refactor: separate PYAUTOFIT_TEST_MODE into distinct PYAUTO_* env vars

Split the catch-all PYAUTOFIT_TEST_MODE into purpose-specific variables:
- PYAUTO_TEST_MODE (sampler speedup, levels 0-3)
- PYAUTO_SKIP_FIT_OUTPUT (pre/post-fit I/O, VRAM, result text)
- PYAUTO_SKIP_VISUALIZATION (fit visualization and plotting)
- PYAUTO_SKIP_CHECKS (mesh validation, position resampling, weight thresholds)

Also renames:
- PYAUTOARRAY_OUTPUT_MODE -> PYAUTO_OUTPUT_MODE
- PYAUTO_WORKSPACE_SMALL_DATASETS -> PYAUTO_SMALL_DATASETS

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

Author: 2 people

CLAUDE.md

@@ -41,10 +41,10 @@ black autogalaxy/
 
 ### Plot Output Mode
 
-Set `PYAUTOARRAY_OUTPUT_MODE=1` to capture every figure produced by a script into numbered PNG files in `./output_mode/<script_name>/`. This is useful for visually inspecting all plots from an integration test without needing a display.
+Set `PYAUTO_OUTPUT_MODE=1` to capture every figure produced by a script into numbered PNG files in `./output_mode/<script_name>/`. This is useful for visually inspecting all plots from an integration test without needing a display.
 
 ```bash
-PYAUTOARRAY_OUTPUT_MODE=1 python scripts/my_script.py
+PYAUTO_OUTPUT_MODE=1 python scripts/my_script.py
 # -> ./output_mode/my_script/0_fit.png, 1_tracer.png, ...
 ```
 

autogalaxy/analysis/model_util.py

@@ -90,7 +90,7 @@ def mge_model_from(
 
     import os
 
-    if os.environ.get("PYAUTO_WORKSPACE_SMALL_DATASETS") == "1":
+    if os.environ.get("PYAUTO_SMALL_DATASETS") == "1":
         total_gaussians = 2
         gaussian_per_basis = 1
 

autogalaxy/quantity/model/visualizer.py

@@ -1,7 +1,7 @@
 import os
 
 import autofit as af
-from autoconf.test_mode import is_test_mode
+from autoconf.test_mode import skip_visualization
 
 from autogalaxy.quantity.model.plotter import PlotterQuantity
 
@@ -66,7 +66,7 @@ def visualize(
             via a non-linear search).
         """
 
-        if is_test_mode():
+        if skip_visualization():
             return
 
         fit = analysis.fit_quantity_for_instance(instance=instance)