fix: resample invalid profile parameters

autogalaxy/exc.py

@@ -13,6 +13,18 @@ class ProfileException(Exception):
     pass
 
 
+class ModelParameterException(ValueError, af.exc.FitException):
+    """
+    Raised when a concrete model parameter is outside its physical domain.
+
+    Direct profile construction remains a conventional ``ValueError`` for users,
+    while PyAutoFit can recognize the same failure as a ``FitException`` and reject
+    that candidate instead of terminating a non-linear search.
+    """
+
+    pass
+
+
 class GalaxyException(Exception):
     """
     Raises exceptions associated with the `galaxy` module and `Galaxy` class.

autogalaxy/profiles/validate.py

@@ -24,6 +24,7 @@
 import numpy as np
 
 from autoarray import validate
+from autogalaxy import exc
 
 
 def validate_scale_radius(scale_radius, name: str = "scale_radius"):
@@ -45,6 +46,7 @@ def validate_scale_radius(scale_radius, name: str = "scale_radius"):
     validate.validate_positive_finite(
         value=scale_radius,
         name=name,
+        exc_type=exc.ModelParameterException,
         extra=(
             "The scale radius is the angular radius at which the halo's log-slope "
             "changes, so it must be above zero. A value of 0.0 divides the grid by "
@@ -72,6 +74,7 @@ def validate_sersic_index(sersic_index, name: str = "sersic_index"):
     validate.validate_positive_finite(
         value=sersic_index,
         name=name,
+        exc_type=exc.ModelParameterException,
         extra=(
             "The Sersic index controls the concentration of the profile and appears "
             "as a divisor in its normalisation, so it must be above zero. A value of "
@@ -107,6 +110,7 @@ def validate_redshift(redshift, name: str = "redshift"):
     validate.validate_non_negative_finite(
         value=redshift,
         name=name,
+        exc_type=exc.ModelParameterException,
         extra=(
             "A redshift is a cosmological distance measure and cannot be negative — "
             "a negative value yields meaningless angular diameter distances in every "
@@ -152,7 +156,7 @@ def validate_ell_comps(ell_comps, name: str = "ell_comps"):
     magnitude_squared = ell_y * ell_y + ell_x * ell_x
 
     if not np.isfinite(magnitude_squared) or magnitude_squared >= 1.0:
-        raise ValueError(
+        raise exc.ModelParameterException(
             f"{name} must satisfy {name}[0]**2 + {name}[1]**2 < 1; got "
             f"{tuple(ell_comps)!r}, whose magnitude is "
             f"{np.sqrt(magnitude_squared) if np.isfinite(magnitude_squared) else magnitude_squared!r}. "

test_autogalaxy/profiles/test_validate.py

@@ -18,7 +18,9 @@
 import numpy as np
 import pytest
 
+import autofit as af
 import autogalaxy as ag
+from autofit.non_linear.fitness import Fitness
 from autogalaxy.profiles import validate
 
 
@@ -46,6 +48,50 @@ def __add__(self, other):
         return self
 
 
+@pytest.mark.parametrize(
+    "constructor",
+    [
+        lambda: ag.mp.NFW(scale_radius=0.0),
+        lambda: ag.lp.Sersic(sersic_index=0.0),
+        lambda: ag.lp.Sersic(ell_comps=(1.0, 1.0)),
+        lambda: ag.Galaxy(redshift=-1.0),
+    ],
+)
+def test__invalid_model_parameters_are_value_errors_and_fit_exceptions(constructor):
+    """
+    The same narrow error contract serves direct API calls and model fitting:
+    users see ``ValueError`` while PyAutoFit treats the candidate as resampleable.
+    """
+    with pytest.raises(ag.exc.ModelParameterException) as error:
+        constructor()
+
+    assert isinstance(error.value, ValueError)
+    assert isinstance(error.value, af.exc.FitException)
+
+
+def test__invalid_sampled_profile_is_returned_as_a_resample_figure_of_merit():
+    """Exercise the PyAutoFit boundary that non-linear searches call."""
+
+    class Model:
+        @staticmethod
+        def instance_from_vector(vector, xp):
+            return ag.lp.Sersic(sersic_index=vector[0])
+
+    class Analysis(af.Analysis):
+        @staticmethod
+        def log_likelihood_function(instance):
+            return 1.0
+
+    fitness = Fitness(
+        model=Model(),
+        analysis=Analysis(),
+        resample_figure_of_merit=-1.0e99,
+    )
+
+    assert fitness.call([0.0]) == fitness.resample_figure_of_merit
+    assert fitness.call([1.0]) == 1.0
+
+
 # ======================================================================================
 # B9 — scale_radius must be finite and positive
 # ======================================================================================