refactor!: depend on and import autonerves (renamed from autoconf)

AGENTS.md

@@ -10,9 +10,9 @@ model composition, non-linear search, and Bayesian inference: `af.Model` /
 `af.Collection`, the `af.Analysis` interface, MCMC / nested-sampling / MLE
 searches, samples, aggregator, and a SQLAlchemy results database.
 
-Dependency direction: autofit depends on **autoconf** only. It does **not**
+Dependency direction: autofit depends on **autonerves** only. It does **not**
 import `autoarray`, `autogalaxy`, or `autolens` — never add such an import.
-Shared utilities (e.g. `test_mode`, `jax_wrapper`) belong in autoconf.
+Shared utilities (e.g. `test_mode`, `jax_wrapper`) belong in autonerves.
 
 ## Related repos
 
@@ -47,7 +47,7 @@ is no black/ruff/flake8 gate — formatting is advisory. (`requires-python` in
 
 ## Configuration & defaults
 
-autoconf supplies the packaged defaults under `autofit/config/`. Workspaces
+autonerves supplies the packaged defaults under `autofit/config/`. Workspaces
 override them via their own `config/` directory; the test suite pushes a local
 config dir via `conf.instance.push(...)` in `test_autofit/conftest.py`. When a
 change adds a new config key, mirror it into the packaged defaults so
@@ -70,7 +70,7 @@ nautilus; MLE: LBFGS/BFGS/drawer), `mapper/` (model + priors),
 
 ## Key rules / footguns
 
-- Import direction: autoconf only — never `autoarray` / `autogalaxy` /
+- Import direction: autonerves only — never `autoarray` / `autogalaxy` /
   `autolens`.
 - **The EP seam rule**: `autofit/graphical` is two layers — the inner
   factor-graph/message engine and the `declarative/` user layer. A new

autofit/__init__.py

@@ -1,5 +1,5 @@
-from autoconf import jax_wrapper
-from autoconf.dictable import register_parser
+from autonerves import jax_wrapper
+from autonerves.dictable import register_parser
 from . import conf
 
 conf.instance.register(__file__)
@@ -146,34 +146,34 @@ def save_abc(pickler, obj):
 
 __version__ = "2026.7.9.1"
 
-from autoconf import check_version
+from autonerves import check_version
 
 check_version(__version__)
 
 # ---------------------------------------------------------------------------
-# Public re-export of the autoconf configuration / serialization surface.
+# Public re-export of the autonerves configuration / serialization surface.
 #
 # Workspaces, tutorials and downstream code import these names from the science
 # library (e.g. ``from autolens import conf``) rather than depending on the
-# ``autoconf`` package directly, so the underlying configuration / serialization
+# ``autonerves`` package directly, so the underlying configuration / serialization
 # layer stays an implementation detail of the library.
 # ---------------------------------------------------------------------------
 # ``conf`` is already exported above (``from . import conf``); the names below
 # complete the surface.
-from autoconf import jax_wrapper
-from autoconf import fitsable
-from autoconf import setup_colab
-from autoconf import setup_notebook
-from autoconf.conf import with_config
-from autoconf.dictable import from_dict, from_json, to_dict, output_to_json
-from autoconf.fitsable import (
+from autonerves import jax_wrapper
+from autonerves import fitsable
+from autonerves import setup_colab
+from autonerves import setup_notebook
+from autonerves.conf import with_config
+from autonerves.dictable import from_dict, from_json, to_dict, output_to_json
+from autonerves.fitsable import (
     output_to_fits,
     hdu_list_for_output_from,
     ndarray_via_fits_from,
     ndarray_via_hdu_from,
     header_obj_from,
 )
-from autoconf.test_mode import (
+from autonerves.test_mode import (
     with_test_mode_segment,
     skip_visualization,
     skip_fit_output,

autofit/aggregator/file_output.py

@@ -7,7 +7,7 @@
 
 import dill
 
-from autoconf.dictable import from_dict
+from autonerves.dictable import from_dict
 
 
 class FileOutput(ABC):

