chang et al. 2013
elif zsrc == 'chang13':
zsrc_list = _draw_random_points_from_distribution(
zsrc_min, zsrc_max, ngals, _chang_z_distrib)
# Draw zsrc from the distribution used in the DESC SRD (arxiv:1809.01669)
elif zsrc == 'desc_srd':
zsrc_list = _draw_random_points_from_distribution(
zsrc_min, zsrc_max, ngals, _srd_z_distrib)
# Draw zsrc from a uniform distribution between zmin and zmax
elif zsrc == 'uniform':
zsrc_list = np.random.uniform(zsrc_min, zsrc_max, ngals)
# Invalid entry
else:
raise ValueError(
"zsrc must be a float, chang13 or desc_srd. You set: {}".format(
zsrc))
return GCData([zsrc_list, zsrc_list], names=('ztrue', 'z'))
def test_print_gc():
# Cluster with empty galcat
cl = clmm.GalaxyCluster(unique_id='1',
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData())
print(cl)
assert isinstance(cl.__str__(), str)
assert isinstance(cl.__repr__(), str)
# Cluster with galcat
galcat = GCData(
[[120.1, 119.9, 119.9], [41.9, 42.2, 42.2], [1, 1, 1], [1, 2, 3]],
names=('ra', 'dec', 'z', 'id'))
cl = clmm.GalaxyCluster(unique_id='1',
ra=161.3,
dec=34.,
z=0.3,
galcat=galcat)
print(cl)
assert isinstance(cl.__str__(), str)
assert isinstance(cl.__repr__(), str)
def test_save_load():
cl1 = clmm.GalaxyCluster(unique_id='1',
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData())
cl1.save('testcluster.pkl')
cl2 = clmm.GalaxyCluster.load('testcluster.pkl')
os.system('rm testcluster.pkl')
assert_equal(cl2.unique_id, cl1.unique_id)
assert_equal(cl2.ra, cl1.ra)
assert_equal(cl2.dec, cl1.dec)
assert_equal(cl2.z, cl1.z)
def test_initialization():
testdict1 = {
'unique_id': '1',
'ra': 161.3,
'dec': 34.,
'z': 0.3,
'galcat': GCData()
}
cl1 = clmm.GalaxyCluster(**testdict1)
assert_equal(testdict1['unique_id'], cl1.unique_id)
assert_equal(testdict1['ra'], cl1.ra)
assert_equal(testdict1['dec'], cl1.dec)
assert_equal(testdict1['z'], cl1.z)
assert isinstance(cl1.galcat, GCData)
def test_compute_critical_surface_density(modeling_data):
""" Validation test for critical surface density """
reltol = modeling_data['theory_reltol']
cfg = load_validation_config()
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'],
z_cluster=cfg['TEST_CASE']['z_cluster'],
z_source=cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Check errors for z<0
assert_raises(ValueError, theo.compute_critical_surface_density, cfg['cosmo'], z_cluster=-0.2, z_source=0.3)
assert_raises(ValueError, theo.compute_critical_surface_density, cfg['cosmo'], z_cluster=0.2, z_source=-0.3)
# Check behaviour when sources are in front of the lens
z_cluster = 0.3
z_source = 0.2
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'], z_cluster=z_cluster, z_source=z_source),
np.inf, 1.0e-10)
z_source = [0.2, 0.12, 0.25]
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'], z_cluster=z_cluster, z_source=z_source),
[np.inf, np.inf, np.inf], 1.0e-10)
# Check usage with cluster object function
z_src = np.array([cfg['TEST_CASE']['z_source']])
cluster = GalaxyCluster(unique_id='blah', ra=0, dec=0, z=cfg['TEST_CASE']['z_cluster'],
galcat=GCData([0*z_src, 0*z_src, z_src],
names=('ra', 'dec', 'z')))
cluster.add_critical_surface_density(cfg['cosmo'])
assert_allclose(cluster.galcat['sigma_c'],
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Object Oriented tests
m = theo.Modeling()
m.set_cosmo(cfg['cosmo'])
assert_allclose(m.eval_critical_surface_density(cfg['TEST_CASE']['z_cluster'],
cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Check behaviour when sources are in front of the lens
z_cluster = 0.3
z_source = 0.2
assert_allclose(m.eval_critical_surface_density(z_cluster, z_source),
np.inf, 1.0e-10)
z_source = [0.2, 0.12, 0.25]
assert_allclose(m.eval_critical_surface_density(z_cluster, z_source),
[np.inf, np.inf, np.inf], 1.0e-10)
def test_make_radial_profiles():
# Set up a cluster object and compute cross and tangential shears
ra_lens, dec_lens, z_lens = 120., 42., 0.5
gals = GCData({
'ra': np.array([120.1, 119.9, 119.9]),
'dec': np.array([41.9, 42.2, 42.2]),
'id': np.array([1, 2, 3]),
'e1': np.array([0.2, 0.4, 0.4]),
'e2': np.array([0.3, 0.5, 0.5]),
'z': np.ones(3),
})
angsep_units, bin_units = 'radians', 'radians'
# Set up radial values
bins_radians = np.array([0.002, 0.003, 0.004])
expected_radius = [0.0021745039090962414, 0.0037238407383072053]
expected_flat = {
'angsep': np.array([0.0021745039090962414, 0.0037238407383072053, 0.0037238407383072053]),
'cross_shear': np.array([0.2780316984090899, 0.6398792901134982, 0.6398792901134982]),
