Technical documentation

import torch
import numpy as np
# Full code at modules.html#amp and modules.html#harmonic
from livingcircuit import AdaptiveAmplitudeStabilizer, harmonic_vector_stabilizer
from phase_reflection_agent_v3 import PhaseReflectionAgent

# Transformer block with full stabilization stack
class StabilizedTransformerBlock(torch.nn.Module):
    def __init__(self, dim, layer_idx=0):
        super().__init__()
        self.norm1 = torch.nn.LayerNorm(dim)
        self.norm2 = torch.nn.LayerNorm(dim)
        self.attn  = torch.nn.MultiheadAttention(dim, num_heads=8, batch_first=True)
        self.ffn   = torch.nn.Sequential(
            torch.nn.Linear(dim, dim * 4),
            torch.nn.GELU(),
            torch.nn.Linear(dim * 4, dim)
        )
        self.stab1 = AdaptiveAmplitudeStabilizer(dim, layer_idx=layer_idx)
        self.stab2 = AdaptiveAmplitudeStabilizer(dim, layer_idx=layer_idx)

    def forward(self, x):
        # Harmonic conditioning on input
        t = np.linspace(0, 4 * np.pi, x.shape[1])
        h = harmonic_vector_stabilizer(t)
        gain = float(h["quality_score"]) * 0.1  # subtle modulation

        attn_out, _ = self.attn(self.norm1(x), self.norm1(x), self.norm1(x))
        x = x + self.stab1(attn_out)

        ffn_out = self.ffn(self.norm2(x))
        x = x + self.stab2(ffn_out)
        return x

# Health monitoring during inference
monitor = PhaseReflectionAgent(name="inference-monitor")

def monitored_inference(model, input_ids, cycle):
    with torch.no_grad():
        output = model(input_ids)
    state = monitor.step(cycle)
    if state.stability < 0.35:
        print(f"Cycle {cycle}: stability low ({state.stability}) — check upstream")
    return output, state

import numpy as np
from livingcircuit import (adaptive_amplitude_stabilizer,
                            harmonic_vector_stabilizer,
                            SimpleVoiceToneAnalyzer,
                            stabilize_solar_output)

def process_audio_frame(frame: np.ndarray, set_pt: float, memory: float):
    """
    Full audio conditioning pipeline for a single frame.
    frame   : raw audio samples (float32 array)
    set_pt  : current amplitude set point (carry forward)
    memory  : smoother memory (carry forward)
    """
    # 1. Tone analysis — is this frame calm, neutral, or intense?
    analyzer   = SimpleVoiceToneAnalyzer()
    tone_info  = analyzer.analyze(frame)

    # 2. Amplitude stabilization — prevent spikes from clipping downstream
    rms = float(np.sqrt(np.mean(frame ** 2)))
    stabilized_rms, set_pt = adaptive_amplitude_stabilizer(
        signal=rms, set_point=set_pt,
        variance_limit=0.15, impedance=0.5,
        learn_rate=0.03
    )

    # 3. Harmonic coherence check — is this frame worth processing?
    t = np.linspace(0, 2 * np.pi, len(frame))
    h = harmonic_vector_stabilizer(t * tone_info["intensity"])
    coherence = h["quality_score"]

    # 4. Envelope smoothing with carried state
    smooth_rms, memory = stabilize_solar_output(
        current_val=stabilized_rms,
        previous_val=set_pt,
        dt=1.0 / 44100 * len(frame),
        memory=memory
    )

    return {
        "tone":      tone_info["tone"],
        "intensity": tone_info["intensity"],
        "coherence": coherence,
        "rms":       smooth_rms,
        "set_pt":    set_pt,
        "memory":    memory,
    }