autofit/aggregator/search_output.py

@@ -9,8 +9,8 @@
 import dill
 from PIL import Image
 
-from autoconf import cached_property
-from autoconf.class_path import get_class
+from autonerves import cached_property
+from autonerves.class_path import get_class
 from autofit.non_linear.samples import Samples
 
 from autofit.non_linear.samples.pdf import SamplesPDF
@@ -20,7 +20,7 @@
 )
 from autofit.mapper.identifier import Identifier
 from autofit.non_linear.samples.sample import samples_from_iterator
-from autoconf.dictable import from_dict
+from autonerves.dictable import from_dict
 from autofit.non_linear.samples.summary import SamplesSummary
 from autofit.non_linear.samples.util import simple_model_for_kwargs
 from . import fit_interface
@@ -164,7 +164,7 @@ def value(self, name: str):
         This may be a pickle, json, csv or fits file.
 
         If the JSON has a specified type it is parsed as that type. See dictable.py
-        in autoconf.
+        in autonerves.
 
         Returns None if the file does not exist.
 

autofit/conf.py

@@ -1 +1 @@
-from autoconf.conf import *
+from autonerves.conf import *

autofit/database/__init__.py

@@ -3,7 +3,7 @@
 
 from .sqlalchemy_ import sa
 
-from autoconf import conf
+from autonerves import conf
 from .aggregator import *
 from .migration.steps import migrator
 from .model import *

autofit/database/model/array.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from autoconf.class_path import get_class_path, get_class
+from autonerves.class_path import get_class_path, get_class
 from .model import Object
 from ..sqlalchemy_ import sa
 

autofit/database/model/fit.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from autoconf.dictable import from_dict
+from autonerves.dictable import from_dict
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.samples import Samples
 from .model import Base, Object

autofit/database/model/model.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from autoconf.class_path import get_class, get_class_path
+from autonerves.class_path import get_class, get_class_path
 from ..sqlalchemy_ import sa
 from sqlalchemy.orm import declarative_base
 

autofit/exc.py

@@ -1,4 +1,4 @@
-from autoconf.exc import PriorException
+from autonerves.exc import PriorException
 
 
 class MessageException(PriorException):

autofit/graphical/expectation_propagation/ep_mean_field.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.factor_graphs.factor import Factor
 from autofit.graphical.factor_graphs.graph import FactorGraph
 from autofit.graphical.mean_field import MeanField, FactorApproximation

autofit/graphical/factor_graphs/abstract.py

@@ -13,7 +13,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.utils import (
     FlattenArrays,
     nested_filter,

autofit/graphical/factor_graphs/graph.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.factor_graphs.abstract import FactorValue, AbstractNode
 from autofit.graphical.factor_graphs.factor import Factor
 from autofit.graphical.factor_graphs.jacobians import VectorJacobianProduct

autofit/graphical/factor_graphs/jacobians.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.utils import (
     nested_filter,
     nested_update,

autofit/graphical/factor_graphs/transform.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.factor_graphs.abstract import AbstractNode, Value, FactorValue
 # from ...mapper.operator import
 from autofit.mapper.operator import (

autofit/graphical/laplace/line_search.py

@@ -12,7 +12,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.graphical.factor_graphs.abstract import (
     FactorValue,
     FactorInterface,

autofit/graphical/mean_field.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit import exc
 from autofit.graphical.factor_graphs.abstract import AbstractNode
 from autofit.graphical.factor_graphs.factor import Factor

autofit/jax/pytrees.py

@@ -2,12 +2,12 @@
 
 The classes themselves already carry the necessary ``tree_flatten`` /
 ``tree_unflatten`` methods. This module simply registers them with
-``jax.tree_util`` on demand, via the lazy autoconf wrapper, so callers can
+``jax.tree_util`` on demand, via the lazy autonerves wrapper, so callers can
 pass ``Model`` / ``Collection`` / ``ModelInstance`` / prior instances
 through ``jax.jit`` and ``jax.vmap`` directly.
 """
 
