Improved compatibility with py3.9

byron/classes/evaluator.py

@@ -33,7 +33,7 @@
     'ScriptEvaluator',
 ]
 
-from typing import Callable, Sequence
+from typing import Callable, Sequence, Optional
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from itertools import zip_longest
@@ -444,7 +444,7 @@ class ScriptEvaluator(EvaluatorABC):
     def __init__(
         self,
         script_name: str,
-        args: Sequence[str] | None = None,
+        args: Optional[Sequence[str]] = None,
         *,
         filename_format: str = 'phenotype_{i:x}.txt',
         timeout: int | None = 60,

byron/classes/individual.py

@@ -110,8 +110,8 @@ class Individual(Paranoid):
     __COUNTER: int = 0
 
     _genome: nx.classes.MultiDiGraph
-    _fitness: FitnessABC | None
-    _lineage: Lineage | None
+    _fitness: Optional[FitnessABC]
+    _lineage: Optional[Lineage]
     _age: Age
     _str: str
 
@@ -365,7 +365,7 @@ def run_paranoia_checks(self) -> bool:
 
         # ==[check nodes (semantic)]=========================================
         assert all(
-            n < self._genome.graph['node_count'] for n in self._genome
+            n < self._genome.graph["node_count"] for n in self._genome
         ), f"{PARANOIA_VALUE_ERROR}: Invalid 'node_count' attribute ({self._genome.graph['node_count']})"
 
         assert all(
@@ -402,12 +402,6 @@ def run_paranoia_checks(self) -> bool:
 
         return True
 
-    def consolidate_genome(self) -> None:
-        self._genome = nx.convert_node_labels_to_integers(self._genome)
-        fasten_subtree_parameters(NodeReference(self._genome, NODE_ZERO))
-        self._genome.graph['node_count'] = len(self._genome)
-        self.run_paranoia_checks()
-
     def describe(
         self,
         *,

byron/classes/parameter.py

@@ -28,7 +28,7 @@
 __all__ = ['ParameterABC', 'ParameterNumericABC', 'ParameterArrayABC', 'ParameterStructuralABC', 'ParameterSharedABC']
 
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Optional
 
 from networkx.classes import MultiDiGraph
 
@@ -101,7 +101,7 @@ class ParameterStructuralABC(ParameterABC):
 
     __slots__ = []  # Preventing the automatic creation of __dict__
 
-    _node_reference: NodeReference | None
+    _node_reference: Optional[NodeReference]
 
     def __init__(self):
         super().__init__()

byron/ea/parametric.py

@@ -27,6 +27,8 @@
 
 __all__ = ["parametric_ea"]
 
+from typing import Optional
+
 from byron.operators import *
 from byron.sys import *
 from byron.classes.selement import *
@@ -52,7 +54,7 @@ def parametric_ea(
     mu: int = 10,
     lambda_: int = 20,
     max_generation: int = 100,
-    max_fitness: FitnessABC | None = None,
+    max_fitness: Optional[FitnessABC] = None,
     top_best: int = None,
     lifespan: int = None,
     operators: list = None,

byron/ea/vanilla.py

@@ -27,7 +27,7 @@
 
 __all__ = ["vanilla_ea"]
 
-from typing import Any
+from typing import Any, Optional
 from time import perf_counter_ns, process_time_ns
 from datetime import timedelta
 
@@ -73,7 +73,7 @@ def vanilla_ea(
     mu: int = 10,
     lambda_: int = 20,
     max_generation: int = 100,
-    max_fitness: Any | None = None,
+    max_fitness: Optional = None,
     population_extra_parameters: dict = None,
 ) -> Population:
     r"""A simple evolutionary algorithm

byron/registry.py

@@ -29,7 +29,7 @@
 
 __all__ = ["Statistics", "fitness_function", "genetic_operator", "get_byron_type"]
 
-from typing import Callable
+from typing import Callable, Optional
 from dataclasses import dataclass
 
 from functools import wraps
@@ -99,7 +99,7 @@ def __str__(self):
 
 
 def fitness_function(
-    func: Callable[..., FitnessABC] | None = None, /, *, type_: type[FitnessABC] = None, backend: str | None = 'list'
+    func: Optional[Callable[..., FitnessABC]] = None, /, *, type_: type[FitnessABC] = None, backend: str | None = 'list'
 ):
     if type_ is None:
         type_ = lambda f: fitness.make_fitness(f)