Description
python based 3d graphical interface, who cares what it looks like could be cad sticks for all i care for first implemtation.
follows code structure and data volume path, represents data volume path a basic light based ray traced feild for the the area that data will flow, and each manipulation of data affects the data flow path and therfore the light field emanation, then using electrical physics principles to make an efficient algorithm specific to that program that reduces the data flow field to a singular iterative algorithm. this could allow the visualisation of extranious data leaks and using randomised data within the space around the light emanation and you could see where data can leak into your program, could also be made within a 3d field tensor architecture for efficiency. such as
import numpy as np
import matplotlib.pyplot as plt
from typing import Tuple, Dict
class MentalStageEngine:
"""
A physics engine for simulating the dynamics of a global physics stage.
It evolves multiple interacting scalar fields on a 3D grid, representing
the contents and flow of the workspace.
The dynamics are designed to be stable and self-regulating.
"""
def __init__(self,
grid_size: int = 64,
dt: float = 0.01,
coupling_rate: float = 0.01,
interaction_strength: float = 0.01,
phase_amplitude: float = 0.1):
"""
Initializes the mental stage.
Args:
grid_size: The resolution of the 3D space.
dt: The time step for each evolution cycle.
coupling_rate: The strength of interaction between fields.
phase_amplitude: The magnitude of phase-based modulation.
"""
self.grid_size = grid_size
self.dt = dt
self.time = 0.0
self.iteration = 0
# Hyperparameters controlling the "physics"
self.coupling_rate = coupling_rate
self.interaction_strength = interaction_strength
self.phase_amplitude = phase_amplitude
# Initialize 3D tensor blocks for fields (e.g., sensory, abstract,physical)
# Start with a small, uniform positive value to represent a baseline state.
self.x_field = np.ones((grid_size, grid_size, grid_size)) * 1e-3
self.y_field = np.ones((grid_size, grid_size, grid_size)) * 1e-3
self.z_field = np.ones((grid_size, grid_size, grid_size)) * 1e-3
# Phase field, representing the rhythm or timing of processes.
self.phase_field = np.zeros((grid_size, grid_size, grid_size))
# Initial normalization to establish a baseline conserved quantity.
self.normalize_fields()
def _calculate_coupling_strength(self, field_magnitude: np.ndarray) -> np.ndarray:
"""
Calculates a stable coupling factor based on field magnitude.
High magnitude -> low coupling, preventing runaway feedback.
"""
# Using 1.0 + magnitude prevents division by zero and dampens the response.
return 1.0 / (1.0 + field_magnitude)
def evolve(self):
"""
Performs a single evolution step of the stage.
"""
# 1. Calculate current field magnitudes (L1 norm)
x_mag = np.abs(self.x_field)
y_mag = np.abs(self.y_field)
# 2. Update the phase field based on the combined activity of x and y
total_magnitude = x_mag + y_mag
coupling_strength = self._calculate_coupling_strength(total_magnitude)
self.phase_field += coupling_strength * self.dt
# 3. Evolve the primary fields
# Using a sinusoidal modulation ensures the factor is always positive and oscillatory.
phase_modulation = 1.0 + self.phase_amplitude * np.sin(self.phase_field)
# Copy previous state for the update step
x_prev = self.x_field.copy()
y_prev = self.y_field.copy()
# The core update logic:
# X and Y are symmetric, representing similar types of processes.
self.x_field = x_prev * phase_modulation + self.interaction_strength * (y_prev + self.z_field)
self.y_field = y_prev * phase_modulation + self.interaction_strength * (x_prev + self.z_field)
# Z is asymmetric, integrating from X and Y. It could represent a higher-level process.
self.z_field = (x_mag + y_mag) * phase_modulation * self.coupling_rate + self.interaction_strength * self.z_field
# 4. Normalize fields to conserve the total "substance".
self.normalize_fields()
# 5. Update simulation time
self.time += self.dt
self.iteration += 1
def normalize_fields(self):
"""
Normalizes the total L1 norm of all fields to 1.0.
This acts as a homeostatic mechanism, conserving the total "substance".
"""
# We only normalize the primary fields, phase is separate.
total_substance = np.sum(np.abs(self.x_field)) + np.sum(np.abs(self.y_field)) + np.sum(np.abs(self.z_field))
if total_substance > 1e-10: # Avoid division by zero
self.x_field /= total_substance
self.y_field /= total_substance
self.z_field /= total_substance
def get_system_activity(self) -> float:
"""
Calculates the total system activity (squared L2 norm).
This is a measure of the "intensity" or "focus" of the current state.
It is NOT a conserved quantity. need actual qauntum mechanics for that, this is just a tribbbuuuuuute
"""
return np.sum(self.x_field**2 + self.y_field**2 + self.z_field**2)
# ... (keep get_field_statistics, visualize_2d_slice, and a run_simulation method)
# The example usage would also be adapted for the new parameter names.
or something there abouts,
this is intended to give space time to compute architectures. not real sure if useful.
this then used as basis for the light architecture mathematics and simulation, and could also be platformed in combination with electrical simulation to find most efficient path within the field the light archiecture defines, because with all code and life first you need to define the universe and then work out the little shit.
please and thank you on god 100% cap cap