# Usage in a streaming pipeline
set_pt = 0.1
memory = 0.0
for frame in audio_stream:
    result = process_audio_frame(frame, set_pt, memory)
    set_pt = result["set_pt"]
    memory = result["memory"]
    if result["coherence"] > 1.5:  # high coherence = meaningful content
        send_to_feature_extractor(frame)

import numpy as np
from livingcircuit import harmonic_vector_stabilizer, adaptive_amplitude_stabilizer
from impedance_variance_vectors import impedance_variance_vectors

def condition_eeg_channel(raw_signal: np.ndarray, sample_rate: int = 256):
    """
    Condition a single EEG channel while preserving harmonic structure.
    raw_signal  : raw EEG samples (microvolts, float64)
    sample_rate : samples per second (default 256 Hz)
    """
    # Normalize to [-1, 1] range
    peak = np.max(np.abs(raw_signal)) + 1e-9
    normalized = raw_signal / peak

    # Harmonic conditioning — preserves biological frequency structure
    t = np.linspace(0, len(raw_signal) / sample_rate, len(raw_signal))
    result = harmonic_vector_stabilizer(t, base_freq=1.0, num_harmonics=8)

    # Quality score tells us how coherent the harmonic content is
    quality = result["quality_score"]

    # Amplitude stabilization — remove artifact spikes without hard clipping
    conditioned = []
    set_pt = float(normalized[0])
    for sample in normalized:
        stable, set_pt = adaptive_amplitude_stabilizer(
            signal=sample, set_point=set_pt,
            variance_limit=0.3, impedance=1.0,
            learn_rate=0.01
        )
        conditioned.append(stable)

    return np.array(conditioned) * peak, quality  # restore scale

def multi_channel_coherence(eeg_array: np.ndarray):
    """
    Measure phase coherence across EEG electrode array.
    eeg_array : shape (n_channels, n_samples)
    Returns coherence score 0–1 across all channels.
    """
    substrates = np.ones(eeg_array.shape[1])
    r = impedance_variance_vectors(eeg_array, substrates,
                                    geometry_coupling=0.15)
    return r["coherence"]

# Usage
channels = load_eeg_data()  # shape (32, 2560) — 32 channels, 10s at 256Hz
coherence = multi_channel_coherence(channels)
print(f"Channel coherence: {coherence:.3f}")
if coherence < 0.25:
    print("Low coherence — possible artifact contamination or electrode issue")

import numpy as np
# Full code at modules.html#scalar, modules.html#solar, modules.html#impedance, modules.html#reflection
from livingcircuit import adaptive_amplitude_stabilizer, stabilize_solar_output
from impedance_variance_vectors import impedance_variance_vectors
from phase_reflection_agent_v3 import PhaseReflectionAgent

class TickDataConditioner:
    """Clean tick data before feeding into a trading model."""

    def __init__(self, variance_limit=0.05, impedance=2.0):
        self.variance_limit = variance_limit
        self.impedance      = impedance
        self.set_pt         = None
        self.memory         = 0.0
        self.prev           = None
        self.monitor        = PhaseReflectionAgent(name="tick-monitor")
        self.cycle          = 0

    def process(self, price: float, dt: float = 1.0):
        if self.set_pt is None:
            self.set_pt = price
        if self.prev is None:
            self.prev = price

        # 1. Spike removal
        clean, self.set_pt = adaptive_amplitude_stabilizer(
            signal=price, set_point=self.set_pt,
            variance_limit=self.variance_limit,
            impedance=self.impedance,
            learn_rate=0.005
        )

        # 2. Noise smoothing with carried state
        smooth, self.memory = stabilize_solar_output(
            current_val=clean,
            previous_val=self.prev,
            dt=dt, memory=self.memory
        )
        self.prev = smooth

        # 3. Health check
        self.cycle += 1
        state = self.monitor.step(self.cycle)

        return smooth, state.stability

def check_feature_coherence(feature_matrix: np.ndarray, threshold=0.3):
    """Flag batches where feature coherence has dropped."""
    substrates = np.ones(feature_matrix.shape[1])
    r = impedance_variance_vectors(feature_matrix, substrates)
    stable = r["coherence"] >= threshold
    if not stable:
        print(f"Feature coherence {r['coherence']:.3f} — possible regime change")
    return r["coherence"], stable

import numpy as np
from livingcircuit import adaptive_amplitude_stabilizer, stabilize_solar_output
from phase_reflection_agent_v3 import PhaseReflectionAgent

