test_model Namespace Reference

Functions
	channels ()
	data (channels)
	psfmodels (data)
	interptype_sersic_default ()
	sources (channels, interptype_sersic_default)
	model (data, psfmodels, sources)
	test_data (channels, data)
	test_model (channels, model)
	test_model_eval_jacobian (model)
	test_model_eval_loglike_grad (model)
	test_model_hessian (model)

Variables
int	max_diff_ll = 1e-25

Function Documentation

channels()

test_model.channels

(

)

Definition at line 12 of file test_model.py.

def channels():
    return tuple(g2f.Channel(x) for x in "rgb")
 
 
@pytest.fixture(scope="module")

data()

test_model.data

(

channels

)

Definition at line 17 of file test_model.py.

def data(channels):
    n_x, n_y = 5, 5
    return g2f.DataD(
        [
            g2f.ObservationD(
                g2d.ImageD(np.zeros((n_y, n_x))),
                g2d.ImageD(np.full((n_y, n_x), 1e3)),
                g2d.ImageB(np.ones((n_y, n_x))),
                channel,
            )
            for channel in channels
        ]
    )
 
 
@pytest.fixture(scope="module")

interptype_sersic_default()

test_model.interptype_sersic_default

(

)

Definition at line 53 of file test_model.py.

def interptype_sersic_default():
    return g2f.SersicMixComponentIndexParameterD().interptype
 
 
@pytest.fixture(scope="module")

model()

test_model.model	(		data,
			psfmodels,
			sources )

Definition at line 130 of file test_model.py.

def model(data, psfmodels, sources):
    model = g2f.ModelD(data, list(psfmodels), list(sources))
    with pytest.raises(RuntimeError):
        model.evaluate()
    model.setup_evaluators()
    model.evaluate()
    result = model.evaluate()
    for value in result:
        assert value == 0
    for data, output in zip(model.data, model.outputs):
        data.image.data.flat = output.data.flat
    return model
 
 

psfmodels()

test_model.psfmodels

(

data

)

Definition at line 33 of file test_model.py.

def psfmodels(data):
    psfmodels = tuple(
        g2f.PsfModel(
            [
                g2f.GaussianComponent(
                    g2f.GaussianParametricEllipse(1.0, 1.0, 0.0),
                    None,
                    g2f.LinearIntegralModel([(g2f.Channel.NONE, g2f.IntegralParameterD(1.0))]),
                )
            ]
        )
        for _ in range(len(data))
    )
    for psfmodel in psfmodels:
        for param in psfmodel.parameters():
            param.fixed = True
    return psfmodels
 
 
@pytest.fixture(scope="module")

sources()

test_model.sources	(		channels,
			interptype_sersic_default )

Definition at line 58 of file test_model.py.

def sources(channels, interptype_sersic_default):
    last = None
    n_sources, n_components = 2, 2
    sources = [None] * n_sources
    n_comp_max = n_components - 1
    log10 = g2f.Log10TransformD()
    for i in range(n_sources):
        components = [None] * n_components
        for c in range(n_components):
            is_last = c == n_comp_max
            last = g2f.FractionalIntegralModel(
                [
                    (
                        channel,
                        g2f.ProperFractionParameterD(
                            (is_last == 1) or 0.25 * (1 + idx_channel), fixed=is_last
                        ),
                    )
                    for idx_channel, channel in enumerate(channels)
                ],
                g2f.LinearIntegralModel(
                    [(channel, g2f.IntegralParameterD(0.5 + 0.5 * (i + 1))) for channel in channels]
                )
                if (c == 0)
                else last,
                is_last,
            )
            components[c] = g2f.GaussianComponent(
                centroid=None,
                ellipse=g2f.GaussianParametricEllipse(1.0, 1.0, 0.0),
                integral=last,
            )
        components.append(
            g2f.SersicMixComponent(
                centroid=None,
                ellipse=g2f.SersicParametricEllipse(
                    size_x=g2f.ReffXParameterD(2.6, transform=log10, fixed=False),
                    size_y=g2f.ReffYParameterD(3.5, transform=log10, fixed=False),
                    rho=g2f.RhoParameterD(
                        -0.1,
                        fixed=False,
                        transform=g2f.LogitLimitedTransformD(
                            limits=g2f.LimitsD(min=-0.999, max=0.999, name="ref_logit_sersic[0.5, 6.0]"),
                        ),
                    ),
                ),
                integral=g2f.LinearIntegralModel(
                    [
                        (
                            channel,
                            g2f.IntegralParameterD(1.0, transform=log10, label=f"Sersic {channel}-band"),
                        )
                        for channel in channels
                    ]
                ),
                # Linear interpolation fails at exact knot values
                # Adding a small offset solves the problem
                sersicindex=g2f.SersicMixComponentIndexParameterD(
                    value=1.0 + 5e-4 * (interptype_sersic_default == g2f.InterpType.linear),
                    fixed=False,
                    transform=g2f.LogitLimitedTransformD(
                        limits=g2f.LimitsD(min=0.5, max=6.0, name="ref_logit_sersic[0.5, 6.0]"),
                    ),
                ),
            ),
        )
        sources[i] = g2f.Source(components)
 
