fix: skip the JAX-only sparse-operator tests where jax is unavailable (#658)

unit_tests (3.9, PyAutoLens) and (3.10, PyAutoLens) have been red in
PyAutoHands/python_matrix with 5 failures, all:

  ModuleNotFoundError: No module named 'jax'
  .../inversion_interferometer_util.py:654: in from_nufft_precision_operator
      import jax.numpy as jnp

reached via Interferometer.apply_sparse_operator (dataset.py:280). The whole
sparse-operator subsystem is JAX-only by design — InterferometerSparseOperator
builds its FFT kernel with jax.numpy and projects with jax.ops.segment_sum /
jax.lax, and the imaging counterpart even types a field as 'jax.Array'. There
is no NumPy equivalent, and autonerves[jax] gates jax to Python >= 3.11.

So these are JAX-feature tests sitting in matrix legs that have no jax: a test
placement problem, not a library bug. Marking exactly the 5 cases that call
apply_sparse_operator() with a find_spec-based skipif, matching the
pytest.importorskip idiom already used in test_autolens/interop/test_coolest.py.

This also restores the standing 'library unit tests are numpy-only' rule for
these files: the dense-route cases stay NumPy-only and keep running on 3.9/3.10,
which is exactly what those legs exist to prove.

Verified both ways:
  jax present -> 9 passed, 0 skipped
  jax absent  -> 4 passed, 5 skipped (the 5 CI failures, and only those)

Co-authored-by: Jammy2211 <JNightingale2211@gmail.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>

Author: 3 people

test_autolens/potential_correction/test_fit_interferometer.py

@@ -1,3 +1,5 @@
+import importlib.util
+
 import numpy as np
 import pytest
 
@@ -7,6 +9,17 @@
     FitDpsiSrcInterferometer,
 )
 
+# `apply_sparse_operator` is a JAX-only code path: `InterferometerSparseOperator`
+# builds its FFT kernel with `jax.numpy` and projects with `jax.ops.segment_sum`
+# / `jax.lax`, with no NumPy equivalent. `autonerves[jax]` gates jax to
+# Python >= 3.11, so the sparse-route case cannot run on the 3.9/3.10 matrix
+# legs — skip it there rather than fail. The dense-route cases stay NumPy-only
+# and run everywhere, which is what those legs exist to prove.
+requires_jax = pytest.mark.skipif(
+    importlib.util.find_spec("jax") is None,
+    reason="apply_sparse_operator is a JAX-only path; jax requires Python >= 3.11",
+)
+
 
 def fit_from(dataset, use_sparse_operator):
     lens = al.Galaxy(
@@ -50,6 +63,7 @@ def test__dense_route__end_to_end_evidence_is_finite(interferometer_7):
     )
 
 
+@requires_jax
 def test__sparse_route__matches_dense_route(interferometer_7):
     dataset_sparse = interferometer_7.apply_sparse_operator()
 

test_autolens/potential_correction/test_iterative_interferometer.py

@@ -1,9 +1,22 @@
+import importlib.util
+
 import numpy as np
 import pytest
 
 import autoarray as aa
 import autolens as al
 
+# `apply_sparse_operator` is a JAX-only code path: `InterferometerSparseOperator`
+# builds its FFT kernel with `jax.numpy` and projects with `jax.ops.segment_sum`
+# / `jax.lax`, with no NumPy equivalent. `autonerves[jax]` gates jax to
+# Python >= 3.11, so the cases below cannot run on the 3.9/3.10 matrix legs —
+# skip them there rather than fail. The dense-route cases stay NumPy-only and
+# run everywhere, which is what those legs exist to prove.
+requires_jax = pytest.mark.skipif(
+    importlib.util.find_spec("jax") is None,
+    reason="apply_sparse_operator is a JAX-only path; jax requires Python >= 3.11",
+)
+
 
 def iter_fit_from(dataset, gauge_constraints=False, n_iter=2):
     lens = al.Galaxy(
@@ -33,6 +46,7 @@ def test__requires_sparse_operator(interferometer_7):
         iter_fit_from(interferometer_7)
 
 
+@requires_jax
 def test__solve_joint_optimization__finite_state_and_decreasing_cost(
     interferometer_7,
 ):
@@ -56,6 +70,7 @@ def test__solve_joint_optimization__finite_state_and_decreasing_cost(
     assert cost_opt < 0.5 * fit.data_weighted_norm
 
 
+@requires_jax
 def test__cost_identity_matches_direct_visibility_chi2(interferometer_7):
     """
     The normal-equation chi^2 identity (d^H C^-1 d - 2 x^T D + x^T F x) must
@@ -88,6 +103,7 @@ def test__cost_identity_matches_direct_visibility_chi2(interferometer_7):
     assert chi2_half == pytest.approx(chi2_direct, rel=1e-3)
 
 
+@requires_jax
 def test__gauge_constraints_are_satisfied(interferometer_7):
     dataset = interferometer_7.apply_sparse_operator()
     fit = iter_fit_from(dataset, gauge_constraints=True)
@@ -103,6 +119,7 @@ def test__gauge_constraints_are_satisfied(interferometer_7):
     assert G @ dpsi_opt == pytest.approx(np.zeros(3), abs=1.0e-6)
 
 
+@requires_jax
 def test__log_evidence__finite_at_optimum(interferometer_7):
     dataset = interferometer_7.apply_sparse_operator()
     fit = iter_fit_from(dataset)