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feat: Complete Rust port of WiFi-DensePose with modular crates

Browse Source 
Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
This commit is contained in:
Claude
2026-01-13 03:11:16 +00:00
parent 5101504b72
commit 6ed69a3d48
427 changed files with 90993 additions and 0 deletions
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rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/Cargo.toml Normal file
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[package]
name = "wifi-densepose-signal"
version.workspace = true
edition.workspace = true
description = "WiFi CSI signal processing for DensePose estimation"
license.workspace = true
[dependencies]
# Core utilities
thiserror.workspace = true
serde = { workspace = true }
serde_json.workspace = true
chrono = { version = "0.4", features = ["serde"] }
# Signal processing
ndarray = { workspace = true }
rustfft.workspace = true
num-complex.workspace = true
num-traits.workspace = true
# Internal
wifi-densepose-core = { path = "../wifi-densepose-core" }
[dev-dependencies]
criterion.workspace = true
proptest.workspace = true

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rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/csi_processor.rs Normal file
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//! CSI (Channel State Information) Processor
//!
//! This module provides functionality for preprocessing and processing CSI data
//! from WiFi signals for human pose estimation.
use chrono::{DateTime, Utc};
use ndarray::Array2;
use num_complex::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::VecDeque;
use std::f64::consts::PI;
use thiserror::Error;
/// Errors that can occur during CSI processing
#[derive(Debug, Error)]
pub enum CsiProcessorError {
/// Invalid configuration parameters
#[error("Invalid configuration: {0}")]
InvalidConfig(String),
/// Preprocessing failed
#[error("Preprocessing failed: {0}")]
PreprocessingFailed(String),
/// Feature extraction failed
#[error("Feature extraction failed: {0}")]
FeatureExtractionFailed(String),
/// Invalid input data
#[error("Invalid input data: {0}")]
InvalidData(String),
/// Processing pipeline error
#[error("Pipeline error: {0}")]
PipelineError(String),
}
/// CSI data structure containing raw channel measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CsiData {
/// Timestamp of the measurement
pub timestamp: DateTime<Utc>,
/// Amplitude values (num_antennas x num_subcarriers)
pub amplitude: Array2<f64>,
/// Phase values in radians (num_antennas x num_subcarriers)
pub phase: Array2<f64>,
/// Center frequency in Hz
pub frequency: f64,
/// Bandwidth in Hz
pub bandwidth: f64,
/// Number of subcarriers
pub num_subcarriers: usize,
/// Number of antennas
pub num_antennas: usize,
/// Signal-to-noise ratio in dB
pub snr: f64,
/// Additional metadata
#[serde(default)]
pub metadata: CsiMetadata,
}
/// Metadata associated with CSI data
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct CsiMetadata {
/// Whether noise filtering has been applied
pub noise_filtered: bool,
/// Whether windowing has been applied
pub windowed: bool,
/// Whether normalization has been applied
pub normalized: bool,
/// Additional custom metadata
#[serde(flatten)]
pub custom: std::collections::HashMap<String, serde_json::Value>,
}
/// Builder for CsiData
#[derive(Debug, Default)]
pub struct CsiDataBuilder {
timestamp: Option<DateTime<Utc>>,
amplitude: Option<Array2<f64>>,
phase: Option<Array2<f64>>,
frequency: Option<f64>,
bandwidth: Option<f64>,
snr: Option<f64>,
metadata: CsiMetadata,
}
impl CsiDataBuilder {
/// Create a new builder
pub fn new() -> Self {
Self::default()
}
/// Set the timestamp
pub fn timestamp(mut self, timestamp: DateTime<Utc>) -> Self {
self.timestamp = Some(timestamp);
self
}
/// Set amplitude data
pub fn amplitude(mut self, amplitude: Array2<f64>) -> Self {
self.amplitude = Some(amplitude);
self
}
/// Set phase data
pub fn phase(mut self, phase: Array2<f64>) -> Self {
self.phase = Some(phase);
self
}
/// Set center frequency
pub fn frequency(mut self, frequency: f64) -> Self {
self.frequency = Some(frequency);
self
}
/// Set bandwidth
pub fn bandwidth(mut self, bandwidth: f64) -> Self {
self.bandwidth = Some(bandwidth);
self
}
/// Set SNR
pub fn snr(mut self, snr: f64) -> Self {
self.snr = Some(snr);
self
}
/// Set metadata
pub fn metadata(mut self, metadata: CsiMetadata) -> Self {
self.metadata = metadata;
self
}
/// Build the CsiData
pub fn build(self) -> Result<CsiData, CsiProcessorError> {
let amplitude = self
.amplitude
.ok_or_else(|| CsiProcessorError::InvalidData("Amplitude data is required".into()))?;
let phase = self
.phase
.ok_or_else(|| CsiProcessorError::InvalidData("Phase data is required".into()))?;
if amplitude.shape() != phase.shape() {
return Err(CsiProcessorError::InvalidData(