'tan_shear': np.array([-0.22956126563459447, -0.02354769805831558, -0.02354769805831558]),
}
expected_curve = {# <<TO BE ADDED IN THE FUTURE>>
'angsep': np.array([]),
'cross_shear': np.array([]),
'tan_shear': np.array([]),
}
# Geometries to test
geo_tests = [('flat', expected_flat), ('curve', expected_curve)]
geo_tests = geo_tests[:1] # <<DELETE THIS LINE WHEN CURVE VALUES ADDED>>
for geometry, expected in geo_tests:
#######################################
### Use without cluster object ########
#######################################
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], geometry=geometry)
# Tests passing int as bins arg makes the correct bins
bins = 2
vec_bins = clmm.utils.make_bins(np.min(angsep), np.max(angsep), bins)
testing.assert_array_equal(da.make_radial_profile([tshear, xshear, gals['z']], angsep, angsep_units, bin_units, bins=bins)[0],
da.make_radial_profile([tshear, xshear, gals['z']], angsep, angsep_units, bin_units, bins=vec_bins)[0])
# Test the outputs of compute_tangential_and_cross_components just to be safe
testing.assert_allclose(angsep, expected['angsep'], **TOLERANCE,
err_msg="Angular Separation not correct when testing shear profiles")
testing.assert_allclose(tshear, expected['tan_shear'], **TOLERANCE,
err_msg="Tangential Shear not correct when testing shear profiles")
testing.assert_allclose(xshear, expected['cross_shear'], **TOLERANCE,
err_msg="Cross Shear not correct when testing shear profiles")
# Test default behavior, remember that include_empty_bins=False excludes all bins with N>=1
profile = da.make_radial_profile([tshear, xshear, gals['z']], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=False)
_test_profile_table_output(profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected['tan_shear'][1], expected['cross_shear'][1], [2])
# Test metadata
testing.assert_array_equal(profile.meta['bin_units'], bin_units)
testing.assert_array_equal(profile.meta['cosmo'], None)
# Test simple unit convesion
profile = da.make_radial_profile([tshear, xshear, gals['z']], angsep*180./np.pi, 'degrees', bin_units,
bins=bins_radians, include_empty_bins=False)
_test_profile_table_output(profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected['tan_shear'][1], expected['cross_shear'][1], [2])
# including empty bins
profile = da.make_radial_profile([tshear, xshear, gals['z']], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=True)
_test_profile_table_output(profile, bins_radians[:-1], expected_radius, bins_radians[1:],
expected['tan_shear'][:-1], expected['cross_shear'][:-1], [1,2])
# test with return_binnumber
profile, binnumber = da.make_radial_profile([tshear, xshear, gals['z']], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=True, return_binnumber=True)
_test_profile_table_output(profile, bins_radians[:-1], expected_radius, bins_radians[1:],
expected['tan_shear'][:-1], expected['cross_shear'][:-1], [1,2])
testing.assert_array_equal(binnumber, [1, 2, 2])
###################################
### Test with cluster object ######
###################################
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2', 'z', 'id'])
cluster.compute_tangential_and_cross_components(geometry=geometry)
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=False)
# Test default behavior, remember that include_empty_bins=False excludes all bins with N>=1
_test_profile_table_output(cluster.profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected['tan_shear'][1], expected['cross_shear'][1], [2],
p0='gt', p1='gx')
# including empty bins
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True, table_name='profile2')
_test_profile_table_output(cluster.profile2, bins_radians[:-1], expected_radius, bins_radians[1:],
expected['tan_shear'][:-1], expected['cross_shear'][:-1], [1,2],
p0='gt', p1='gx')
# Test with galaxy id's
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[:-1], expected_radius, bins_radians[1:],
expected['tan_shear'][:-1], expected['cross_shear'][:-1], [1,2], [[1],[2,3]],
p0='gt', p1='gx')
# Test it runs with galaxy id's and int bins
cluster.make_radial_profile(bin_units, bins=5, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
# And overwriting table
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[:-1], expected_radius, bins_radians[1:],
expected['tan_shear'][:-1], expected['cross_shear'][:-1], [1,2], [[1],[2,3]],
p0='gt', p1='gx')
# Test it runs with galaxy id's and int bins and no empty bins
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=False,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[1], expected_radius[1], bins_radians[2],
