main.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#------------------------------------------------------------------------------
# Standard Library Imports - 3.13 std libs **ONLY**
#------------------------------------------------------------------------------
import re
import os
import io
import dis
import sys
import ast
import time
import json
import math
import uuid
import shlex
import struct
import shutil
import pickle
import ctypes
import logging
import tomllib
import pathlib
import asyncio
import inspect
import hashlib
import platform
import traceback
import functools
import linecache
import importlib
import threading
import subprocess
import tracemalloc
from pathlib import Path
from enum import Enum, auto, StrEnum
from queue import Queue, Empty
from datetime import datetime
from abc import ABC, abstractmethod
from contextlib import contextmanager
from functools import wraps, lru_cache
from dataclasses import dataclass, field
from concurrent.futures import ThreadPoolExecutor
from importlib.util import spec_from_file_location, module_from_spec
from types import SimpleNamespace, ModuleType,  MethodType, FunctionType, CodeType, TracebackType, FrameType
from typing import (
    Any, Dict, List, Optional, Union, Callable, TypeVar, Tuple, Generic, Set,
    Coroutine, Type, NamedTuple, ClassVar, Protocol, runtime_checkable
)
#------------------------------------------------------------------------------
# Logging Configuration
#------------------------------------------------------------------------------
class CustomFormatter(logging.Formatter):
    """Custom formatter for colored console output."""
    COLORS = {
        'grey': "\x1b[38;20m",
        'yellow': "\x1b[33;20m",
        'red': "\x1b[31;20m",
        'bold_red': "\x1b[31;1m",
        'green': "\x1b[32;20m",
        'reset': "\x1b[0m"
    }
    FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)"
    FORMATS = {
        logging.DEBUG: COLORS['grey'] + FORMAT + COLORS['reset'],
        logging.INFO: COLORS['green'] + FORMAT + COLORS['reset'],
        logging.WARNING: COLORS['yellow'] + FORMAT + COLORS['reset'],
        logging.ERROR: COLORS['red'] + FORMAT + COLORS['reset'],
        logging.CRITICAL: COLORS['bold_red'] + FORMAT + COLORS['reset']
    }
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.log_queue = Queue()
        self.log_thread = threading.Thread(target=self._log_thread_func, daemon=True)
        self.log_thread.start()
    def format(self, record):
        log_fmt = self.FORMATS.get(record.levelno, self.FORMAT)
        formatter = logging.Formatter(log_fmt)
        return formatter.format(record)
    def _log_thread_func(self):
        while True:
            try:
                record = self.log_queue.get()
                if record is None:
                    break
                super().handle(record)
            except Exception:
                import traceback
                print("Error in log thread:", file=sys.stderr)
                traceback.print_exc()
    def emit(self, record):
        self.log_queue.put(record)
    def close(self):
        self.log_queue.put(None)
        self.log_thread.join()
class AdminLogger(logging.LoggerAdapter):
    """Logger adapter for administrative logging."""
    def __init__(self, logger, extra=None):
        super().__init__(logger, extra or {})
    def process(self, msg, kwargs):
        return f"{self.extra.get('name', 'Admin')}: {msg}", kwargs
logger = AdminLogger(logging.getLogger(__name__))
#------------------------------------------------------------------------------
# Security
#------------------------------------------------------------------------------
AccessLevel = Enum('AccessLevel', 'READ WRITE EXECUTE ADMIN USER')
@dataclass
class AccessPolicy:
    """Defines access control policies for runtime operations."""
    level: AccessLevel
    namespace_patterns: list[str] = field(default_factory=list)
    allowed_operations: list[str] = field(default_factory=list)
    def can_access(self, namespace: str, operation: str) -> bool:
        return any(pattern in namespace for pattern in self.namespace_patterns) and \
               operation in self.allowed_operations
class SecurityContext:
    """Manages security context and audit logging for runtime operations."""
    def __init__(self, user_id: str, access_policy: AccessPolicy):
        self.user_id = user_id
        self.access_policy = access_policy
        self._audit_log = []
    def log_access(self, namespace: str, operation: str, success: bool):
        self._audit_log.append({
            "user_id": self.user_id,
            "namespace": namespace,
            "operation": operation,
            "success": success,
            "timestamp": datetime.now().timestamp()
        })
class SecurityValidator(ast.NodeVisitor):
    """Validates AST nodes against security policies."""
    def __init__(self, security_context: SecurityContext):
        self.security_context = security_context
    def visit_Name(self, node):
        if not self.security_context.access_policy.can_access(node.id, "read"):
            raise PermissionError(f"Access denied to name: {node.id}")
        self.generic_visit(node)
    def visit_Call(self, node):
        if isinstance(node.func, ast.Name):
            if not self.security_context.access_policy.can_access(node.func.id, "execute"):
                raise PermissionError(f"Access denied to function: {node.func.id}")
        self.generic_visit(node)
#------------------------------------------------------------------------------
# Runtime Namespace Management
#------------------------------------------------------------------------------
class RuntimeNamespace:
    """Manages hierarchical runtime namespaces with security controls."""
    def __init__(self, name: str = "root", parent: Optional['RuntimeNamespace'] = None):
        self._name = name
        self._parent = parent
        self._children: Dict[str, 'RuntimeNamespace'] = {}
        self._content = SimpleNamespace()
        self._security_context: Optional[SecurityContext] = None
        self.available_modules: Dict[str, Any] = {}
    @property
    def full_path(self) -> str:
        if self._parent:
            return f"{self._parent.full_path}.{self._name}"
        return self._name
    def add_child(self, name: str) -> 'RuntimeNamespace':
        child = RuntimeNamespace(name, self)
        self._children[name] = child
        return child
    def get_child(self, path: str) -> Optional['RuntimeNamespace']:
        parts = path.split(".", 1)
        if len(parts) == 1:
            return self._children.get(parts[0])
        child = self._children.get(parts[0])
        return child.get_child(parts[1]) if child and len(parts) > 1 else None
#------------------------------------------------------------------------------
# Type Definitions
#------------------------------------------------------------------------------
class FrameModel(ABC):
    """A frame model is a data structure that contains the data of a frame aka a chunk of text contained by dilimiters.
        Delimiters are defined as '---' and '\n' or its analogues (EOF) or <|in_end|> or "..." etc for the start and end of a frame respectively.)
        the frame model is a data structure that is independent of the source of the data.
        portability note: "dilimiters" are established by the type of encoding and the arbitrary writing-style of the source data. eg: ASCII
    """
    @abstractmethod
    def to_bytes(self) -> bytes:
        """Return the frame data as bytes."""
        pass
class SerialObject(FrameModel, ABC):
    """SerialObject is an abstract class that defines the interface for serializable objects within the abstract data model.
        Inputs:
            AbstractDataModel: The base class for the SerialObject class