"Amplitude and phase must have the same shape".into(),
));
}
let (num_antennas, num_subcarriers) = amplitude.dim();
Ok(CsiData {
timestamp: self.timestamp.unwrap_or_else(Utc::now),
amplitude,
phase,
frequency: self.frequency.unwrap_or(5.0e9), // Default 5 GHz
bandwidth: self.bandwidth.unwrap_or(20.0e6), // Default 20 MHz
num_subcarriers,
num_antennas,
snr: self.snr.unwrap_or(20.0),
metadata: self.metadata,
})
}
}
impl CsiData {
/// Create a new CsiData builder
pub fn builder() -> CsiDataBuilder {
CsiDataBuilder::new()
}
/// Get complex CSI values
pub fn to_complex(&self) -> Array2<Complex64> {
let mut complex = Array2::zeros(self.amplitude.dim());
for ((i, j), amp) in self.amplitude.indexed_iter() {
let phase = self.phase[[i, j]];
complex[[i, j]] = Complex64::from_polar(*amp, phase);
}
complex
}
/// Create from complex values
pub fn from_complex(
complex: &Array2<Complex64>,
frequency: f64,
bandwidth: f64,
) -> Result<Self, CsiProcessorError> {
let (num_antennas, num_subcarriers) = complex.dim();
let mut amplitude = Array2::zeros(complex.dim());
let mut phase = Array2::zeros(complex.dim());
for ((i, j), c) in complex.indexed_iter() {
amplitude[[i, j]] = c.norm();
phase[[i, j]] = c.arg();
}
Ok(Self {
timestamp: Utc::now(),
amplitude,
phase,
frequency,
bandwidth,
num_subcarriers,
num_antennas,
snr: 20.0,
metadata: CsiMetadata::default(),
})
}
}
/// Configuration for CSI processor
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CsiProcessorConfig {
/// Sampling rate in Hz
pub sampling_rate: f64,
/// Window size for processing
pub window_size: usize,
/// Overlap fraction (0.0 to 1.0)
pub overlap: f64,
/// Noise threshold in dB
pub noise_threshold: f64,
/// Human detection threshold (0.0 to 1.0)
pub human_detection_threshold: f64,
/// Temporal smoothing factor (0.0 to 1.0)
pub smoothing_factor: f64,
/// Maximum history size
pub max_history_size: usize,
/// Enable preprocessing
pub enable_preprocessing: bool,
/// Enable feature extraction
pub enable_feature_extraction: bool,
/// Enable human detection
pub enable_human_detection: bool,
}
impl Default for CsiProcessorConfig {
fn default() -> Self {
Self {
sampling_rate: 1000.0,
window_size: 256,
overlap: 0.5,
noise_threshold: -30.0,
human_detection_threshold: 0.8,
smoothing_factor: 0.9,
max_history_size: 500,
enable_preprocessing: true,
enable_feature_extraction: true,
enable_human_detection: true,
}
}
}
/// Builder for CsiProcessorConfig
#[derive(Debug, Default)]
pub struct CsiProcessorConfigBuilder {
config: CsiProcessorConfig,
}
impl CsiProcessorConfigBuilder {
/// Create a new builder
pub fn new() -> Self {
Self {
config: CsiProcessorConfig::default(),
}
}
/// Set sampling rate
pub fn sampling_rate(mut self, rate: f64) -> Self {
self.config.sampling_rate = rate;
self
}
/// Set window size
pub fn window_size(mut self, size: usize) -> Self {
self.config.window_size = size;
self
}
/// Set overlap fraction
pub fn overlap(mut self, overlap: f64) -> Self {
self.config.overlap = overlap;
self
}
/// Set noise threshold
pub fn noise_threshold(mut self, threshold: f64) -> Self {
self.config.noise_threshold = threshold;
self
}
/// Set human detection threshold
pub fn human_detection_threshold(mut self, threshold: f64) -> Self {
self.config.human_detection_threshold = threshold;
self
}
/// Set smoothing factor
pub fn smoothing_factor(mut self, factor: f64) -> Self {
self.config.smoothing_factor = factor;
self
}
/// Set max history size
pub fn max_history_size(mut self, size: usize) -> Self {
self.config.max_history_size = size;
self
}
/// Enable/disable preprocessing
pub fn enable_preprocessing(mut self, enable: bool) -> Self {
self.config.enable_preprocessing = enable;
self
}
/// Enable/disable feature extraction
pub fn enable_feature_extraction(mut self, enable: bool) -> Self {
self.config.enable_feature_extraction = enable;
self
}
/// Enable/disable human detection
pub fn enable_human_detection(mut self, enable: bool) -> Self {
self.config.enable_human_detection = enable;
self
}
/// Build the configuration
pub fn build(self) -> CsiProcessorConfig {
self.config
}
}
impl CsiProcessorConfig {
/// Create a new config builder
pub fn builder() -> CsiProcessorConfigBuilder {
CsiProcessorConfigBuilder::new()
}
/// Validate configuration
pub fn validate(&self) -> Result<(), CsiProcessorError> {
if self.sampling_rate <= 0.0 {
return Err(CsiProcessorError::InvalidConfig(
"sampling_rate must be positive".into(),
));
}
if self.window_size == 0 {
return Err(CsiProcessorError::InvalidConfig(
"window_size must be positive".into(),
));
}
if !(0.0..1.0).contains(&self.overlap) {
return Err(CsiProcessorError::InvalidConfig(
"overlap must be between 0 and 1".into(),
));
}
Ok(())
}
}
/// CSI Preprocessor for cleaning and preparing raw CSI data
#[derive(Debug)]
pub struct CsiPreprocessor {
noise_threshold: f64,
}