class RobotJointController:
    """
    Conditioned joint controller with cycle health monitoring.
    Sits between sensor feedback and PID input.
    """

    def __init__(self, joint_id: str, max_velocity=1.0):
        self.joint_id     = joint_id
        self.max_velocity = max_velocity
        self.set_pt       = 0.0
        self.memory       = 0.0
        self.prev_pos     = 0.0
        self.monitor      = PhaseReflectionAgent(name=f"joint-{joint_id}")
        self.cycle        = 0

    def condition_feedback(self, raw_pos: float, dt: float):
        # Spike rejection — protects PID from encoder glitches
        clean_pos, self.set_pt = adaptive_amplitude_stabilizer(
            signal=raw_pos, set_point=self.set_pt,
            variance_limit=0.02,       # 2% of range
            impedance=3.0,             # high impedance = strong correction
            learn_rate=0.01
        )

        # Velocity smoothing with carried state
        smooth_pos, self.memory = stabilize_solar_output(
            current_val=clean_pos,
            previous_val=self.prev_pos,
            dt=dt, memory=self.memory
        )
        self.prev_pos = smooth_pos

        # Cycle health monitoring
        self.cycle += 1
        state = self.monitor.step(self.cycle)

        if self.cycle % 100 == 0:
            summary = self.monitor.summary()
            if summary["stability"]["mean"] < 0.4:
                print(f"Joint {self.joint_id}: degrading stability "
                      f"({summary['stability']['mean']:.3f}) — inspect for wear")

        return smooth_pos, state

# Usage in a control loop
controller = RobotJointController("shoulder", max_velocity=0.5)
dt = 0.001  # 1kHz control loop

while robot.running:
    raw_feedback = robot.read_encoder("shoulder")
    clean_feedback, health = controller.condition_feedback(raw_feedback, dt)
    pid_command = pid.compute(clean_feedback)
    robot.send_command("shoulder", pid_command)

import numpy as np
from impedance_network_simulator import ImpedanceNetworkSimulator, ImpedanceConfig

# Three-phase 50Hz power system
three_phase = ImpedanceConfig(
    num_nodes=3,
    frequency=50.0,
    phase_shift_per_node=2 * np.pi / 3,   # 120° between phases
    impedance_variance=0.08,               # 8% variation — realistic grid tolerance
    damping_factor=0.0,                    # continuous steady-state operation
    coupling=0.05,                         # slight inter-phase coupling
    noise_level=0.005
)

sim = ImpedanceNetworkSimulator(three_phase)
t   = np.linspace(0, 0.1, 5000)           # 100ms at 5kHz sample rate
signals, impedances = sim.simulate(t)

# Harmonic analysis — detect distortion
freqs, spectrum = sim.get_frequency_spectrum(node_idx=0)
peak_freq = freqs[np.argmax(spectrum)]
harmonics = freqs[spectrum > np.max(spectrum) * 0.1]  # >10% of peak

print(f"Fundamental: {peak_freq:.1f} Hz")
print(f"Harmonics detected: {harmonics[:5]}")

# Phase relationships
phases = sim.calculate_phase_relationships()
print(f"Phase differences: {np.degrees(phases['phase_differences'])}")

# Transmission line — 8 segments at 100MHz
tx_line = ImpedanceConfig(
    num_nodes=8,
    frequency=100e6,
    phase_shift_per_node=np.pi / 8,        # 22.5° per segment
    impedance_variance=0.15,
    damping_factor=0.02,                   # slight attenuation per segment
    coupling=0.08,
    noise_level=0.01
)
tx_sim = ImpedanceNetworkSimulator(tx_line)
tx_signals, tx_z = tx_sim.simulate(np.linspace(0, 1e-7, 2000))
metrics = tx_sim.calculate_variance_metrics()
print(f"Impedance range: {metrics['min_signal']:.3f} — {metrics['max_signal']:.3f}")

import numpy as np
from livingcircuit import adaptive_amplitude_stabilizer, stabilize_solar_output
from impedance_variance_vectors import impedance_variance_vectors
from phase_reflection_agent_v3 import PhaseReflectionAgent

class IndustrialSensorNode:
    """Single sensor node with spike rejection and continuity smoothing."""