expected['tan_shear'][1], expected['cross_shear'][1], [2],
p0='gt', p1='gx')
########################################
### Basic tests of cluster object ######
########################################
# Test error of missing redshift
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2'])
cluster.compute_tangential_and_cross_components()
testing.assert_raises(TypeError, cluster.make_radial_profile, bin_units)
# Test error of missing shear
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2', 'z', 'id'])
testing.assert_raises(TypeError, cluster.make_radial_profile, bin_units)
# Test error with overwrite=False
cluster.compute_tangential_and_cross_components()
cluster.make_radial_profile(bin_units, bins=bins_radians, table_name='profile3')
testing.assert_raises(AttributeError, cluster.make_radial_profile, bin_units, bins=bins_radians,
table_name='profile3', overwrite=False)
# Check error of missing id's
cluster_noid = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2', 'z'])
cluster_noid.compute_tangential_and_cross_components()
testing.assert_raises(TypeError, cluster_noid.make_radial_profile, bin_units, gal_ids_in_bins=True)
def test_compute_tangential_and_cross_components(modeling_data):
# Input values
reltol = modeling_data['dataops_reltol']
ra_lens, dec_lens, z_lens = 120., 42., 0.5
gals = GCData({
'ra': np.array([120.1, 119.9]),
'dec': np.array([41.9, 42.2]),
'id': np.array([1, 2]),
'e1': np.array([0.2, 0.4]),
'e2': np.array([0.3, 0.5]),
'z': np.array([1.,2.]),
})
# Correct values
expected_flat = {
'angsep': np.array([0.0021745039090962414, 0.0037238407383072053]),
'cross_shear': np.array([0.2780316984090899, 0.6398792901134982]),
'tangential_shear': np.array([-0.22956126563459447, -0.02354769805831558]),
# DeltaSigma expected values for clmm.Cosmology(H0=67.66, Omega_dm0=0.262, Omega_b0=0.049)
'cross_DS': np.array([8.58093068e+14, 1.33131522e+15]), #[1224.3326297393244, 1899.6061989365176])*0.7*1.0e12*1.0002565513832675
'tangential_DS': np.array([-7.08498103e+14, -4.89926917e+13]), #[-1010.889584349285, -69.9059242788237])*0.7*1.0e12*1.0002565513832675
}
expected_curve = {# <<TO BE ADDED IN THE FUTURE>>
'angsep': np.array([]),
'cross_shear': np.array([]),
'tangential_shear': np.array([]),
'cross_DS': np.array([]),
'tangential_DS': np.array([]),
}
# Geometries to test
geo_tests = [('flat', expected_flat), ('curve', expected_curve)]
geo_tests = geo_tests[:1] # <<DELETE THIS LINE WHEN CURVE VALUES ADDED>>
# test incosnsitent data
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'][0], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'])
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'][:1], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'])
# test not implemented geometry
testing.assert_raises(NotImplementedError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], geometry='something crazy')
for geometry, expected in geo_tests:
# Pass arrays directly into function
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], geometry=geometry)
testing.assert_allclose(angsep, expected['angsep'], **TOLERANCE,
err_msg="Angular Separation not correct when passing lists")
testing.assert_allclose(tshear, expected['tangential_shear'], **TOLERANCE,
err_msg="Tangential Shear not correct when passing lists")
testing.assert_allclose(xshear, expected['cross_shear'], **TOLERANCE,
err_msg="Cross Shear not correct when passing lists")
# Pass LISTS into function
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=list(gals['ra']), dec_source=list(gals['dec']),
shear1=list(gals['e1']), shear2=list(gals['e2']), geometry=geometry)
testing.assert_allclose(angsep, expected['angsep'], **TOLERANCE,
err_msg="Angular Separation not correct when passing lists")
testing.assert_allclose(tshear, expected['tangential_shear'], **TOLERANCE,
err_msg="Tangential Shear not correct when passing lists")
testing.assert_allclose(xshear, expected['cross_shear'], **TOLERANCE,
err_msg="Cross Shear not correct when passing lists")
# Use the cluster method
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2'])
# Test error with bad name/missing column
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components,
shape_component1='crazy name')
# Test output
for geometry, expected in geo_tests:
angsep3, tshear3, xshear3 = cluster.compute_tangential_and_cross_components(geometry=geometry)
testing.assert_allclose(angsep3, expected['angsep'], **TOLERANCE,
