refactor: separate PYAUTOFIT_TEST_MODE into distinct PYAUTO_* env vars

autofit/non_linear/analysis/analysis.py

@@ -328,9 +328,9 @@ def print_vram_use(self, model, batch_size : int) -> str:
         batch_size
             The batch size to profile, which is the number of model evaluations JAX will perform simultaneously.
         """
-        from autofit.non_linear.test_mode import test_mode_level
+        from autofit.non_linear.test_mode import skip_fit_output
 
-        if test_mode_level() >= 2:
+        if skip_fit_output():
             return
 
         if not self._use_jax:

autofit/non_linear/analysis/visualize.py

@@ -6,7 +6,7 @@
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.paths.database import DatabasePaths
 from autofit.non_linear.paths.null import NullPaths
-from autofit.non_linear.test_mode import is_test_mode
+from autofit.non_linear.test_mode import skip_visualization
 
 class Visualizer:
 
@@ -41,7 +41,7 @@ def should_visualize(
         A bool determining whether visualization should be performed or not.
         """
 
-        if is_test_mode():
+        if skip_visualization():
             return False
 
         if isinstance(paths, DatabasePaths) or isinstance(paths, NullPaths):

autofit/non_linear/fitness.py

@@ -456,8 +456,8 @@ def check_log_likelihood(self, fitness):
         """
         import numpy as np
 
-        from autofit.non_linear.test_mode import is_test_mode
-        if is_test_mode():
+        from autofit.non_linear.test_mode import skip_fit_output
+        if skip_fit_output():
             return
 
         if not conf.instance["general"]["test"]["check_likelihood_function"]:

autofit/non_linear/plot/plot_util.py

@@ -5,15 +5,15 @@
 
 import numpy as np
 
-from autofit.non_linear.test_mode import is_test_mode
+from autofit.non_linear.test_mode import skip_visualization
 
 logger = logging.getLogger(__name__)
 
 
 def skip_in_test_mode(func):
     @wraps(func)
     def wrapper(*args, **kwargs):
-        if is_test_mode():
+        if skip_visualization():
             return
         return func(*args, **kwargs)
 

autofit/non_linear/samples/samples.py

@@ -13,7 +13,7 @@
 from autoconf.class_path import get_class_path
 from autofit import exc
 from autofit.mapper.model import ModelInstance
-from autofit.non_linear.test_mode import is_test_mode
+from autofit.non_linear.test_mode import skip_checks
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.samples.sample import Sample
 
@@ -379,7 +379,7 @@ def samples_above_weight_threshold_from(
         if weight_threshold is None:
             weight_threshold = conf.instance["output"]["samples_weight_threshold"]
 
-        if is_test_mode():
+        if skip_checks():
             weight_threshold = None
 
         if weight_threshold is None:

autofit/non_linear/search/abstract_search.py

@@ -49,7 +49,7 @@
 from autofit.graphical.expectation_propagation import AbstractFactorOptimiser
 
 from autofit.non_linear.fitness import get_timeout_seconds
-from autofit.non_linear.test_mode import is_test_mode, test_mode_level
+from autofit.non_linear.test_mode import is_test_mode, test_mode_level, skip_fit_output
 
 logger = logging.getLogger(__name__)
 
@@ -497,15 +497,14 @@ class represented by model M and gives a score for their fitness.
         analysis = analysis.modify_before_fit(paths=self.paths, model=model)
         model.unfreeze()
 
-        mode = test_mode_level()
-        if mode < 2:
+        if not skip_fit_output():
             self.pre_fit_output(
                 analysis=analysis,
                 model=model,
                 info=info,
             )
         else:
-            # Bypass mode still needs the metadata + identifier files written
+            # Skip mode still needs the metadata + identifier files written
             # so downstream aggregator scraping can discover the search
             # directory. `save_all` is lightweight (a handful of JSON dumps)
             # and skips the expensive `analysis.save_attributes` /
@@ -527,7 +526,7 @@ class represented by model M and gives a score for their fitness.
                 model=model,
             )
 
-        if mode < 2:
+        if not skip_fit_output():
             analysis = analysis.modify_after_fit(
                 paths=self.paths, model=model, result=result
             )

autofit/non_linear/test_mode.py

@@ -1,3 +1,3 @@
-from autoconf.test_mode import test_mode_level, is_test_mode
+from autoconf.test_mode import test_mode_level, is_test_mode, skip_fit_output, skip_visualization, skip_checks
 
-__all__ = ["test_mode_level", "is_test_mode"]
+__all__ = ["test_mode_level", "is_test_mode", "skip_fit_output", "skip_visualization", "skip_checks"]

autofit/text/text_util.py

@@ -55,10 +55,10 @@ def result_info_from(samples) -> str:
     Output the full model.results file, which include the most-likely model, most-probable model at 1 and 3
     sigma confidence and information on the maximum log likelihood.
     """
-    from autofit.non_linear.test_mode import test_mode_level
+    from autofit.non_linear.test_mode import skip_fit_output
 
-    if test_mode_level() >= 2:
-        return "[test mode — result info skipped]"
+    if skip_fit_output():
+        return "[fit output skipped — PYAUTO_SKIP_FIT_OUTPUT=1]"
 
     results = []
 

test_autofit/non_linear/test_initializer.py

@@ -80,7 +80,7 @@ def test__priors__samples_from_model__raise_exception_if_all_likelihoods_identic
 
 
 def test__priors__samples_in_test_mode():
-    os.environ["PYAUTOFIT_TEST_MODE"] = "1"
+    os.environ["PYAUTO_TEST_MODE"] = "1"
 
     model = af.Model(af.m.MockClassx4)
     model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
@@ -121,7 +121,7 @@ def test__priors__samples_in_test_mode():
 
     assert figure_of_merit_list == [-1.0e99, -1.0e100]
 
-    os.environ["PYAUTOFIT_TEST_MODE"] = "0"
+    os.environ["PYAUTO_TEST_MODE"] = "0"
 
 
 def test__ball__samples_sample_centre_of_priors():