revert: re-define frozendicts as a utility module

boiling_learning/daq/channels.py

@@ -1,11 +1,11 @@
 import enum
 from typing import Dict, List, Optional, Type, TypeVar, Union
 
-from frozendict import frozendict
 from nidaqmx.constants import ChannelType as NIChannelType
 from nidaqmx.task import Task
 
 from boiling_learning.daq.devices import Device
+from boiling_learning.utils.frozendicts import frozendict
 
 T = TypeVar('T')
 

boiling_learning/data/samples.py

@@ -1,6 +1,5 @@
-from frozendict import frozendict
-
 from boiling_learning.utils import geometry
+from boiling_learning.utils.frozendicts import frozendict
 from boiling_learning.utils.units import unit_registry as ureg
 
 WIRE_SAMPLES = frozendict[int, geometry.Prism](

boiling_learning/io/json.py

@@ -10,10 +10,10 @@
 
 from classes import AssociatedType, Supports
 from classes import typeclass as _typeclass
-from frozendict import frozendict
 from typing_extensions import Protocol, TypedDict, runtime_checkable
 
 from boiling_learning.utils.dataclasses import is_dataclass_instance, shallow_asdict
+from boiling_learning.utils.frozendicts import frozendict
 from boiling_learning.utils.functional import P, Pack
 from boiling_learning.utils.pathutils import PathLike, resolve
 from boiling_learning.utils.table_dispatch import TableDispatcher

boiling_learning/preprocessing/visualize.py

@@ -16,7 +16,7 @@
 # from boiling_learning.preprocessing.experiment_video import ExperimentVideo
 # from boiling_learning.preprocessing.transformers import Transformer
 # from boiling_learning.preprocessing.video import VideoFrame
-# from frozendict import frozendict
+# from boiling_learning.utils.frozendicts import frozendict
 # from boiling_learning.utils.functional import P, Pack, nth_arg
 
 # _T = TypeVar('_T')

boiling_learning/scripts/set_condensation_datasets_data.py

@@ -4,13 +4,13 @@
 
 import parse
 import yaml
-from frozendict import frozendict
 from loguru import logger
 
 from boiling_learning.preprocessing.experiment_video import ExperimentVideo
 from boiling_learning.preprocessing.image_datasets import ImageDataset
 from boiling_learning.preprocessing.video import Video
 from boiling_learning.scripts.utils.setting_data import check_experiment_video_dataframe_indices
+from boiling_learning.utils.frozendicts import frozendict
 from boiling_learning.utils.pathutils import PathLike, resolve
 
 _SUBCASE_PATTERNS = frozendict(

boiling_learning/utils/descriptions.py

@@ -4,10 +4,10 @@
 from typing import Any, Dict, FrozenSet, Generic, List, Set, Tuple, Type, TypeVar, Union
 
 from classes import AssociatedType, Supports, typeclass
-from frozendict import frozendict
 from typing_extensions import Protocol, final, runtime_checkable
 
 from boiling_learning.utils.dataclasses import is_dataclass_instance, shallow_asdict
+from boiling_learning.utils.frozendicts import frozendict
 
 _AnyType = TypeVar('_AnyType', bound=Type[Any])
 _Description = TypeVar('_Description')

boiling_learning/utils/frozendicts.py

@@ -0,0 +1,20 @@
+import sys
+from typing import Generic, TypeVar
+
+from frozendict import frozendict as _frozendict
+
+_T = TypeVar('_T')
+_S = TypeVar('_S')
+
+
+__all__ = ('frozendict',)
+
+if sys.version_info >= (3, 9, 0):
+
+    class frozendict(_frozendict):
+        pass
+
+else:
+
+    class frozendict(_frozendict, Generic[_T, _S]):
+        pass

boiling_learning/utils/functional.py

@@ -17,9 +17,9 @@
 )
 
 import funcy
-from frozendict import frozendict
 
 from boiling_learning.utils.descriptions import describe
+from boiling_learning.utils.frozendicts import frozendict
 from boiling_learning.utils.sentinels import Sentinel
 
 # TODO: when variadic generics are available, they will be very useful here

tests/test_io_.py

@@ -1,9 +1,9 @@
 from typing import Any, Dict
 
 import pytest
-from frozendict import frozendict
 
 from boiling_learning.io import json
+from boiling_learning.utils.frozendicts import frozendict
 from boiling_learning.utils.functional import P