impl CsiPreprocessor {
/// Create a new preprocessor
pub fn new(noise_threshold: f64) -> Self {
Self { noise_threshold }
}
/// Remove noise from CSI data based on amplitude threshold
pub fn remove_noise(&self, csi_data: &CsiData) -> Result<CsiData, CsiProcessorError> {
// Convert amplitude to dB
let amplitude_db = csi_data.amplitude.mapv(|a| 20.0 * (a + 1e-12).log10());
// Create noise mask
let noise_mask = amplitude_db.mapv(|db| db > self.noise_threshold);
// Apply mask to amplitude
let mut filtered_amplitude = csi_data.amplitude.clone();
for ((i, j), &mask) in noise_mask.indexed_iter() {
if !mask {
filtered_amplitude[[i, j]] = 0.0;
}
}
let mut metadata = csi_data.metadata.clone();
metadata.noise_filtered = true;
Ok(CsiData {
timestamp: csi_data.timestamp,
amplitude: filtered_amplitude,
phase: csi_data.phase.clone(),
frequency: csi_data.frequency,
bandwidth: csi_data.bandwidth,
num_subcarriers: csi_data.num_subcarriers,
num_antennas: csi_data.num_antennas,
snr: csi_data.snr,
metadata,
})
}
/// Apply Hamming window to reduce spectral leakage
pub fn apply_windowing(&self, csi_data: &CsiData) -> Result<CsiData, CsiProcessorError> {
let n = csi_data.num_subcarriers;
let window = Self::hamming_window(n);
// Apply window to each antenna's amplitude
let mut windowed_amplitude = csi_data.amplitude.clone();
for mut row in windowed_amplitude.rows_mut() {
for (i, val) in row.iter_mut().enumerate() {
*val *= window[i];
}
}
let mut metadata = csi_data.metadata.clone();
metadata.windowed = true;
Ok(CsiData {
timestamp: csi_data.timestamp,
amplitude: windowed_amplitude,
phase: csi_data.phase.clone(),
frequency: csi_data.frequency,
bandwidth: csi_data.bandwidth,
num_subcarriers: csi_data.num_subcarriers,
num_antennas: csi_data.num_antennas,
snr: csi_data.snr,
metadata,
})
}
/// Normalize amplitude values to unit variance
pub fn normalize_amplitude(&self, csi_data: &CsiData) -> Result<CsiData, CsiProcessorError> {
let std_dev = self.calculate_std(&csi_data.amplitude);
let normalized_amplitude = csi_data.amplitude.mapv(|a| a / (std_dev + 1e-12));
let mut metadata = csi_data.metadata.clone();
metadata.normalized = true;
Ok(CsiData {
timestamp: csi_data.timestamp,
amplitude: normalized_amplitude,
phase: csi_data.phase.clone(),
frequency: csi_data.frequency,
bandwidth: csi_data.bandwidth,
num_subcarriers: csi_data.num_subcarriers,
num_antennas: csi_data.num_antennas,
snr: csi_data.snr,
metadata,
})
}
/// Generate Hamming window
fn hamming_window(n: usize) -> Vec<f64> {
(0..n)
.map(|i| 0.54 - 0.46 * (2.0 * PI * i as f64 / (n - 1) as f64).cos())
.collect()
}
/// Calculate standard deviation
fn calculate_std(&self, arr: &Array2<f64>) -> f64 {
let mean = arr.mean().unwrap_or(0.0);
let variance = arr.mapv(|x| (x - mean).powi(2)).mean().unwrap_or(0.0);
variance.sqrt()
}
}
/// Statistics for CSI processing
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ProcessingStatistics {
/// Total number of samples processed
pub total_processed: usize,
/// Number of processing errors
pub processing_errors: usize,
/// Number of human detections
pub human_detections: usize,
/// Current history size
pub history_size: usize,
}
impl ProcessingStatistics {
/// Calculate error rate
pub fn error_rate(&self) -> f64 {
if self.total_processed > 0 {
self.processing_errors as f64 / self.total_processed as f64
} else {
0.0
}
}
/// Calculate detection rate
pub fn detection_rate(&self) -> f64 {
if self.total_processed > 0 {
self.human_detections as f64 / self.total_processed as f64
} else {
0.0
}
}
}
/// Main CSI Processor for WiFi-DensePose
#[derive(Debug)]
pub struct CsiProcessor {
config: CsiProcessorConfig,
preprocessor: CsiPreprocessor,
history: VecDeque<CsiData>,
previous_detection_confidence: f64,
statistics: ProcessingStatistics,
}
impl CsiProcessor {
/// Create a new CSI processor
pub fn new(config: CsiProcessorConfig) -> Result<Self, CsiProcessorError> {
config.validate()?;
let preprocessor = CsiPreprocessor::new(config.noise_threshold);
Ok(Self {
history: VecDeque::with_capacity(config.max_history_size),
config,
preprocessor,
previous_detection_confidence: 0.0,
statistics: ProcessingStatistics::default(),
})
}
/// Get the configuration
pub fn config(&self) -> &CsiProcessorConfig {
&self.config
}
/// Preprocess CSI data
pub fn preprocess(&self, csi_data: &CsiData) -> Result<CsiData, CsiProcessorError> {
if !self.config.enable_preprocessing {
return Ok(csi_data.clone());
}
// Remove noise
let cleaned = self.preprocessor.remove_noise(csi_data)?;
// Apply windowing
let windowed = self.preprocessor.apply_windowing(&cleaned)?;
// Normalize amplitude
let normalized = self.preprocessor.normalize_amplitude(&windowed)?;
Ok(normalized)
}
/// Add CSI data to history
pub fn add_to_history(&mut self, csi_data: CsiData) {
if self.history.len() >= self.config.max_history_size {
self.history.pop_front();
}