err_msg="Angular Separation not correct when using cluster method")
testing.assert_allclose(tshear3, expected['tangential_shear'], **TOLERANCE,
err_msg="Tangential Shear not correct when using cluster method")
testing.assert_allclose(xshear3, expected['cross_shear'], **TOLERANCE,
err_msg="Cross Shear not correct when using cluster method")
# Check behaviour for the deltasigma option.
cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
# test missing info for is_deltasigma=True
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], is_deltasigma=True, cosmo=None, z_lens=z_lens, z_source=gals['z'])
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], is_deltasigma=True, cosmo=cosmo, z_lens=None, z_source=gals['z'])
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], is_deltasigma=True, cosmo=cosmo, z_lens=z_lens, z_source=None)
# check values for DeltaSigma
for geometry, expected in geo_tests:
angsep_DS, tDS, xDS = da.compute_tangential_and_cross_components(
ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=gals['ra'], dec_source=gals['dec'],
shear1=gals['e1'], shear2=gals['e2'], is_deltasigma=True,
cosmo=cosmo, z_lens=z_lens, z_source=gals['z'], geometry=geometry)
testing.assert_allclose(angsep_DS, expected['angsep'], reltol,
err_msg="Angular Separation not correct")
testing.assert_allclose(tDS, expected['tangential_DS'], reltol,
err_msg="Tangential Shear not correct")
testing.assert_allclose(xDS, expected['cross_DS'], reltol,
err_msg="Cross Shear not correct")
# Tests with the cluster object
# cluster object missing source redshift, and function call missing cosmology
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2'])
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components, is_deltasigma=True)
# cluster object OK but function call missing cosmology
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=gals['ra', 'dec', 'e1', 'e2','z'])
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components, is_deltasigma=True)
# check values for DeltaSigma
for geometry, expected in geo_tests:
angsep_DS, tDS, xDS = cluster.compute_tangential_and_cross_components(cosmo=cosmo, is_deltasigma=True, geometry=geometry)
testing.assert_allclose(angsep_DS, expected['angsep'], reltol,
err_msg="Angular Separation not correct when using cluster method")
testing.assert_allclose(tDS, expected['tangential_DS'], reltol,
err_msg="Tangential Shear not correct when using cluster method")
testing.assert_allclose(xDS, expected['cross_DS'], reltol,
err_msg="Cross Shear not correct when using cluster method")
def test_integrity(): # Converge on name
# Ensure we have all necessary values to make a GalaxyCluster
assert_raises(TypeError,
clmm.GalaxyCluster,
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=34.,
galcat=GCData())
# Test that we get errors when we pass in values outside of the domains
assert_raises(ValueError,
clmm.GalaxyCluster,
unique_id=1,
ra=-360.3,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(ValueError,
clmm.GalaxyCluster,
unique_id=1,
ra=360.3,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(ValueError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=95.,
z=0.3,
galcat=GCData())
assert_raises(ValueError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=-95.,
z=0.3,
galcat=GCData())
assert_raises(ValueError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=34.,
z=-0.3,
galcat=GCData())
# Test that inputs are the correct type
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=None,
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=34.,
z=0.3,
galcat=1)
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=34.,
z=0.3,
galcat=[])
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=None,
dec=34.,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=None,
z=0.3,
galcat=GCData())
assert_raises(TypeError,
clmm.GalaxyCluster,
unique_id=1,
ra=161.3,
dec=34.,
z=None,
galcat=GCData())
# Test that id can support numbers and strings
assert isinstance(
clmm.GalaxyCluster(unique_id=1,
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData()).unique_id, str)
# assert clmm.GalaxyCluster(unique_id=1.0, ra=161.3, dec=34., z=0.3, galcat=GCData()).unique_id == '1'
assert isinstance(
clmm.GalaxyCluster(unique_id='1',
ra=161.3,
dec=34.,
z=0.3,
galcat=GCData()).unique_id, str)
# Test that ra/dec/z can be converted from int/str to float if needed
assert clmm.GalaxyCluster('1', '161.', '55.', '.3', GCData())
assert clmm.GalaxyCluster('1', 161, 55, 1, GCData())
def test_make_radial_profiles():
