lambda transformation, holoatom ABC/polymorph

Author: Phovos

src/gg.py

@@ -0,0 +1,240 @@
+import ast
+import hashlib
+import json
+import logging
+import inspect
+from typing import Any, Callable, Dict, Generic, List, Type, TypeVar, Union
+from dataclasses import dataclass, field
+
+#------------------------------------------------------------------------------
+# Holoiconic-Atomic-logic
+#------------------------------------------------------------------------------
+"""
+This module demonstrates the concept of combining imperative and non-imperative programming paradigms using Python's AST transformations. It allows dynamic modification and execution of source code at runtime, inspired by functional programming and lambda calculus principles.
+"""
+
+@dataclass
+class GrammarRule:
+    """
+    Represents a single grammar rule in a context-free grammar.
+    
+    Attributes:
+        lhs (str): Left-hand side of the rule.
+        rhs (List[Union[str, 'GrammarRule']]): Right-hand side of the rule, which can be terminals or other rules.
+    """
+    lhs: str
+    rhs: List[Union[str, 'GrammarRule']]
+    
+    def __repr__(self):
+        """
+        Provide a string representation of the grammar rule.
+        
+        Returns:
+            str: The string representation.
+        """
+        rhs_str = ' '.join([str(elem) for elem in self.rhs])
+        return f"{self.lhs} -> {rhs_str}"
+
+T = TypeVar('T')
+V = TypeVar('V')
+C = TypeVar('C')
+
+class LambdaModifier(ast.NodeTransformer):
+    def __init__(self, operation: str):
+        self.operation = operation
+
+    def visit_Lambda(self, node: ast.Lambda) -> ast.Lambda:
+        if self.operation == "multiply":
+            node.body = ast.BinOp(left=node.body, op=ast.Mult(), right=ast.Constant(value=2))
+        elif self.operation == "subtract":
+            node.body = ast.BinOp(left=node.body, op=ast.Sub(), right=ast.Constant(value=1))
+        elif self.operation == "divide":
+            node.body = ast.BinOp(left=node.body, op=ast.Div(), right=ast.Constant(value=2))
+        # Add more operations as needed
+        return node
+
+def transform_lambda(source_code: str, operation: str) -> str:
+    tree = ast.parse(source_code, mode='eval')
+    modifier = LambdaModifier(operation)
+    modified_tree = modifier.visit(tree)
+    return ast.unparse(modified_tree)
+
+class Atom(Generic[T, V, C]):
+    """
+    Abstract Base Class for all Atom types.
+    
+    Atoms are the smallest units of data or executable code, and this interface
+    defines common operations such as encoding, decoding, execution, and conversion
+    to data classes.
+    
+    Attributes:
+        grammar_rules (List[GrammarRule]): List of grammar rules defining the syntax of the Atom.
+    """
+    __slots__ = ('_id', '_value', '_type', '_metadata', '_children', '_parent', 'hash', 'tag', 'children', 'metadata')
+    type: Union[str, str]
+    value: Union[T, V, C] = field(default=None)
+    grammar_rules: List[GrammarRule] = field(default_factory=list)
+    id: str = field(init=False)
+    case_base: Dict[str, Callable[..., bool]] = field(default_factory=dict)
+
+    def __init__(self, value: Union[T, V, C], type: Union[str, str]):
+        self._value = value
+        self._type = type
+        self._metadata = {}
+        self._children = []
+        self._parent = None
+        self.hash = hashlib.sha256(repr(self._value).encode()).hexdigest()
+        self.tag = ''
+        self.children = []
+        self.metadata = {}
+        self.__post_init__()
+
+    def __post_init__(self):
+        self.case_base = {
+            '⊤': lambda x, _: x,
+            '⊥': lambda _, y: y,
+            '¬': lambda a: not a,
+            '∧': lambda a, b: a and b,
+            '∨': lambda a, b: a or b,
+            '→': lambda a, b: (not a) or b,
+            '↔': lambda a, b: (a and b) or (not a and not b),
+        }
+
+    reflexivity: Callable[[T], bool] = lambda x: x == x
+    symmetry: Callable[[T, T], bool] = lambda x, y: x == y
+    transitivity: Callable[[T, T, T], bool] = lambda x, y, z: (x == y and y == z)
+    transparency: Callable[[Callable[..., T], T, T], T] = lambda f, x, y: f(True, x, y) if x == y else None
+