self.history.push_back(csi_data);
self.statistics.history_size = self.history.len();
}
/// Clear history
pub fn clear_history(&mut self) {
self.history.clear();
self.statistics.history_size = 0;
}
/// Get recent history
pub fn get_recent_history(&self, count: usize) -> Vec<&CsiData> {
let len = self.history.len();
if count >= len {
self.history.iter().collect()
} else {
self.history.iter().skip(len - count).collect()
}
}
/// Get history length
pub fn history_len(&self) -> usize {
self.history.len()
}
/// Apply temporal smoothing (exponential moving average)
pub fn apply_temporal_smoothing(&mut self, raw_confidence: f64) -> f64 {
let smoothed = self.config.smoothing_factor * self.previous_detection_confidence
+ (1.0 - self.config.smoothing_factor) * raw_confidence;
self.previous_detection_confidence = smoothed;
smoothed
}
/// Get processing statistics
pub fn get_statistics(&self) -> &ProcessingStatistics {
&self.statistics
}
/// Reset statistics
pub fn reset_statistics(&mut self) {
self.statistics = ProcessingStatistics::default();
}
/// Increment total processed count
pub fn increment_processed(&mut self) {
self.statistics.total_processed += 1;
}
/// Increment error count
pub fn increment_errors(&mut self) {
self.statistics.processing_errors += 1;
}
/// Increment human detection count
pub fn increment_detections(&mut self) {
self.statistics.human_detections += 1;
}
/// Get previous detection confidence
pub fn previous_confidence(&self) -> f64 {
self.previous_detection_confidence
}
}
#[cfg(test)]
mod tests {
use super::*;
use ndarray::Array2;
fn create_test_csi_data() -> CsiData {
let amplitude = Array2::from_shape_fn((4, 64), |(i, j)| {
1.0 + 0.1 * ((i + j) as f64).sin()
});
let phase = Array2::from_shape_fn((4, 64), |(i, j)| {
0.5 * ((i + j) as f64 * 0.1).sin()
});
CsiData::builder()
.amplitude(amplitude)
.phase(phase)
.frequency(5.0e9)
.bandwidth(20.0e6)
.snr(25.0)
.build()
.unwrap()
}
#[test]
fn test_config_validation() {
let config = CsiProcessorConfig::builder()
.sampling_rate(1000.0)
.window_size(256)
.overlap(0.5)
.build();
assert!(config.validate().is_ok());
}
#[test]
fn test_invalid_config() {
let config = CsiProcessorConfig::builder()
.sampling_rate(-100.0)
.build();
assert!(config.validate().is_err());
}
#[test]
fn test_csi_processor_creation() {
let config = CsiProcessorConfig::default();
let processor = CsiProcessor::new(config);
assert!(processor.is_ok());
}
#[test]
fn test_preprocessing() {
let config = CsiProcessorConfig::default();
let processor = CsiProcessor::new(config).unwrap();
let csi_data = create_test_csi_data();
let result = processor.preprocess(&csi_data);
assert!(result.is_ok());
let preprocessed = result.unwrap();
assert!(preprocessed.metadata.noise_filtered);
assert!(preprocessed.metadata.windowed);
assert!(preprocessed.metadata.normalized);
}
#[test]
fn test_history_management() {
let config = CsiProcessorConfig::builder()
.max_history_size(5)
.build();
let mut processor = CsiProcessor::new(config).unwrap();
for _ in 0..10 {
let csi_data = create_test_csi_data();
processor.add_to_history(csi_data);
}
assert_eq!(processor.history_len(), 5);
}
#[test]
fn test_temporal_smoothing() {
let config = CsiProcessorConfig::builder()
.smoothing_factor(0.9)
.build();
let mut processor = CsiProcessor::new(config).unwrap();
let smoothed1 = processor.apply_temporal_smoothing(1.0);
assert!((smoothed1 - 0.1).abs() < 1e-6);
let smoothed2 = processor.apply_temporal_smoothing(1.0);
assert!(smoothed2 > smoothed1);
}
#[test]
fn test_csi_data_builder() {
let amplitude = Array2::ones((4, 64));
let phase = Array2::zeros((4, 64));
let csi_data = CsiData::builder()
.amplitude(amplitude)
.phase(phase)
.frequency(2.4e9)
.bandwidth(40.0e6)
.snr(30.0)
.build();
assert!(csi_data.is_ok());
let data = csi_data.unwrap();
assert_eq!(data.num_antennas, 4);
assert_eq!(data.num_subcarriers, 64);
}
#[test]
fn test_complex_conversion() {
let csi_data = create_test_csi_data();
let complex = csi_data.to_complex();
assert_eq!(complex.dim(), (4, 64));
for ((i, j), c) in complex.indexed_iter() {
let expected_amp = csi_data.amplitude[[i, j]];
let expected_phase = csi_data.phase[[i, j]];
let c_val: num_complex::Complex64 = *c;
assert!((c_val.norm() - expected_amp).abs() < 1e-10);
assert!((c_val.arg() - expected_phase).abs() < 1e-10);
}
}
#[test]
fn test_hamming_window() {
let window = CsiPreprocessor::hamming_window(64);
assert_eq!(window.len(), 64);
// Hamming window should be symmetric
for i in 0..32 {
assert!((window[i] - window[63 - i]).abs() < 1e-10);
}
// First and last values should be approximately 0.08
assert!((window[0] - 0.08).abs() < 0.01);
}
}

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rust-port/wifi-densepose-rs/crates/wifi-densepose-signal/src/features.rs Normal file
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//! Feature Extraction Module
//!
