Fix JAX array leak from PointSolver.solve() into Grid2DIrregular

When the solver uses a JAX backend, the final boolean-indexed solution
was a JAX DeviceArray. Wrapping it in Grid2DIrregular without conversion
caused downstream np.array() calls in the visualizer to raise:
  ValueError: object __array__ method not producing an array

Fix: convert solution to numpy via np.asarray() before constructing the
return Grid2DIrregular. Safe because solve() is never called inside
jax.jit (variable-length boolean indexing prevents it).

Also updates the Returns docstring to document the numpy-backed guarantee.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: 2 people

CLAUDE.md

@@ -9,21 +9,31 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 pip install -e ".[dev]"
 ```
 
-### Run Tests
-```bash
-# All tests
-python -m pytest test_autolens/
+### Run Tests
+```bash
+# All tests
+python -m pytest test_autolens/
 
 # Single test file
 python -m pytest test_autolens/lens/test_tracer.py
 
-# With output
-python -m pytest test_autolens/imaging/test_fit_imaging.py -s
-```
-
-### Formatting
-```bash
-black autolens/
+# With output
+python -m pytest test_autolens/imaging/test_fit_imaging.py -s
+```
+
+### Codex / sandboxed runs
+
+When running Python from Codex or any restricted environment, set writable cache directories so `numba` and `matplotlib` do not fail on unwritable home or source-tree paths:
+
+```bash
+NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib python -m pytest test_autolens/
+```
+
+This workspace is often imported from `/mnt/c/...` and Codex may not be able to write to module `__pycache__` directories or `/home/jammy/.cache`, which can cause import-time `numba` caching failures without this override.
+
+### Formatting
+```bash
+black autolens/
 ```
 
 ## Architecture

autolens/point/solver/point_solver.py

@@ -19,6 +19,7 @@
 import logging
 from typing import Tuple, Optional
 
+import numpy as np
 import autoarray as aa
 from autoarray.structures.triangles.shape import Point
 
@@ -66,7 +67,8 @@ def solve(
 
         Returns
         -------
-        A list of image plane coordinates that are traced to the source plane coordinate.
+        A ``Grid2DIrregular`` of image-plane coordinates, always numpy-backed even when the
+        solver uses a JAX backend internally.
         """
         kept_triangles = super().solve_triangles(
             tracer=tracer,
@@ -90,4 +92,4 @@ def solve(
 
             solution = solution[~self._xp.isinf(solution).any(axis=1)]
 
-        return aa.Grid2DIrregular(solution)
+        return aa.Grid2DIrregular(np.asarray(solution))