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3bb804c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | """
Signal Attractor Node - Generates a 2D chaotic pattern from two signals
Place this file in the 'nodes' folder
"""
import numpy as np
from PyQt6 import QtGui
import cv2
import sys
import os
# --- This is the new, correct block ---
import __main__
BaseNode = __main__.BaseNode
PA_INSTANCE = getattr(__main__, "PA_INSTANCE", None)
# ------------------------------------
class SignalAttractorNode(BaseNode):
NODE_CATEGORY = "Transform"
NODE_COLOR = QtGui.QColor(180, 80, 180) # Attractor Purple
def __init__(self, width=128, height=128, param_c=1.0, param_d=0.7):
super().__init__()
self.node_title = "Signal Attractor"
self.inputs = {
'signal_a': 'signal',
'signal_b': 'signal'
}
self.outputs = {'image': 'image', 'x_out': 'signal', 'y_out': 'signal'}
self.w, self.h = int(width), int(height)
# Attractor state
self.x, self.y = 0.1, 0.1
# Parameters (a & b are controlled by input, c & d are configurable)
self.param_c = float(param_c)
self.param_d = float(param_d)
# For visualization
self.points = np.zeros((self.h, self.w), dtype=np.float32)
self.img = np.zeros((self.h, self.w), dtype=np.float32)
def step(self):
# Get signals, map from [-1, 1] to [-2, 2]
param_a = (self.get_blended_input('signal_a', 'sum') or 0.0) * 2.0
param_b = (self.get_blended_input('signal_b', 'sum') or 0.0) * 2.0
# Iterate the attractor equations 500 times per frame
for _ in range(500):
# Clifford Attractor equations
x_new = np.sin(param_a * self.y) + self.param_c * np.cos(param_a * self.x)
y_new = np.sin(param_b * self.x) + self.param_d * np.cos(param_b * self.y)
self.x, self.y = x_new, y_new
# Scale from [-2, 2] range to image coordinates
px = int((self.x + 2.0) / 4.0 * self.w)
py = int((self.y + 2.0) / 4.0 * self.h)
if 0 <= px < self.w and 0 <= py < self.h:
self.points[py, px] += 0.1 # Add energy
# Apply decay to the image so it fades
self.points *= 0.97
self.points = np.clip(self.points, 0, 1.0)
# Blur for a "glowing" effect
self.img = cv2.GaussianBlur(self.points, (3, 3), 0)
def get_output(self, port_name):
if port_name == 'image':
return self.img
elif port_name == 'x_out':
return self.x / 2.0 # Normalize to [-1, 1]
elif port_name == 'y_out':
return self.y / 2.0 # Normalize to [-1, 1]
return None
def get_display_image(self):
img_u8 = (np.clip(self.img, 0, 1) * 255).astype(np.uint8)
img_u8 = np.ascontiguousarray(img_u8)
return QtGui.QImage(img_u8.data, self.w, self.h, self.w, QtGui.QImage.Format.Format_Grayscale8)
def get_config_options(self):
return [
("Param C", "param_c", self.param_c, None),
("Param D", "param_d", self.param_d, None),
("Width", "w", self.w, None),
("Height", "h", self.h, None),
] |