//! This module provides feature extraction capabilities for CSI data,
//! including amplitude, phase, correlation, Doppler, and power spectral density features.
use crate::csi_processor::CsiData;
use chrono::{DateTime, Utc};
use ndarray::{Array1, Array2};
use num_complex::Complex64;
use rustfft::FftPlanner;
use serde::{Deserialize, Serialize};
/// Amplitude-based features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AmplitudeFeatures {
/// Mean amplitude across antennas for each subcarrier
pub mean: Array1<f64>,
/// Variance of amplitude across antennas for each subcarrier
pub variance: Array1<f64>,
/// Peak amplitude value
pub peak: f64,
/// RMS amplitude
pub rms: f64,
/// Dynamic range (max - min)
pub dynamic_range: f64,
}
impl AmplitudeFeatures {
/// Extract amplitude features from CSI data
pub fn from_csi_data(csi_data: &CsiData) -> Self {
let amplitude = &csi_data.amplitude;
let (nrows, ncols) = amplitude.dim();
// Calculate mean across antennas (axis 0)
let mut mean = Array1::zeros(ncols);
for j in 0..ncols {
let mut sum = 0.0;
for i in 0..nrows {
sum += amplitude[[i, j]];
}
mean[j] = sum / nrows as f64;
}
// Calculate variance across antennas
let mut variance = Array1::zeros(ncols);
for j in 0..ncols {
let mut var_sum = 0.0;
for i in 0..nrows {
var_sum += (amplitude[[i, j]] - mean[j]).powi(2);
}
variance[j] = var_sum / nrows as f64;
}
// Calculate global statistics
let flat: Vec<f64> = amplitude.iter().copied().collect();
let peak = flat.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
let min_val = flat.iter().cloned().fold(f64::INFINITY, f64::min);
let dynamic_range = peak - min_val;
let rms = (flat.iter().map(|x| x * x).sum::<f64>() / flat.len() as f64).sqrt();
Self {
mean,
variance,
peak,
rms,
dynamic_range,
}
}
}
/// Phase-based features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhaseFeatures {
/// Phase differences between adjacent subcarriers (mean across antennas)
pub difference: Array1<f64>,
/// Phase variance across subcarriers
pub variance: Array1<f64>,
/// Phase gradient (rate of change)
pub gradient: Array1<f64>,
/// Phase coherence measure
pub coherence: f64,
}
impl PhaseFeatures {
/// Extract phase features from CSI data
pub fn from_csi_data(csi_data: &CsiData) -> Self {
let phase = &csi_data.phase;
let (nrows, ncols) = phase.dim();
// Calculate phase differences between adjacent subcarriers
let mut diff_matrix = Array2::zeros((nrows, ncols.saturating_sub(1)));
for i in 0..nrows {
for j in 0..ncols.saturating_sub(1) {
diff_matrix[[i, j]] = phase[[i, j + 1]] - phase[[i, j]];
}
}
// Mean phase difference across antennas
let mut difference = Array1::zeros(ncols.saturating_sub(1));
for j in 0..ncols.saturating_sub(1) {
let mut sum = 0.0;
for i in 0..nrows {
sum += diff_matrix[[i, j]];
}
difference[j] = sum / nrows as f64;
}
// Phase variance per subcarrier
let mut variance = Array1::zeros(ncols);
for j in 0..ncols {
let mut col_sum = 0.0;
for i in 0..nrows {
col_sum += phase[[i, j]];
}
let mean = col_sum / nrows as f64;
let mut var_sum = 0.0;
for i in 0..nrows {
var_sum += (phase[[i, j]] - mean).powi(2);
}
variance[j] = var_sum / nrows as f64;
}
// Calculate gradient (second order differences)
let gradient = if ncols >= 3 {
let mut grad = Array1::zeros(ncols.saturating_sub(2));
for j in 0..ncols.saturating_sub(2) {
grad[j] = difference[j + 1] - difference[j];
}
grad
} else {
Array1::zeros(1)
};
// Phase coherence (measure of phase stability)
let coherence = Self::calculate_coherence(phase);
Self {
difference,
variance,
gradient,
coherence,
}
}
/// Calculate phase coherence
fn calculate_coherence(phase: &Array2<f64>) -> f64 {
let (nrows, ncols) = phase.dim();
if nrows < 2 || ncols == 0 {
return 0.0;
}
// Calculate coherence as the mean of cross-antenna phase correlation
let mut coherence_sum = 0.0;
let mut count = 0;
for i in 0..nrows {
for k in (i + 1)..nrows {
// Calculate correlation between antenna pairs
let row_i: Vec<f64> = phase.row(i).to_vec();
let row_k: Vec<f64> = phase.row(k).to_vec();
let mean_i: f64 = row_i.iter().sum::<f64>() / ncols as f64;
let mean_k: f64 = row_k.iter().sum::<f64>() / ncols as f64;
let mut cov = 0.0;
let mut var_i = 0.0;
let mut var_k = 0.0;
for j in 0..ncols {
let diff_i = row_i[j] - mean_i;
let diff_k = row_k[j] - mean_k;
cov += diff_i * diff_k;