-from autoconf.jax_wrapper import register_pytree_node, register_pytree_node_class
+from autonerves.jax_wrapper import register_pytree_node, register_pytree_node_class
 
 _ENABLED = False
 _REGISTERED_INSTANCE_CLASSES: set = set()

autofit/mapper/identifier.py

@@ -3,7 +3,7 @@
 from hashlib import md5
 from typing import Optional
 
-from autoconf.class_path import get_class_path
+from autonerves.class_path import get_class_path
 
 # floats are rounded to this increment so floating point errors
 # have no impact on identifier value

autofit/mapper/model.py

@@ -95,7 +95,7 @@ def _cached_property_names(cls) -> frozenset:
         downstream JAX pytree flattening. See PyAutoFit#1300 for the
         diagnosed leak this defends against.
         """
-        from autoconf.tools.decorators import cached_property_names
+        from autonerves.tools.decorators import cached_property_names
 
         return cached_property_names(cls)
 

autofit/mapper/model_object.py

@@ -3,8 +3,8 @@
 from typing import Type, Union, Tuple, Optional, Dict
 import logging
 
-from autoconf.class_path import get_class
-from autoconf.dictable import from_dict, to_dict
+from autonerves.class_path import get_class
+from autonerves.dictable import from_dict, to_dict
 from .identifier import Identifier
 
 logger = logging.getLogger(__name__)

autofit/mapper/operator.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 
-from autoconf import cached_property
+from autonerves import cached_property
 
 
 class LinearOperator(ABC):

autofit/mapper/prior/abstract.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from autoconf import conf
+from autonerves import conf
 
 from autofit.mapper.prior.arithmetic import ArithmeticMixin
 from autofit.mapper.prior.constant import Constant

autofit/mapper/prior/width_modifier.py

@@ -3,8 +3,8 @@
 import sys
 from abc import ABC, abstractmethod
 
-from autoconf import conf
-from autoconf.exc import ConfigException
+from autonerves import conf
+from autonerves.exc import ConfigException
 
 logger = logging.getLogger(__name__)
 

autofit/mapper/prior_model/abstract.py

@@ -9,8 +9,8 @@
 
 import numpy as np
 
-from autoconf import conf
-from autoconf.exc import ConfigException
+from autonerves import conf
+from autonerves.exc import ConfigException
 from autofit import exc
 from autofit.mapper import model
 from autofit.mapper.model import AbstractModel, frozen_cache

autofit/mapper/prior_model/array.py

@@ -1,6 +1,6 @@
 from typing import Tuple, Dict, Optional, Union
 
-from autoconf.dictable import from_dict
+from autonerves.dictable import from_dict
 from .abstract import AbstractPriorModel
 from autofit.mapper.prior.abstract import Prior
 import numpy as np

autofit/mapper/prior_model/prior_model.py

@@ -6,8 +6,8 @@
 import typing
 from typing import *
 
-from autoconf.class_path import get_class_path
-from autoconf.exc import ConfigException
+from autonerves.class_path import get_class_path
+from autonerves.exc import ConfigException
 from autofit.mapper.model import assert_not_frozen
 from autofit.mapper.model_object import ModelObject
 from autofit.mapper.prior.abstract import Prior

autofit/mapper/variable.py

@@ -6,7 +6,7 @@
 from math import sqrt
 
 import numpy as np
-from autoconf import cached_property
+from autonerves import cached_property
 
 from autofit.mapper.model_object import ModelObject
 

autofit/mapper/variable_operator.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 
 from autofit.mapper.operator import (
     LinearOperator,

autofit/messages/abstract.py

@@ -11,7 +11,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from ..mapper.variable import Variable
 
 from .interface import MessageInterface

autofit/messages/beta.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from ..messages.abstract import AbstractMessage
 
 

autofit/messages/fixed.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.messages.abstract import AbstractMessage
 
 

autofit/messages/gamma.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 from autofit.messages.abstract import AbstractMessage
 from autofit.messages.utils import invpsilog
 

autofit/messages/interface.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from autoconf import cached_property
+from autonerves import cached_property
 
 
 class MessageInterface(ABC):