        Returns:
            SerialObject object
    
    """
    @abstractmethod
    def dict(self) -> dict:
        """Return a dictionary representation of the model."""
        pass

    @abstractmethod
    def json(self) -> str:
        """Return a JSON string representation of the model."""
        pass
@dataclass
class ConcreteModel(SerialObject):
    """
    This concrete implementation of SerialObject ensures that instances can
    be used wherever a FrameModel or SerialObject is required,
    hence demonstrating polymorphism.
        Inputs:
            SerialObject: The base class for the ConcreteModel class

        Returns:
            ConcreteModel object        
    """
    name: str
    age: int
    timestamp: datetime = field(default_factory=datetime.now)

    def to_bytes(self) -> bytes:
        """Return the JSON representation as bytes."""
        return self.json().encode()

    def to_str(self) -> str:
        """Return the JSON representation as a string."""
        return self.json()

    def dict(self) -> dict:
        """Return a dictionary representation of the model."""
        return {
            "name": self.name,
            "age": self.age,
            "timestamp": self.timestamp.isoformat(),
        }

    def json(self) -> str:
        """Return a JSON representation of the model as a string."""
        return json.dumps(self.dict())

# Abstract Base Class for models
class AtomicModel(ConcreteModel, ABC):
    @abstractmethod
    def get_properties(self) -> Dict[str, Any]:
        """Method to get properties of the AtomicModel instance."""
        pass

    @abstractmethod
    def update_state(self, state: Dict[str, Any]) -> None:
        """Method to update the state of the AtomicModel."""
        pass

    @abstractmethod
    def analyze(self) -> Dict[str, Any]:
        """Method for performing analysis on the AtomicModel."""
        pass

    @abstractmethod
    def validate(self) -> bool:
        """Method for validating the AtomicModel state."""
        pass

    @abstractmethod
    def __repr__(self) -> str:
        """Return the string representation of the model."""
        pass