+    def process_attributes(self, mapping_description: Dict[str, Any], input_data: Dict[str, Any]) -> None:
+        """
+        Use the `mapper` function to process input data and map it to attributes.
+        
+        Args:
+            mapping_description (Dict[str, Any]): The mapping description for transformation.
+            input_data (Dict[str, Any]): Data to be processed and mapped.
+        """
+        mapped_data = self.mapper(mapping_description, input_data)
+        for key, value in mapped_data.items():
+            if hasattr(self, key):
+                setattr(self, key, value)
+
+    def mapper(self, mapping_description: Dict[str, Any], input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Example mapper function."""
+        # Implement the actual mapping logic here
+        return input_data
+
+    def encode(self) -> bytes:
+        return json.dumps({
+            'id': self.id,
+            'attributes': self.attributes
+        }).encode()
+
+    @classmethod
+    def decode(cls, data: bytes) -> 'Atom':
+        decoded_data = json.loads(data.decode())
+        return cls(id=decoded_data['id'], **decoded_data['attributes'])
+
+    def introspect(self) -> str:
+        """
+        Reflect on its own code structure via AST.
+        """
+        source = inspect.getsource(self.__class__)
+        return ast.dump(ast.parse(source))
+
+    def __repr__(self):
+        return f"{self.value} : {self.type}"
+
+    def __str__(self):
+        return str(self.value)
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, Atom) and self.hash == other.hash
+
+    def __hash__(self) -> int:
+        return int(self.hash, 16)
+
+    def __getitem__(self, key):
+        return self.value[key]
+
+    def __setitem__(self, key, value):
+        self.value[key] = value
+
+    def __delitem__(self, key):
+        del self.value[key]
+
+    def __len__(self):
+        return len(self.value)
+
+    def __iter__(self):
+        return iter(self.value)
+
+    def __contains__(self, item):
+        return item in self.value
+
+    def __call__(self, *args, **kwargs):
+        return self.value(*args, **kwargs)
+
+    def __bytes__(self) -> bytes:
+        return bytes(self.value)
+
+    @property
+    def memory_view(self) -> memoryview:
+        if isinstance(self.value, (bytes, bytearray)):
+            return memoryview(self.value)
+        raise TypeError("Unsupported type for memoryview")
+
+    def __buffer__(self, flags: int) -> memoryview: # Buffer protocol
+        return memoryview(self.value)
+
+    async def send_message(self, message: Any, ttl: int = 3) -> None:
+        if ttl <= 0:
+            logging.info(f"Message {message} dropped due to TTL")
+            return
+        logging.info(f"Atom {self.id} received message: {message}")
+        for sub in self.subscribers:
+            await sub.receive_message(message, ttl - 1)
+
+    async def receive_message(self, message: Any, ttl: int) -> None:
+        logging.info(f"Atom {self.id} processing received message: {message} with TTL {ttl}")
+        await self.send_message(message, ttl)
+
+    def subscribe(self, atom: 'Atom') -> None:
+        self.subscribers.add(atom)
+        logging.info(f"Atom {self.id} subscribed to {atom.id}")
+
+    def unsubscribe(self, atom: 'Atom') -> None:
+        self.subscribers.discard(atom)
+        logging.info(f"Atom {self.id} unsubscribed from {atom.id}")
+
+    def __add__(self, other):
+        return self.value + other
+
+    def __sub__(self, other):
+        return self.value - other
+
+    def __mul__(self, other):
+        return self.value * other
+
+    def __truediv__(self, other):
+        return self.value / other
+
+    def __floordiv__(self, other):
+        return self.value // other
+
+    @staticmethod
+    def serialize_data(data: Any) -> bytes:
+        # return msgpack.packb(data, use_bin_type=True)
+        pass
+
+    @staticmethod
+    def deserialize_data(data: bytes) -> Any:
+        # return msgpack.unpackb(data, raw=False)
+        pass
+
+# Example usage of transform_lambda
+source_code = "lambda x: x + 2"
+operations = ["multiply", "subtract", "divide"]
+
+for operation in operations:
+    modified_code = transform_lambda(source_code, operation)
+    print(f"Operation: {operation}, Modified Code: {modified_code}")