var_i += diff_i * diff_i;
var_k += diff_k * diff_k;
}
let std_prod = (var_i * var_k).sqrt();
if std_prod > 1e-10 {
coherence_sum += cov / std_prod;
count += 1;
}
}
}
if count > 0 {
coherence_sum / count as f64
} else {
0.0
}
}
}
/// Correlation features between antennas
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationFeatures {
/// Correlation matrix between antennas
pub matrix: Array2<f64>,
/// Mean off-diagonal correlation
pub mean_correlation: f64,
/// Maximum correlation coefficient
pub max_correlation: f64,
/// Correlation spread (std of off-diagonal elements)
pub correlation_spread: f64,
}
impl CorrelationFeatures {
/// Extract correlation features from CSI data
pub fn from_csi_data(csi_data: &CsiData) -> Self {
let amplitude = &csi_data.amplitude;
let matrix = Self::correlation_matrix(amplitude);
let (n, _) = matrix.dim();
let mut off_diagonal: Vec<f64> = Vec::new();
for i in 0..n {
for j in 0..n {
if i != j {
off_diagonal.push(matrix[[i, j]]);
}
}
}
let mean_correlation = if !off_diagonal.is_empty() {
off_diagonal.iter().sum::<f64>() / off_diagonal.len() as f64
} else {
0.0
};
let max_correlation = off_diagonal
.iter()
.cloned()
.fold(f64::NEG_INFINITY, f64::max);
let correlation_spread = if !off_diagonal.is_empty() {
let var: f64 = off_diagonal
.iter()
.map(|x| (x - mean_correlation).powi(2))
.sum::<f64>()
/ off_diagonal.len() as f64;
var.sqrt()
} else {
0.0
};
Self {
matrix,
mean_correlation,
max_correlation: if max_correlation.is_finite() { max_correlation } else { 0.0 },
correlation_spread,
}
}
/// Compute correlation matrix between rows (antennas)
fn correlation_matrix(data: &Array2<f64>) -> Array2<f64> {
let (nrows, ncols) = data.dim();
let mut corr = Array2::zeros((nrows, nrows));
// Calculate means
let means: Vec<f64> = (0..nrows)
.map(|i| data.row(i).sum() / ncols as f64)
.collect();
// Calculate standard deviations
let stds: Vec<f64> = (0..nrows)
.map(|i| {
let mean = means[i];
let var: f64 = data.row(i).iter().map(|x| (x - mean).powi(2)).sum::<f64>() / ncols as f64;
var.sqrt()
})
.collect();
// Calculate correlation coefficients
for i in 0..nrows {
for j in 0..nrows {
if i == j {
corr[[i, j]] = 1.0;
} else {
let mut cov = 0.0;
for k in 0..ncols {
cov += (data[[i, k]] - means[i]) * (data[[j, k]] - means[j]);
}
cov /= ncols as f64;
let std_prod = stds[i] * stds[j];
corr[[i, j]] = if std_prod > 1e-10 { cov / std_prod } else { 0.0 };
}
}
}
corr
}
}
/// Doppler shift features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DopplerFeatures {
/// Estimated Doppler shifts per subcarrier
pub shifts: Array1<f64>,
/// Peak Doppler frequency
pub peak_frequency: f64,
/// Mean Doppler shift magnitude
pub mean_magnitude: f64,
/// Doppler spread (standard deviation)
pub spread: f64,
}
impl DopplerFeatures {
/// Extract Doppler features from temporal CSI data
pub fn from_csi_history(history: &[CsiData], sampling_rate: f64) -> Self {
if history.is_empty() {
return Self::empty();
}
let num_subcarriers = history[0].num_subcarriers;
let num_samples = history.len();
if num_samples < 2 {
return Self::empty_with_size(num_subcarriers);
}
// Stack amplitude data for each subcarrier across time
let mut shifts = Array1::zeros(num_subcarriers);
let mut fft_planner = FftPlanner::new();
let fft = fft_planner.plan_fft_forward(num_samples);
for j in 0..num_subcarriers {
// Extract time series for this subcarrier (use first antenna)
let mut buffer: Vec<Complex64> = history
.iter()
.map(|csi| Complex64::new(csi.amplitude[[0, j]], 0.0))
.collect();
// Apply FFT
fft.process(&mut buffer);
// Find peak frequency (Doppler shift)
let mut max_mag = 0.0;
let mut max_idx = 0;
for (idx, val) in buffer.iter().enumerate() {
let mag = val.norm();
if mag > max_mag && idx != 0 {
// Skip DC component
max_mag = mag;
max_idx = idx;
}
}
// Convert bin index to frequency
let freq_resolution = sampling_rate / num_samples as f64;
let doppler_freq = if max_idx <= num_samples / 2 {
max_idx as f64 * freq_resolution
} else {
(max_idx as i64 - num_samples as i64) as f64 * freq_resolution
};
shifts[j] = doppler_freq;
}
let magnitudes: Vec<f64> = shifts.iter().map(|x| x.abs()).collect();
let peak_frequency = magnitudes.iter().cloned().fold(0.0, f64::max);
let mean_magnitude = magnitudes.iter().sum::<f64>() / magnitudes.len() as f64;
