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"""
TrueFractalSurferNode (v11 - Asynchronous)
--------------------------------
This node implements the "two-brain" P-KAS model.
It fixes the "massive slowth" by moving the "Soma"
(the deep fractal calculation) onto a separate thread.
The "Dendrite" (the main step() function) runs at full
speed, making steering decisions based on the last
available "thought" from the Soma.
This enables true, infinite, real-time surfing.
"""
import numpy as np
import cv2
import time
import threading # We need this for the "Soma"
# --- Magic import block ---
import __main__
BaseNode = __main__.BaseNode
QtGui = __main__.QtGui
# --------------------------
# --- Numba JIT for high-speed fractal math ---
try:
from numba import jit
NUMBA_AVAILABLE = True
except ImportError:
NUMBA_AVAILABLE = False
print("Warning: TrueFractalSurferNode requires 'numba' for speed.")
@jit(nopython=True, fastmath=True)
def compute_mandelbrot_core(width, height, center_x, center_y, zoom, max_iter):
"""
Fast Numba-compiled Mandelbrot set calculator.
This is the "Soma" - it's allowed to be slow.
"""
result = np.zeros((height, width), dtype=np.float32)
scale_x = 3.0 / (width * zoom)
scale_y = 2.0 / (height * zoom)
for y in range(height):
for x in range(width):
c_real = center_x + (x - width / 2) * scale_x
c_imag = center_y + (y - height / 2) * scale_y
z_real = 0.0
z_imag = 0.0
n = 0
while n < int(max_iter):
if z_real * z_real + z_imag * z_imag > 4.0:
break
new_z_real = z_real * z_real - z_imag * z_imag + c_real
z_imag = 2.0 * z_real * z_imag + c_imag
z_real = new_z_real
n += 1
result[y, x] = n / max_iter
return result
class TrueFractalSurferNode(BaseNode):
NODE_CATEGORY = "Source"
NODE_COLOR = QtGui.QColor(100, 200, 250) # Crystalline Blue
def __init__(self, resolution=128, base_iterations=50, home_strength=0.05, boredom_threshold=0.1, iteration_scale=10.0):
super().__init__()
self.node_title = "True Surfer (Async)"
self.inputs = {
'zoom_speed': 'signal',
'steer_damp': 'signal',
'reset': 'signal'
}
self.outputs = {
'image': 'image',
'complexity': 'signal',
'x_pos': 'signal',
'y_pos': 'signal',
'zoom': 'signal',
'depth': 'signal'
}
if not NUMBA_AVAILABLE:
self.node_title = "Surfer (No Numba!)"
self.resolution = int(resolution)
self.base_iterations = int(base_iterations)
self.iteration_scale = float(iteration_scale)
self.home_strength = float(home_strength)
self.boredom_threshold = float(boredom_threshold)
# --- Internal Surfer State ---
self.home_x, self.home_y = -0.7, 0.0
self.center_x, self.center_y = self.home_x, self.home_y
self.zoom = 1.0
self.current_max_iter = self.base_iterations
# --- Internal Logic State ---
self.complexity = 0.0
self.nudge_x, self.nudge_y = 0.0, 0.0
# --- Asynchronous "Soma" (The Slow Brain) ---
self.soma_thread = None
self.soma_is_working = False
self.soma_lock = threading.Lock() # To safely pass data
# Data to pass to the thread
self.job_x = self.center_x
self.job_y = self.center_y
self.job_zoom = self.zoom
self.job_max_iter = self.current_max_iter
# Data to get back from the thread
self.completed_fractal_data = np.zeros((self.resolution, self.resolution), dtype=np.float32)
# Start the "Soma"
self.is_running = True
self.start_soma_thread()
def randomize(self):
"""Reset to the 'home' position"""
with self.soma_lock:
self.center_x, self.center_y = self.home_x, self.home_y
self.zoom = 1.0
self.current_max_iter = self.base_iterations
# -----------------------------------------------------------------
# --- "THIN LOGIC" (The Fast Brain / Dendrite) ---
# -----------------------------------------------------------------
def _find_steering_vector(self, fractal_data):
"""
The "Thin Logic" of the surfer.
Calculates a steering vector as a blend of two forces.
"""
if fractal_data.size == 0:
return 0, 0
self.complexity = np.std(fractal_data)
# "Surf Force" (Steer to complex edge)
score_map = fractal_data * (1.0 - fractal_data) * 4.0
max_idx = np.argmax(score_map)
target_y, target_x = np.unravel_index(max_idx, score_map.shape)
center = self.resolution // 2
surf_nudge_x = (target_x - center) / center
surf_nudge_y = (target_y - center) / center
# "Home Force" (Steer to "shallows")
home_nudge_x = self.home_x - self.center_x
home_nudge_y = self.home_y - self.center_y
norm = np.sqrt(home_nudge_x**2 + home_nudge_y**2) + 1e-9
home_nudge_x = (home_nudge_x / norm) * self.home_strength
home_nudge_y = (home_nudge_y / norm) * self.home_strength
# Logic Blend Weight
surf_weight = np.clip(self.complexity / self.boredom_threshold, 0.0, 1.0)
home_weight = 1.0 - surf_weight
# Combine Forces
target_nudge_x = (surf_nudge_x * surf_weight) + (home_nudge_x * home_weight)
target_nudge_y = (surf_nudge_y * surf_weight) + (home_nudge_y * home_weight)
return target_nudge_x, target_nudge_y
# -----------------------------------------------------------------
# -----------------------------------------------------------------
# --- "SOMA" THREAD (The Slow Brain) ---
# -----------------------------------------------------------------
def start_soma_thread(self):
"""Starts the background calculation thread."""
if self.soma_is_working or not self.is_running:
return
self.soma_is_working = True
self.soma_thread = threading.Thread(target=self.soma_worker, daemon=True)
self.soma_thread.start()
def soma_worker(self):
"""
This is the "Soma." It runs in the background.