    return sources
 
 
@pytest.fixture(scope="module")

test_data()

test_model.test_data	(		channels,
			data )

Definition at line 144 of file test_model.py.

def test_data(channels, data):
    assert len(data) == len(channels)
 
 

test_model()

test_model.test_model	(		channels,
			model )

Definition at line 148 of file test_model.py.

def test_model(channels, model):
    assert str(model) != ""
    gaussians = model.gaussians(channels[0])
    assert len(gaussians) == 12
    params = model.parameters()
    assert len(params) == 80
    assert len(set(params)) == 68
 
 

test_model_eval_jacobian()

test_model.test_model_eval_jacobian

(

model

)

Definition at line 157 of file test_model.py.

def test_model_eval_jacobian(model):
    # Force is needed in case other tests use a jacobian evaluator
    # with different parameters
    model.setup_evaluators(g2f.EvaluatorMode.jacobian, force=True)
    result = np.array(model.evaluate())
    # TODO: Investigate why clang/osx builds have non-zero values in DM-45308
    # GCC/linux are exactly 0. Hopefully it's not something like ffast-math
    assert (np.abs(result) <= max_diff_ll).all()
    # TODO: Investigate why this rtol needs to be so high
    errors = model.verify_jacobian(atol=1e-3, rtol=5e-3, max_diff_ll=max_diff_ll)
    # All of the IntegralParameters got double-counted - see DM-40674
    # ... but also, linear interpolators will fail the Sersic index params
    assert len(errors) == 6
 
 

test_model_eval_loglike_grad()

test_model.test_model_eval_loglike_grad

(

model

)

Definition at line 172 of file test_model.py.

def test_model_eval_loglike_grad(model):
    model.setup_evaluators(g2f.EvaluatorMode.loglike_grad, force=True)
    result = np.array(model.evaluate())
    # TODO: Investigate why clang/osx builds have non-zero values in DM-45308
    assert (np.abs(result) <= max_diff_ll).all()
    dloglike = np.array(model.compute_loglike_grad(True))
    assert (np.abs(dloglike) < max_diff_ll).all()
 
 

test_model_hessian()

test_model.test_model_hessian

(

model

)

Definition at line 181 of file test_model.py.

def test_model_hessian(model):
    options = g2f.HessianOptions(return_negative=False)
    params_fixed = set()
 
    for idx in range(2):
        hessians = {
            is_transformed: model.compute_hessian(transformed=is_transformed, options=options)
            for is_transformed in (False, True)
        }
        # Note that since there is no noise in the image and none of the
        # param values have changed, the Hessian terms are not all negative -
        # the sign corrections are based on the sign of loglike_grad,
        # and those are all exactly zero
        assert all(np.isfinite(hessian.data).all() for hessian in hessians.values())
        if idx == 0:
            for param in set(model.parameters()):
                # TODO: Remove this when DM-40674 is fixed
                if isinstance(param, g2f.IntegralParameterD) and param.free:
                    param.free = False
                    params_fixed.add(param)
 
    for is_transformed in (False, True):
        # can set print=True for debugging, but the output is too verbose for
        # pytest output even on a failure
        hessian_from_jac = model.compute_hessian(transformed=is_transformed, options=None).data
        # TODO: investigate these unreasonably large differences after DM-40674
        assert np.all(np.isclose(hessians[is_transformed].data, hessian_from_jac, rtol=1e-3, atol=1))
 
    for param in params_fixed:
        param.free = True

Variable Documentation

max_diff_ll

int test_model.max_diff_ll = 1e-25

Definition at line 8 of file test_model.py.