def test_MCMCSamples_importance_sample():
np.random.seed(3)
mc0 = MCMCSamples(root='./tests/example_data/gd')
with pytest.raises(NotImplementedError):
mc0.importance_sample(mc0.logL, action='spam')
# new gaussian logL
logL_i = norm.logpdf(mc0.x3, loc=0.4, scale=0.1)
# add logL
mc1 = mc0.importance_sample(np.zeros_like(mc0.logL), action='add')
assert_array_equal(mc0.logL, mc1.logL)
assert_array_equal(mc0.weights, mc1.weights)
mc1 = mc0.importance_sample(logL_new=logL_i)
assert np.all(mc1.logL.to_numpy() != mc0.logL.to_numpy())
assert not np.all(mc1.weights == mc0.weights)
# replace logL
mc2 = mc0.importance_sample(mc0.logL, action='replace')
assert_array_equal(mc0.logL, mc2.logL)
assert_array_equal(mc0.weights, mc2.weights)
mc2 = mc0.importance_sample(mc0.logL.to_numpy()+logL_i, action='replace')
assert np.all(mc2.logL.to_numpy() != mc0.logL.to_numpy())
assert not np.all(mc2.weights == mc0.weights)
assert_array_equal(mc1.logL.to_numpy(), mc2.logL.to_numpy())
assert_array_almost_equal(mc1.logL.to_numpy(), mc2.logL.to_numpy())
# mask logL
mask = ((mc0.x0 > -0.3) & (mc0.x2 > 0.2) & (mc0.x4 < 3.5)).to_numpy()
mc_masked = mc0[mask]
mc3 = mc0.importance_sample(mask, action='mask')
assert_array_equal(mc_masked.logL, mc3.logL)
assert_array_equal(mc_masked.weights, mc3.weights)
assert np.all(mc3.x0 > -0.3)
for mc in [mc1, mc2, mc3]:
assert mc.tex == mc0.tex
assert mc.limits == mc0.limits
assert mc.root == mc0.root
assert mc.label == mc0.label
assert mc._metadata == mc0._metadata
assert mc is not mc0
assert mc.tex is not mc0.tex
assert mc.limits is not mc0.limits
mc0.importance_sample(mask, action='mask', inplace=True)
assert type(mc0) is MCMCSamples
assert_array_equal(mc3, mc0)
assert mc3.tex == mc0.tex
assert mc3.limits == mc0.limits
assert mc3.root == mc0.root
assert mc3.label == mc0.label
assert mc3._metadata == mc0._metadata
assert mc3 is not mc0
assert mc3.tex is not mc0.tex
assert mc3.limits is not mc0.limits
def test_MCMCSamples_importance_sample():
np.random.seed(3)
mc0 = MCMCSamples(root='./tests/example_data/gd')
with pytest.raises(NotImplementedError):
mc0.importance_sample(mc0.logL, action='spam')
mc_masked = mc0.importance_sample(mc0.logL, action='replace')
assert_array_equal(mc0.logL, mc_masked.logL)
assert_array_equal(mc0.weights, mc_masked.weights)
mc_masked = mc0.importance_sample(np.zeros_like(mc0.logL), action='add')
assert_array_equal(mc0.logL, mc_masked.logL)
assert_array_equal(mc0.weights, mc_masked.weights)
mask = ((mc0.x0 > -0.3) & (mc0.x2 > 0.2) & (mc0.x4 < 3.5)).to_numpy()
mc_masked = mc0[mask]
mc1 = mc0.importance_sample(mask, action='mask')
assert_array_equal(mc_masked.logL, mc1.logL)
assert_array_equal(mc_masked.weights, mc1.weights)
assert mc0.tex == mc1.tex
assert mc0.limits == mc1.limits
assert mc0.root == mc1.root
assert mc0.label == mc1.label
assert mc1._metadata == mc0._metadata
assert mc0 is not mc1
assert mc0.tex is not mc1.tex
assert mc0.limits is not mc1.limits
mc0.importance_sample(mask, action='mask', inplace=True)
assert type(mc0) is MCMCSamples
assert_array_equal(mc0, mc1)
assert mc0.tex == mc1.tex
assert mc0.limits == mc1.limits
assert mc0.root == mc1.root
assert mc0.label == mc1.label
assert mc1._metadata == mc0._metadata
assert mc0 is not mc1
assert mc0.tex is not mc1.tex
assert mc0.limits is not mc1.limits