It just does one job: calculate the fractal.
"""
# Get the job parameters
with self.soma_lock:
x, y, z, i = self.job_x, self.job_y, self.job_zoom, self.job_max_iter
# --- THE SLOW, DEEP CALCULATION ---
fractal_data = compute_mandelbrot_core(
self.resolution, self.resolution,
x, y, z, i
)
# ---------------------------------
# Safely pass the result back to the main thread
with self.soma_lock:
self.completed_fractal_data = fractal_data
self.soma_is_working = False
# -----------------------------------------------------------------
# --- "DENDRITE" (The Fast Brain, runs every frame) ---
# -----------------------------------------------------------------
def step(self):
if not NUMBA_AVAILABLE:
return
# 1. Get Inputs
zoom_speed = self.get_blended_input('zoom_speed', 'sum') or 0.01
steer_damp = self.get_blended_input('steer_damp', 'sum') or 0.1
reset = self.get_blended_input('reset', 'sum') or 0.0
if reset > 0.5:
self.randomize()
# 2. Check on the "Soma" (the thread)
if not self.soma_is_working:
# --- The "Soma" is done! Time to "think" ---
# A. Get the "perception" (the finished fractal)
with self.soma_lock:
fractal_data_to_process = self.completed_fractal_data.copy()
# B. Run the "Thin Logic" (Dendrite)
target_nudge_x, target_nudge_y = self._find_steering_vector(fractal_data_to_process)
# C. Apply Steering (with Damping)
smoothing_factor = 1.0 - np.clip(steer_damp, 0.0, 0.95)
self.nudge_x = (self.nudge_x * (1.0 - smoothing_factor)) + (target_nudge_x * smoothing_factor)
self.nudge_y = (self.nudge_y * (1.0 - smoothing_factor)) + (target_nudge_y * smoothing_factor)
# D. Act on the "World" (Update next job's parameters)
self.center_x += self.nudge_x / (self.zoom * 2.0)
self.center_y += self.nudge_y / (self.zoom * 2.0)
self.zoom *= (1.0 + (zoom_speed * 0.05))
# E. Calculate "Depth of Vision" for the *next* frame
self.current_max_iter = int(self.base_iterations + np.sqrt(max(1.0, self.zoom)) * self.iteration_scale)
# F. Give the "Soma" its *new* job
with self.soma_lock:
self.job_x = self.center_x
self.job_y = self.center_y
self.job_zoom = self.zoom
self.job_max_iter = self.current_max_iter
self.start_soma_thread() # Wake up the "Soma"
# (If the Soma is still working, the Dendrite does nothing
# but wait. It continues to output the *last* frame).
def get_output(self, port_name):
# We *always* output the last *completed* data
if port_name == 'image':
return self.completed_fractal_data
elif port_name == 'complexity':
return self.complexity * 5.0 # Boost signal
elif port_name == 'x_pos':
return self.center_x
elif port_name == 'y_pos':
return self.center_y
elif port_name == 'zoom':
return self.zoom
elif port_name == 'depth':
return float(self.current_max_iter)
return None
def get_display_image(self):
# We *always* display the last *completed* data
img_u8 = (np.clip(self.completed_fractal_data, 0, 1) * 255).astype(np.uint8)
img_color = cv2.applyColorMap(img_u8, cv2.COLORMAP_MAGMA)
# Draw steering vector
h, w, _ = img_color.shape
center = (w // 2, h // 2)
is_surfing = self.complexity > self.boredom_threshold
arrow_color = (0, 255, 0) if is_surfing else (0, 0, 255)
target_x = int(center[0] + self.nudge_x * w)
target_y = int(center[1] + self.nudge_y * h)
cv2.arrowedLine(img_color, center, (target_x, target_y), arrow_color, 1)
# Display the current iteration depth
cv2.putText(img_color, f"Depth: {self.current_max_iter}", (5, h - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)
# --- NEW: Show when the "Soma" (thread) is busy ---
if self.soma_is_working:
cv2.putText(img_color, "CALCULATING...", (5, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)
img_color = np.ascontiguousarray(img_color)
return QtGui.QImage(img_color.data, w, h, 3*w, QtGui.QImage.Format.Format_BGR888)
def get_config_options(self):
return [
("Resolution", "resolution", self.resolution, None),
("Base Iterations", "base_iterations", self.base_iterations, None),
("Iteration Scale", "iteration_scale", self.iteration_scale, None),
("Home Strength", "home_strength", self.home_strength, None),
("Complexity Sensitivity", "boredom_threshold", self.boredom_threshold, None)
]
def close(self):
# Clean up the thread
self.is_running = False
if self.soma_thread is not None:
self.soma_thread.join(timeout=0.5)
super().close() |