    @abstractmethod
    def __eq__(self, other: Any) -> bool:
        """Equality comparison between two models."""
        pass

class Condition(AtomicModel, ABC):
    """Represents a state or condition in the system."""
    attributes: Dict[str, Any]
    @abstractmethod
    def __repr__(self):
        return f"Condition({self.attributes})"

class Action(Condition, ABC):
    """Abstract base class for an elementary action or reaction."""
    @abstractmethod
    def execute(self, input_condition: Condition) -> Condition:
        """Transform an input condition into an output condition."""
        pass
class Reaction(Action, ABC):
    """Concrete implementation of an elementary reaction."""
    transformation: Callable[[Condition], Condition]
    @abstractmethod
    def execute(self, input_condition: Condition) -> Condition:
        output_condition = self.transformation(input_condition)
        print(f"Reaction: {input_condition} -> {output_condition}")
        return output_condition

@dataclass
class Agency:
    """Represents an invariant agency catalyzing actions."""
    name: str
    rules: Dict[str, Action] = field(default_factory=dict)

    def perform_action(self, action_key: str, input_condition: Condition) -> Condition:
        if action_key not in self.rules:
            raise ValueError(f"Action {action_key} is not defined for agency {self.name}.")
        action = self.rules[action_key]
        print(f"Agency '{self.name}' performing action '{action_key}'...")
        return action.execute(input_condition)

    def add_action(self, action_key: str, action: Action):
        self.rules[action_key] = action
        print(f"Action '{action_key}' added to agency '{self.name}'.")

"""Homoiconism dictates that, upon runtime validation, all objects are code and data.
To facilitate; we utilize first class functions and a static typing system.
This maps perfectly to the three aspects of nominative invariance:
    Identity preservation, T: Type structure (static)
    Content preservation, V: Value space (dynamic)
    Behavioral preservation, C: Computation space (transformative)
    [[T (Type) ←→ V (Value) ←→ C (Callable)]] == 'quantum infodynamics, a triparte element; our Atom()(s)'
    Meta-Language (High Level)
      ↓ [First Collapse - Compilation]
    Intermediate Form (Like a quantum superposition)
      ↓ [Second Collapse - Runtime]
    Executed State (Measured Reality)
What's conserved across these transformations:
    Nominative relationships
    Information content
    Causal structure
    Computational potential"""
T = TypeVar('T', bound=any) # T for TypeVar, V for ValueVar. Homoicons are T+V.
V = TypeVar('V', bound=Union[int, float, str, bool, list, dict, tuple, set, object, Callable, type])
C = TypeVar('C', bound=Callable[..., Any])  # callable 'T'/'V' first class function interface
"""py objects are implemented as C structures.
typedef struct _object {
    Py_ssize_t ob_refcnt;
    PyTypeObject *ob_type;
} PyObject; """
# Everything in Python is an object, and every object has a type. The type of an object is a class. Even the
# type class itself is an instance of type. Functions defined within a class become method objects when
# accessed through an instance of the class
"""(3.13 std lib)Functions are instances of the function class
Methods are instances of the method class (which wraps functions)
Both function and method are subclasses of object
homoiconism dictates the need for a way to represent all Python constructs as first class citizen(fcc):
    (functions, classes, control structures, operations, primitive values)
nominative 'true OOP'(SmallTalk) and my specification demands code as data and value as logic, structure.
The Atom(), our polymorph of object and fcc-apparent at runtime, always represents the literal source code
    which makes up their logic and possess the ability to be stateful source code data structure. """
# HOMOICONISTIC morphological source code displays 'modified quine' behavior
# within a validated runtime, if and only if the valid python interpreter
# has r/w/x permissions to the source code file and some method of writing
# state to the source code file is available. Any interruption of the
# '__exit__` method or misuse of '__enter__' will result in a runtime error
# AP (Availability + Partition Tolerance): A system that prioritizes availability and partition
# tolerance may use a distributed architecture with eventual consistency (e.g., Cassandra or Riak).
# This ensures that the system is always available (availability), even in the presence of network
# partitions (partition tolerance). However, the system may sacrifice consistency, as nodes may have
# different views of the data (no consistency). A homoiconic piece of source code is eventually
# consistent, assuming it is able to re-instantiated.
# Enums for type system
#------------------------------------------------------------------------------
# Enums and Data Classes for Symmetries and Manifolds
#------------------------------------------------------------------------------
class Symmetry(Enum):
    TRANSLATION = "Translation"
    ROTATION = "Rotation"
    PHASE = "Phase"
class Conservation(Enum):
    INFORMATION = "Information"
    COHERENCE = "Coherence"
    BEHAVIORAL = "Behavioral"
@dataclass
class State:
    type_space: T
    value_space: V
    computation_space: C
    symmetry: Symmetry
    conservation: Conservation
@runtime_checkable
class Field(Protocol):
    """
    Defines a dynamic field space, leveraging symmetries and manifold mappings.
    """
    def interact(self, state: State) -> State:
        ...
def __field__(cls: Type[{T, V, C}]) -> Type[{T, V, C}]: # homoicon decorator
    """Decorator to create a homoiconic atom."""
    original_init = cls.__init__
    def new_init(self, *args, **kwargs):
        original_init(self, *args, **kwargs)
        if not hasattr(self, 'id'):
            self.id = hashlib.sha256(self.__class__.__name__.encode('utf-8')).hexdigest()