# Set up a cluster object and compute cross and tangential shears
ra_lens, dec_lens, z_lens = 120., 42., 0.5
ra_source = np.array([120.1, 119.9, 119.9])
dec_source = np.array([41.9, 42.2, 42.2])
id_source = np.array([1, 2, 3])
shear1 = np.array([0.2, 0.4, 0.4])
shear2 = np.array([0.3, 0.5, 0.5])
z_sources = np.ones(3)
angsep_units, bin_units = 'radians', 'radians'
# Set up radial values
bins_radians = np.array([0.002, 0.003, 0.004])
expected_radius = [0.0021745039090962414, 0.0037238407383072053]
#######################################
### Use without cluster object ########
#######################################
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2)
# Tests passing int as bins arg makes the correct bins
bins = 2
vec_bins = clmm.utils.make_bins(np.min(angsep), np.max(angsep), bins)
testing.assert_array_equal(da.make_radial_profile([tshear, xshear, z_sources], angsep, angsep_units, bin_units, bins=bins)[0],
da.make_radial_profile([tshear, xshear, z_sources], angsep, angsep_units, bin_units, bins=vec_bins)[0])
# Test the outputs of compute_tangential_and_cross_components just to be safe
expected_angsep = np.array([0.0021745039090962414, 0.0037238407383072053, 0.0037238407383072053])
expected_cross_shear = np.array([0.2780316984090899, 0.6398792901134982, 0.6398792901134982])
expected_tan_shear = np.array([-0.22956126563459447, -0.02354769805831558, -0.02354769805831558])
testing.assert_allclose(angsep, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct when testing shear profiles")
testing.assert_allclose(tshear, expected_tan_shear, **TOLERANCE,
err_msg="Tangential Shear not correct when testing shear profiles")
testing.assert_allclose(xshear, expected_cross_shear, **TOLERANCE,
err_msg="Cross Shear not correct when testing shear profiles")
# Test default behavior, remember that include_empty_bins=False excludes all bins with N>=1
profile = da.make_radial_profile([tshear, xshear, z_sources], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=False)
_test_profile_table_output(profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected_tan_shear[1], expected_cross_shear[1], [2])
# Test metadata
testing.assert_array_equal(profile.meta['bin_units'], bin_units)
testing.assert_array_equal(profile.meta['cosmo'], None)
# Test simple unit convesion
profile = da.make_radial_profile([tshear, xshear, z_sources], angsep*180./np.pi, 'degrees', bin_units,
bins=bins_radians, include_empty_bins=False)
_test_profile_table_output(profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected_tan_shear[1], expected_cross_shear[1], [2])
# including empty bins
profile = da.make_radial_profile([tshear, xshear, z_sources], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=True)
_test_profile_table_output(profile, bins_radians[:-1], expected_radius, bins_radians[1:],
expected_tan_shear[:-1], expected_cross_shear[:-1], [1,2])
# test with return_binnumber
profile, binnumber = da.make_radial_profile([tshear, xshear, z_sources], angsep, angsep_units, bin_units,
bins=bins_radians, include_empty_bins=True, return_binnumber=True)
_test_profile_table_output(profile, bins_radians[:-1], expected_radius, bins_radians[1:],
expected_tan_shear[:-1], expected_cross_shear[:-1], [1,2])
testing.assert_array_equal(binnumber, [1, 2, 2])
###################################
### Test with cluster object ######
###################################
# Test error of missing redshift
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source,
shear1, shear2],
names=('ra', 'dec', 'e1', 'e2')))
cluster.compute_tangential_and_cross_components()
testing.assert_raises(TypeError, cluster.make_radial_profile, bin_units)
# Test error of missing shear
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source,
shear1, shear2, z_sources, id_source],
names=('ra', 'dec', 'e1', 'e2', 'z', 'id')))
testing.assert_raises(TypeError, cluster.make_radial_profile, bin_units)
# Test default behavior, remember that include_empty_bins=False excludes all bins with N>=1
cluster.compute_tangential_and_cross_components()
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=False)
_test_profile_table_output(cluster.profile, bins_radians[1], expected_radius[1], bins_radians[2],
expected_tan_shear[1], expected_cross_shear[1], [2],
p0='gt', p1='gx')
# including empty bins
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True, table_name='profile2')
_test_profile_table_output(cluster.profile2, bins_radians[:-1], expected_radius, bins_radians[1:],
expected_tan_shear[:-1], expected_cross_shear[:-1], [1,2],
