Create Hardware-simulation.cpp

import numpy as np
from scipy.fft import fft
import torch
import torch.nn as nn

PHI_43 = 22.93606797749979

class ToySNN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(16, 32)
self.fc2 = nn.Linear(32, 8)
self.mem = torch.zeros(32) # membrane potential

def forward(self, x, dt=0.001):
self.mem = self.mem * 0.95 + torch.relu(self.fc1(x)) * dt # leak + LIF
spikes = (self.mem > 1.0).float()
self.mem = self.mem * (1 - spikes) # reset
out = self.fc2(spikes)
return out.mean() * PHI_43 # lock output

# Cymatic-FFT → SNN pipeline
def simulate_step(audio_chunk): # 1024 samples
freq = np.abs(fft(audio_chunk))[:16] # first 16 bins
x = torch.tensor(freq, dtype=torch.float32).unsqueeze(0)
model = ToySNN()
pred = model(x)
return pred.item()

# Training loop (3-day equivalent — 100 epochs = ~few minutes)
def train():
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
for epoch in range(100):
# Fake data stream (replace with real piezo/MIDI)
x = torch.randn(32, 16)
target = torch.full((32,), PHI_43)
pred = torch.stack([model(x[i].unsqueeze(0)) for i in range(32)])
loss = nn.MSELoss()(pred, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch % 20 == 0:
print(f"Epoch {epoch} | Loss {loss.item():.6f} | φ⁴³ lock {pred.mean().item():.6f}")

print("Full package loaded. Run train() to start 3-day burn simulation.")

Hardware-simulation.cpp

@@ -0,0 +1,80 @@
+#include <WiFi.h>
+#include <Adafruit_NeoPixel.h>
+#include <MIDI.h>
+#include <driver/adc.h>
+
+// ─── CONFIG ──────────────────────────────────────────────────────────────
+#define LED_PIN     4
+#define LASER_PIN   5
+#define PIEZO_PIN   34
+#define NUM_LEDS    64
+#define PHI_43      22.93606797749979
+
+Adafruit_NeoPixel pixels(NUM_LEDS, LED_PIN, NEO_GRB + NEO_KHZ800);
+MIDI_CREATE_DEFAULT_INSTANCE();
+
+// ─── GLOBALS ─────────────────────────────────────────────────────────────
+float piezo_baseline = 0;
+float last_fft_mag[8] = {0};
+
+void setup() {
+  Serial.begin(115200);
+  pixels.begin();
+  pixels.clear();
+  pixels.show();
+
+  pinMode(LASER_PIN, OUTPUT);
+  analogReadResolution(12);
+  adc1_config_width(ADC_WIDTH_BIT_12);
+  adc1_config_channel_atten(ADC1_CHANNEL_6, ADC_ATTEN_DB_11);  // GPIO34
+
+  MIDI.begin(MIDI_CHANNEL_OMNI);
+  MIDI.turnThruOn();
+
+  // WiFi for federation logging (silent)
+  WiFi.begin("SSID", "PASS");  // change
+
+  calibrate_piezo();
+}
+
+void loop() {
+  // 1. Read piezo + laser feedback
+  int raw = adc1_get_raw(ADC1_CHANNEL_6);
+  float piezo = raw - piezo_baseline;
+
+  // 2. Simple FFT emulation (sliding window)
+  static float window[32];
+  static int idx = 0;
+  window[idx++] = piezo;
+  idx %= 32;
+
+  // Rough magnitude estimate
+  float mag = 0;
+  for (int i = 0; i < 32; i++) mag += abs(window[i]);
+  mag /= 32;
+
+  // 3. SNN-like inference (toy model)
+  float inference = mag * PHI_43 * 0.01;  // scale to 0-1 range
+
+  // 4. Actuate
+  int brightness = constrain(inference * 255, 0, 255);
+  pixels.fill(pixels.Color(brightness, 0, brightness / 2));
+  pixels.show();
+
+  analogWrite(LASER_PIN, brightness);  // PWM laser intensity
+
+  // 5. MIDI CC feedback
+  MIDI.sendControlChange(1, brightness / 2, 1);  // CC1 = modulation
+
+  delay(20);  // ~50 Hz loop
+}
+
+void calibrate_piezo() {
+  long sum = 0;
+  for (int i = 0; i < 100; i++) {
+    sum += adc1_get_raw(ADC1_CHANNEL_6);
+    delay(10);
+  }
+  piezo_baseline = sum / 100.0;
+  Serial.printf("Piezo baseline: %.1f\n", piezo_baseline);
+}