    cls.__init__ = new_init
    return cls
FieldType = TypeVar('AtomType', bound=Field)
class Gauge:
    """
    Manages the field's influence on type, value, and computation manifolds.
    """
    def __init__(self, local: State, global_: State, emergent: State):
        self.fields = [local, global_, emergent]

    def apply_transformation(self, state: State) -> State:
        transformed_state = state
        for field in self.fields:
            transformed_state = self._combine_states(transformed_state, field)
        return transformed_state

    def _combine_states(self, state_a: State, state_b: State) -> State:
        # Apply computation from state_b to the value space of state_a
        new_value = [state_b.computation_space(val) for val in state_a.value_space]

        return State(
            type_space=state_a.type_space,
            value_space=new_value,
            computation_space=state_a.computation_space,
            symmetry=state_a.symmetry,
            conservation=state_b.conservation,
        )
#------------------------------------------------------------------------------
# Deamon/Kernel
#------------------------------------------------------------------------------
class MorphologicalKernel:
    """
    Central to running feedback-driven transformations.
    Interprets configuration space in accordance with Noetherian symmetries.
    """
    def __init__(self):
        self.state_history = []

    def run(self, initial_state: State, gauge: Gauge, steps: int) -> State:
        current_state = initial_state
        for _ in range(steps):
            current_state = gauge.apply_transformation(current_state)
            self.state_history.append(current_state)
        return current_state

    def __repr__(self):
        return f"Kernel with {len(self.state_history)} state transitions."

"""The type system forms the "boundary" theory
The runtime forms the "bulk" theory
The homoiconic property ensures they encode the same information
The holoiconic property enables:
    States as quantum superpositions
    Computations as measurements
    Types as boundary conditions
    Runtime as bulk geometry"""
class HoloiconicTransform(Generic[T, V, C]):
    """A square matrix `A` is Hermitian if and only if it is unitarily diagonalizable with real eigenvalues. """
    @staticmethod
    def flip(value: V) -> C:
        """Transform value to computation (inside-out)"""
        return lambda: value
    @staticmethod
    def flop(computation: C) -> V:
        """Transform computation to value (outside-in)"""
        return computation()
"""
The Heisenberg Uncertainty Principle tells us that we can’t precisely measure both the position and momentum of a particle. In computation, we encounter similar trade-offs between precision and performance:
    For instance, with approximate computing or probabilistic algorithms, we trade off exact accuracy for faster or less resource-intensive computation.
    Quantum computing itself takes advantage of this principle, allowing certain computations to run probabilistically rather than deterministically.
The idea that data could be "uncertain" in some way until acted upon or observed might open new doors in software architecture. Just as quantum computing uses uncertainty productively, conventional computing might benefit from intentionally embracing imprecise states or probabilistic pathways in specific contexts, especially in AI, optimization, and real-time computation.
Zero-copy and immutable data structures are, in a way, a step toward this quantum principle. By reducing the “work” done on data, they minimize thermodynamic loss. We could imagine architectures that go further, preserving computational history or chaining operations in such a way that information isn't “erased” but transformed, making the process more like a conservation of informational “energy.”
If algorithms were seen as “wavefunctions” representing possible computational outcomes, then choosing a specific outcome (running the algorithm) would be like collapsing a quantum state. In this view:
    Each step of an algorithm could be seen as an evolution of the wavefunction, transforming the data structure through time.
    Non-deterministic algorithms could explore multiple “paths” through data, and the most efficient or relevant one could be selected probabilistically.
    Treating data and computation as probabilistic, field-like entities rather than fixed operations on fixed memory.
    Embracing superpositions, potential operations, and entanglement within software architecture, allowing for context-sensitive, energy-efficient, and exploratory computation.
    Leveraging thermodynamic principles more deeply, designing architectures that conserve “informational energy” by reducing unnecessary state changes and maximizing information flow efficiency.
"""
"""Self-Adjoint Operators on a Hilbert Space: In quantum mechanics, the state space of a system is typically modeled as a Hilbert space—a complete vector space equipped with an inner product. States within this space can be represented as vectors (ket vectors, ∣ψ⟩∣ψ⟩), and observables (like position, momentum, or energy) are modeled by self-adjoint operators.