p0='gt', p1='gx')
# Test with galaxy id's
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[:-1], expected_radius, bins_radians[1:],
expected_tan_shear[:-1], expected_cross_shear[:-1], [1,2], [[1],[2,3]],
p0='gt', p1='gx')
# Test it runs with galaxy id's and int bins
cluster.make_radial_profile(bin_units, bins=5, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
# And overwriting table
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[:-1], expected_radius, bins_radians[1:],
expected_tan_shear[:-1], expected_cross_shear[:-1], [1,2], [[1],[2,3]],
p0='gt', p1='gx')
# Test it runs with galaxy id's and int bins and no empty bins
cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=False,
gal_ids_in_bins=True, table_name='profile3')
_test_profile_table_output(cluster.profile3, bins_radians[1], expected_radius[1], bins_radians[2],
expected_tan_shear[1], expected_cross_shear[1], [2],
p0='gt', p1='gx')
# Test error with overwrite=False
testing.assert_raises(AttributeError, cluster.make_radial_profile, bin_units, bins=bins_radians,
include_empty_bins=True, gal_ids_in_bins=True, table_name='profile3', overwrite=False)
# Check error of missing id's
cluster_noid = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source,
shear1, shear2, z_sources],
names=('ra', 'dec', 'e1', 'e2', 'z')))
cluster_noid.compute_tangential_and_cross_components()
testing.assert_raises(TypeError, cluster_noid.make_radial_profile, bin_units, gal_ids_in_bins=True)
def test_compute_tangential_and_cross_components(modeling_data):
# Input values
ra_lens, dec_lens, z_lens = 120., 42., 0.5
ra_source = np.array([120.1, 119.9])
dec_source = np.array([41.9, 42.2])
z_source = np.array([1.,2.])
shear1 = np.array([0.2, 0.4])
shear2 = np.array([0.3, 0.5])
# Correct values
expected_angsep = np.array([0.0021745039090962414, 0.0037238407383072053])
expected_cross_shear = np.array([0.2780316984090899, 0.6398792901134982])
expected_tangential_shear = np.array([-0.22956126563459447, -0.02354769805831558])
# DeltaSigma expected values for clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
expected_cross_DS = np.array([1224.3326297393244, 1899.6061989365176])*0.7*1.0e12*1.0002565513832675
expected_tangential_DS = np.array([-1010.889584349285, -69.9059242788237])*0.7*1.0e12*1.0002565513832675
# test incosnsitent data
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source[0], dec_source=dec_source,
shear1=shear1, shear2=shear2)
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source[:1], dec_source=dec_source,
shear1=shear1, shear2=shear2)
# test not implemented geometry
testing.assert_raises(NotImplementedError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2, geometry='something crazy')
# Pass arrays directly into function
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2)
testing.assert_allclose(angsep, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct when passing lists")
testing.assert_allclose(tshear, expected_tangential_shear, **TOLERANCE,
err_msg="Tangential Shear not correct when passing lists")
testing.assert_allclose(xshear, expected_cross_shear, **TOLERANCE,
err_msg="Cross Shear not correct when passing lists")
# Pass LISTS into function
angsep, tshear, xshear = da.compute_tangential_and_cross_components(ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=list(ra_source), dec_source=list(dec_source),
shear1=list(shear1), shear2=list(shear2))
testing.assert_allclose(angsep, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct when passing lists")
testing.assert_allclose(tshear, expected_tangential_shear, **TOLERANCE,
err_msg="Tangential Shear not correct when passing lists")
testing.assert_allclose(xshear, expected_cross_shear, **TOLERANCE,
err_msg="Cross Shear not correct when passing lists")
# Use the cluster method
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source, shear1, shear2],
names=('ra', 'dec', 'e1', 'e2')))
# Test error with bad name/missing column
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components,
shape_component1='crazy name')
# Test output
angsep3, tshear3, xshear3 = cluster.compute_tangential_and_cross_components()
testing.assert_allclose(angsep3, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct when using cluster method")
testing.assert_allclose(tshear3, expected_tangential_shear, **TOLERANCE,
err_msg="Tangential Shear not correct when using cluster method")
testing.assert_allclose(xshear3, expected_cross_shear, **TOLERANCE,
err_msg="Cross Shear not correct when using cluster method")