let spread = {
let var: f64 = magnitudes
.iter()
.map(|x| (x - mean_magnitude).powi(2))
.sum::<f64>()
/ magnitudes.len() as f64;
var.sqrt()
};
Self {
shifts,
peak_frequency,
mean_magnitude,
spread,
}
}
/// Create empty Doppler features
fn empty() -> Self {
Self {
shifts: Array1::zeros(1),
peak_frequency: 0.0,
mean_magnitude: 0.0,
spread: 0.0,
}
}
/// Create empty Doppler features with specified size
fn empty_with_size(size: usize) -> Self {
Self {
shifts: Array1::zeros(size),
peak_frequency: 0.0,
mean_magnitude: 0.0,
spread: 0.0,
}
}
}
/// Power Spectral Density features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PowerSpectralDensity {
/// PSD values (frequency bins)
pub values: Array1<f64>,
/// Frequency bins in Hz
pub frequencies: Array1<f64>,
/// Total power
pub total_power: f64,
/// Peak power
pub peak_power: f64,
/// Peak frequency
pub peak_frequency: f64,
/// Spectral centroid
pub centroid: f64,
/// Spectral bandwidth
pub bandwidth: f64,
}
impl PowerSpectralDensity {
/// Calculate PSD from CSI amplitude data
pub fn from_csi_data(csi_data: &CsiData, fft_size: usize) -> Self {
let amplitude = &csi_data.amplitude;
let flat: Vec<f64> = amplitude.iter().copied().collect();
// Pad or truncate to FFT size
let mut input: Vec<Complex64> = flat
.iter()
.take(fft_size)
.map(|&x| Complex64::new(x, 0.0))
.collect();
while input.len() < fft_size {
input.push(Complex64::new(0.0, 0.0));
}
// Apply FFT
let mut fft_planner = FftPlanner::new();
let fft = fft_planner.plan_fft_forward(fft_size);
fft.process(&mut input);
// Calculate power spectrum
let mut psd = Array1::zeros(fft_size);
for (i, val) in input.iter().enumerate() {
psd[i] = val.norm_sqr() / fft_size as f64;
}
// Calculate frequency bins
let freq_resolution = csi_data.bandwidth / fft_size as f64;
let frequencies: Array1<f64> = (0..fft_size)
.map(|i| {
if i <= fft_size / 2 {
i as f64 * freq_resolution
} else {
(i as i64 - fft_size as i64) as f64 * freq_resolution
}
})
.collect();
// Calculate statistics (use first half for positive frequencies)
let half = fft_size / 2;
let positive_psd: Vec<f64> = psd.iter().take(half).copied().collect();
let positive_freq: Vec<f64> = frequencies.iter().take(half).copied().collect();
let total_power: f64 = positive_psd.iter().sum();
let peak_power = positive_psd.iter().cloned().fold(0.0, f64::max);
let peak_idx = positive_psd
.iter()
.enumerate()
.max_by(|(_, a): &(usize, &f64), (_, b): &(usize, &f64)| a.partial_cmp(b).unwrap())
.map(|(i, _)| i)
.unwrap_or(0);
let peak_frequency = positive_freq[peak_idx];
// Spectral centroid
let centroid = if total_power > 1e-10 {
let weighted_sum: f64 = positive_psd
.iter()
.zip(positive_freq.iter())
.map(|(p, f)| p * f)
.sum();
weighted_sum / total_power
} else {
0.0
};
// Spectral bandwidth (standard deviation around centroid)
let bandwidth = if total_power > 1e-10 {
let weighted_var: f64 = positive_psd
.iter()
.zip(positive_freq.iter())
.map(|(p, f)| p * (f - centroid).powi(2))
.sum();
(weighted_var / total_power).sqrt()
} else {
0.0
};
Self {
values: psd,
frequencies,
total_power,
peak_power,
peak_frequency,
centroid,
bandwidth,
}
}
}
/// Complete CSI features collection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CsiFeatures {
/// Amplitude-based features
pub amplitude: AmplitudeFeatures,
/// Phase-based features
pub phase: PhaseFeatures,
/// Correlation features
pub correlation: CorrelationFeatures,
/// Doppler features (optional, requires history)
pub doppler: Option<DopplerFeatures>,
/// Power spectral density
pub psd: PowerSpectralDensity,
/// Timestamp of feature extraction
pub timestamp: DateTime<Utc>,
/// Source CSI metadata
pub metadata: FeatureMetadata,
}
/// Metadata for extracted features
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct FeatureMetadata {
/// Number of antennas in source data
pub num_antennas: usize,
/// Number of subcarriers in source data
pub num_subcarriers: usize,
/// FFT size used for PSD
pub fft_size: usize,
/// Sampling rate used for Doppler
pub sampling_rate: Option<f64>,
/// Number of samples used for Doppler
pub doppler_samples: Option<usize>,
}
/// Configuration for feature extraction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureExtractorConfig {
/// FFT size for PSD calculation
pub fft_size: usize,
/// Sampling rate for Doppler calculation