    Self-adjoint operators are crucial because they guarantee that the eigenvalues (which represent possible measurement outcomes in quantum mechanics) are real numbers, which is a necessary condition for observable quantities in a physical theory. In quantum mechanics, the evolution of a state ∣ψ⟩∣ψ⟩ under an observable A^A^ can be described as the action of the operator A^A^ on ∣ψ⟩∣ψ⟩, and these operators must be self-adjoint to maintain physical realism.
    
    In-other words, self-adjoint operators are equal to their Hermitian conjugates."""
#------------------------------------------------------------------------------
# Example Usage
#------------------------------------------------------------------------------
def visualize_state_history(state_history):
    """
    Visualizes the evolution of the state transformations over time.
    
    This function takes the state history from the MorphologicalKernel's execution
    and generates a simple line plot representing the "value space" at each
    transformation step. This is a simplistic visualization to help illustrate
    how the value space evolves, a key concept in understanding transformations
    in this framework.

    Parameters:
    - state_history: A list of State objects created during the kernel's run.
      Each State object represents the system's configuration at a specific point
      in time.

    Returns:
    - Matplotlib Figure showcasing the value space over time.
    
    Raises:
    - ValueError: If the state_history is not provided or is empty.
    """
    if not state_history:
        raise ValueError("state_history cannot be empty!")

    values = [state.value_space for state in state_history]
    #plt.plot(values)
    #plt.title('Evolution of Value Space')
    #plt.xlabel('Step')
    #plt.ylabel('Value Space')
    #plt.grid(True)
    #plt.show()
def main():
    """
    Main Execution and Example of Morphological Kernel.

    This function outlines the setup and execution process for the Morphological Kernel.
    It showcases how initial states and Gauge configurations are used to propagate system
    transformations through the invocation of the kernel's `run` method. Additionally,
    it provides a demonstration of visualizing the resulting state evolution.

    Steps included:
    1. Definition of the initial state as a combination of type, value, and computation
       spaces, decorated with symmetry and conservation laws.
    2. Setup of Gauge states: local, global, and emergent, each providing specific
       transformation rules for manipulating system configurations.
    3. Initialization and execution of the Morphological Kernel, running a series of
       transformations over the specified steps.
    4. Display of the final state and visualization of the state history to illustrate
       the cumulative impact of transformation steps.

    Outputs:
    - Terminal output of the final state configuration after running the kernel.
    - A visual plot showing Value Space evolution for ease of conceptual understanding.
    """
    initial_state = State(
        type_space=lambda x: x,
        value_space=[0],
        computation_space=lambda x: x,
        symmetry=Symmetry.TRANSLATION,
        conservation=Conservation.INFORMATION
    )

    local_gauge = State(
        type_space=lambda x: x,
        value_space=[1],
        computation_space=lambda x: x + 1,
        symmetry=Symmetry.ROTATION,
        conservation=Conservation.COHERENCE
    )

    global_gauge = State(
        type_space=lambda x: x,
        value_space=[4],
        computation_space=lambda x: 2 * x,
        symmetry=Symmetry.PHASE,
        conservation=Conservation.BEHAVIORAL
    )

    emergent_gauge = State(
        type_space=lambda x: x,
        value_space=[0],
        computation_space=lambda x: x,
        symmetry=Symmetry.TRANSLATION,
        conservation=Conservation.INFORMATION
    )

    gauge = Gauge(local=local_gauge, global_=global_gauge, emergent=emergent_gauge)
    
    kernel = MorphologicalKernel()
    final_state = kernel.run(initial_state, gauge, steps=10)
    
    print(f"Final state: {final_state}")
    
    visualize_state_history(kernel.state_history)

    def collapse_wave_function(condition: Condition) -> Condition:
        """Simulates a quantum observation collapsing the wave function."""
        new_attributes = {**condition.attributes, "observed": True}
        return Condition(attributes=new_attributes)

    def metabolize(condition: Condition) -> Condition:
        """Simulates metabolic transformation in an organism."""
        new_attributes = {**condition.attributes, "energy_level": condition.attributes.get("energy_level", 0) - 10}
        return Condition(attributes=new_attributes)

if __name__ == '__main__':
    main()