# Check behaviour for the deltasigma option.
cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
# test missing info for is_deltasigma=True
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2, is_deltasigma=True, cosmo=None, z_lens=z_lens, z_source=z_source)
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2, is_deltasigma=True, cosmo=cosmo, z_lens=None, z_source=z_source)
testing.assert_raises(TypeError, da.compute_tangential_and_cross_components,
ra_lens=ra_lens, dec_lens=dec_lens, ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2, is_deltasigma=True, cosmo=cosmo, z_lens=z_lens, z_source=None)
# check values for DeltaSigma
angsep_DS, tDS, xDS = da.compute_tangential_and_cross_components(
ra_lens=ra_lens, dec_lens=dec_lens,
ra_source=ra_source, dec_source=dec_source,
shear1=shear1, shear2=shear2, is_deltasigma=True,
cosmo=cosmo, z_lens=z_lens, z_source=z_source)
testing.assert_allclose(angsep_DS, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct")
testing.assert_allclose(tDS, expected_tangential_DS, **TOLERANCE,
err_msg="Tangential Shear not correct")
testing.assert_allclose(xDS, expected_cross_DS, **TOLERANCE,
err_msg="Cross Shear not correct")
# Tests with the cluster object
# cluster object missing source redshift, and function call missing cosmology
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source, shear1, shear2],
names=('ra', 'dec', 'e1', 'e2')))
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components, is_deltasigma=True)
# cluster object OK but function call missing cosmology
cluster = clmm.GalaxyCluster(unique_id='blah', ra=ra_lens, dec=dec_lens, z=z_lens,
galcat=GCData([ra_source, dec_source, shear1, shear2, z_source],
names=('ra', 'dec', 'e1', 'e2','z')))
testing.assert_raises(TypeError, cluster.compute_tangential_and_cross_components, is_deltasigma=True)
# check values for DeltaSigma
angsep_DS, tDS, xDS = cluster.compute_tangential_and_cross_components(cosmo=cosmo, is_deltasigma=True)
testing.assert_allclose(angsep_DS, expected_angsep, **TOLERANCE,
err_msg="Angular Separation not correct when using cluster method")
testing.assert_allclose(tDS, expected_tangential_DS, **TOLERANCE,
err_msg="Tangential Shear not correct when using cluster method")
testing.assert_allclose(xDS, expected_cross_DS, **TOLERANCE,
err_msg="Cross Shear not correct when using cluster method")
def test_init():
gcdata = GCData()
assert_equal(None, gcdata.meta['cosmo'])
def test_update_cosmo():
# Define inputs
cosmo1 = Cosmology(H0=70.0, Omega_dm0=0.3 - 0.045, Omega_b0=0.045)
desc1 = cosmo1.get_desc()
gcdata = GCData()
# check it has __str__ adn __repr__
gcdata.__str__()
gcdata.__repr__()
# manual update
gcdata.update_cosmo_ext_valid(gcdata, cosmo1, overwrite=False)
assert_equal(desc1, gcdata.meta['cosmo'])
# check that adding cosmo metadata manually is forbidden
assert_raises(ValueError, gcdata.meta.__setitem__, 'cosmo', None)
assert_raises(ValueError, gcdata.meta.__setitem__, 'cosmo', cosmo1)
# update_cosmo funcs
# input_cosmo=None, data_cosmo=None
gcdata = GCData()
gcdata.update_cosmo_ext_valid(gcdata, None, overwrite=False)
assert_equal(None, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo_ext_valid(gcdata, None, overwrite=True)
assert_equal(None, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(None, overwrite=False)
assert_equal(None, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(None, overwrite=True)
assert_equal(None, gcdata.meta['cosmo'])
# input_cosmo!=None, data_cosmo=None
gcdata = GCData()