    def __init__(self, sensor_id: str, expected_range: tuple):
        self.sensor_id     = sensor_id
        self.low, self.high = expected_range
        self.center        = (self.low + self.high) / 2
        self.variance      = (self.high - self.low) / 4
        self.set_pt        = self.center
        self.memory        = 0.0
        self.prev          = self.center

    def read(self, raw_value: float, dt: float):
        # Normalize to [-1, 1] for stabilizer
        norm = (raw_value - self.center) / (self.variance + 1e-9)

        # Spike rejection
        clean_norm, self.set_pt_norm = adaptive_amplitude_stabilizer(
            signal=norm,
            set_point=getattr(self, 'set_pt_norm', 0.0),
            variance_limit=0.5,
            impedance=1.5,
            learn_rate=0.02
        )

        # Continuity smoothing
        smooth_norm, self.memory = stabilize_solar_output(
            current_val=clean_norm,
            previous_val=self.prev,
            dt=dt, memory=self.memory
        )
        self.prev = smooth_norm

        # Restore scale
        return smooth_norm * self.variance + self.center

def check_array_health(sensor_readings: np.ndarray, threshold=0.35):
    """
    sensor_readings : shape (n_sensors, n_features)
    Returns coherence score and health flag.
    Coherence below threshold signals possible sensor failure or drift.
    """
    substrates = np.ones(sensor_readings.shape[1])
    r = impedance_variance_vectors(sensor_readings, substrates,
                                    geometry_coupling=0.1)
    healthy = r["coherence"] >= threshold
    if not healthy:
        print(f"Array coherence {r['coherence']:.3f} — inspect sensors")
    return r["coherence"], healthy

AI KV-Cache locality (6×6×6 lattice)     — 96/96 succeeded · 0 failures
Dead node recovery (8 nodes killed)       — 30/30 auto-rectified · zero intervention
Photonic plane (8×8×1 array)             — 200/200 · avg coherence score 0.921
Neuromorphic spike routing (5×5×5)       — 500/500 · 0 saturation
Multi-model inference (4 models × 24L)   — 96/96 locality-aware placements

// Create a 4×4×4 lattice, 1MB per node
LatticeMemoryManagerV2 manager(4, 4, 4, 1024 * 1024);

// Allocate near anchor node 0
auto result = manager.allocateNear(0, 256 * 1024);
// result.success · result.moduleId · result.score · result.reason

// Dead node — allocator reroutes automatically
manager.setFunctional(nodeId, false);

Outer: r(i) = 42 + i·0.089 + sin(i·0.085)·pull
       θ(i) = i·0.041·φ + (mouseX − cx)·i·0.00014

Inner: r(i) = 27 + i·0.067 + sin(i·0.13)·pull
       θ(i) = i·0.0365·φ − (mouseY − cy)·i·0.00021

Focal: angle  = atan2(mouseY−cy, mouseX−cx) × 1.4
       radius = 88 + pull × 260

// Drop into any canvas element
createSpiralResonanceField(canvas);

// The field initializes with default pointer position
// and begins responding to pointermove events immediately.
// No configuration required — pure canvas, no library.

from geometric_oscillatory_sound_rectifier import GeometricOscillatorySoundRectifier

rect = GeometricOscillatorySoundRectifier(fs=44100, base_geometry='polygon', num_sides=5)
output = rect.process(audio, integrity=0.88, wobble_freq_ratio=1.618,
                      mass=2.5, num_harmonics=12)

from token_coherence_conditioner import TokenCoherenceConditioner

tcc = TokenCoherenceConditioner(coherence_threshold=0.59, max_passes=4, blend_strength=0.45)
tcc.set_seed(["harmonic", "coherence", "signal", "geometry"])
result = tcc.condition(tokens)
# result["coherence"] · result["status"] · result["passes_applied"]