gcdata.update_cosmo_ext_valid(gcdata, cosmo1, overwrite=True)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1, overwrite=False)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1, overwrite=True)
assert_equal(desc1, gcdata.meta['cosmo'])
# input_cosmo=data_cosmo!=None
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo_ext_valid(gcdata, cosmo1, overwrite=False)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo_ext_valid(gcdata, cosmo1, overwrite=True)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo(cosmo1, overwrite=False)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo(cosmo1, overwrite=True)
assert_equal(desc1, gcdata.meta['cosmo'])
# input_cosmo(!=None) != data_cosmo(!=None)
cosmo2 = Cosmology(H0=60.0, Omega_dm0=0.3 - 0.045, Omega_b0=0.045)
desc2 = cosmo2.get_desc()
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
assert_raises(TypeError,
gcdata.update_cosmo_ext_valid,
gcdata,
cosmo2,
overwrite=False)
assert_raises(TypeError, gcdata.update_cosmo_ext_valid, gcdata, cosmo2)
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo_ext_valid(gcdata, cosmo2, overwrite=True)
assert_equal(desc2, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
gcdata.update_cosmo(cosmo1, overwrite=False)
assert_equal(desc1, gcdata.meta['cosmo'])
gcdata = GCData()
gcdata.update_cosmo(cosmo1)
assert_raises(TypeError, gcdata.update_cosmo, cosmo2, overwrite=False)
assert_raises(TypeError, gcdata.update_cosmo, cosmo2)
def _draw_source_redshifts(zsrc, zsrc_min, zsrc_max, ngals):
"""Set source galaxy redshifts either set to a fixed value or draw from a predefined
distribution. Return a table (GCData) of the source galaxies
Uses a sampling technique found in Numerical Recipes in C, Chap 7.2: Transformation Method.
Pulling out random values from a given probability distribution.
Parameters
----------
ngals : float
Number of galaxies to generate
zsrc : float or str
Choose the source galaxy distribution to be fixed or drawn from a predefined distribution.
float : All sources galaxies at this fixed redshift
str : Draws individual source gal redshifts from predefined distribution. Options
are: chang13
zsrc_min : float
The minimum source redshift allowed.
zsrc_max : float, optional
If source redshifts are drawn, the maximum source redshift
Returns
-------
galaxy_catalog : clmm.GCData
Table of true and 'measured' photometric redshifts, which here the same. Redshift photometric errors
are then added using _compute_photoz_pdfs.
Notes
-----
Much of this code in this function was adapted from the Dallas group
"""
# Set zsrc to constant value
if isinstance(zsrc, float):
zsrc_list = np.ones(ngals) * zsrc
# Draw zsrc from Chang et al. 2013
elif zsrc == 'chang13':
def integrated_pzfxn(zmax, func):
"""Integrated redshift distribution function for transformation method"""
val, _ = integrate.quad(func, zsrc_min, zmax)
return val
vectorization_integrated_pzfxn = np.vectorize(integrated_pzfxn)
zsrc_domain = np.arange(zsrc_min, zsrc_max, 0.001)
# Cumulative probability function of the redshift distribution
probdist = vectorization_integrated_pzfxn(zsrc_domain,
_chang_z_distrib)
uniform_deviate = np.random.uniform(probdist.min(), probdist.max(),
ngals)
zsrc_list = interp1d(probdist, zsrc_domain,
kind='linear')(uniform_deviate)
# Draw zsrc from a uniform distribution between zmin and zmax
elif zsrc == 'uniform':
zsrc_list = np.random.uniform(zsrc_min, zsrc_max, ngals)
# Invalid entry
else:
raise ValueError(
"zsrc must be a float or chang13. You set: {}".format(zsrc))
return GCData([zsrc_list, zsrc_list], names=('ztrue', 'z'))