pub sampling_rate: f64,
/// Minimum history length for Doppler features
pub min_doppler_history: usize,
/// Enable Doppler feature extraction
pub enable_doppler: bool,
}
impl Default for FeatureExtractorConfig {
fn default() -> Self {
Self {
fft_size: 128,
sampling_rate: 1000.0,
min_doppler_history: 10,
enable_doppler: true,
}
}
}
/// Feature extractor for CSI data
#[derive(Debug)]
pub struct FeatureExtractor {
config: FeatureExtractorConfig,
}
impl FeatureExtractor {
/// Create a new feature extractor
pub fn new(config: FeatureExtractorConfig) -> Self {
Self { config }
}
/// Create with default configuration
pub fn default_config() -> Self {
Self::new(FeatureExtractorConfig::default())
}
/// Get configuration
pub fn config(&self) -> &FeatureExtractorConfig {
&self.config
}
/// Extract features from single CSI sample
pub fn extract(&self, csi_data: &CsiData) -> CsiFeatures {
let amplitude = AmplitudeFeatures::from_csi_data(csi_data);
let phase = PhaseFeatures::from_csi_data(csi_data);
let correlation = CorrelationFeatures::from_csi_data(csi_data);
let psd = PowerSpectralDensity::from_csi_data(csi_data, self.config.fft_size);
let metadata = FeatureMetadata {
num_antennas: csi_data.num_antennas,
num_subcarriers: csi_data.num_subcarriers,
fft_size: self.config.fft_size,
sampling_rate: None,
doppler_samples: None,
};
CsiFeatures {
amplitude,
phase,
correlation,
doppler: None,
psd,
timestamp: Utc::now(),
metadata,
}
}
/// Extract features including Doppler from CSI history
pub fn extract_with_history(&self, csi_data: &CsiData, history: &[CsiData]) -> CsiFeatures {
let mut features = self.extract(csi_data);
if self.config.enable_doppler && history.len() >= self.config.min_doppler_history {
let doppler = DopplerFeatures::from_csi_history(history, self.config.sampling_rate);
features.doppler = Some(doppler);
features.metadata.sampling_rate = Some(self.config.sampling_rate);
features.metadata.doppler_samples = Some(history.len());
}
features
}
/// Extract amplitude features only
pub fn extract_amplitude(&self, csi_data: &CsiData) -> AmplitudeFeatures {
AmplitudeFeatures::from_csi_data(csi_data)
}
/// Extract phase features only
pub fn extract_phase(&self, csi_data: &CsiData) -> PhaseFeatures {
PhaseFeatures::from_csi_data(csi_data)
}
/// Extract correlation features only
pub fn extract_correlation(&self, csi_data: &CsiData) -> CorrelationFeatures {
CorrelationFeatures::from_csi_data(csi_data)
}
/// Extract PSD features only
pub fn extract_psd(&self, csi_data: &CsiData) -> PowerSpectralDensity {
PowerSpectralDensity::from_csi_data(csi_data, self.config.fft_size)
}
/// Extract Doppler features from history
pub fn extract_doppler(&self, history: &[CsiData]) -> Option<DopplerFeatures> {
if history.len() >= self.config.min_doppler_history {
Some(DopplerFeatures::from_csi_history(
history,
self.config.sampling_rate,
))
} else {
None
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use ndarray::Array2;
fn create_test_csi_data() -> CsiData {
let amplitude = Array2::from_shape_fn((4, 64), |(i, j)| {
1.0 + 0.5 * ((i + j) as f64 * 0.1).sin()
});
let phase = Array2::from_shape_fn((4, 64), |(i, j)| {
0.5 * ((i + j) as f64 * 0.15).sin()
});
CsiData::builder()
.amplitude(amplitude)
.phase(phase)
.frequency(5.0e9)
.bandwidth(20.0e6)
.snr(25.0)
.build()
.unwrap()
}
fn create_test_history(n: usize) -> Vec<CsiData> {
(0..n)
.map(|t| {
let amplitude = Array2::from_shape_fn((4, 64), |(i, j)| {
1.0 + 0.3 * ((i + j + t) as f64 * 0.1).sin()
});
let phase = Array2::from_shape_fn((4, 64), |(i, j)| {
0.4 * ((i + j + t) as f64 * 0.12).sin()
});
CsiData::builder()
.amplitude(amplitude)
.phase(phase)
.frequency(5.0e9)
.bandwidth(20.0e6)
.build()
.unwrap()
})
.collect()
}
#[test]
fn test_amplitude_features() {
let csi_data = create_test_csi_data();
let features = AmplitudeFeatures::from_csi_data(&csi_data);
assert_eq!(features.mean.len(), 64);
assert_eq!(features.variance.len(), 64);
assert!(features.peak > 0.0);
assert!(features.rms > 0.0);
assert!(features.dynamic_range >= 0.0);
}
#[test]
fn test_phase_features() {
let csi_data = create_test_csi_data();
let features = PhaseFeatures::from_csi_data(&csi_data);
assert_eq!(features.difference.len(), 63);
assert_eq!(features.variance.len(), 64);
assert!(features.coherence.abs() <= 1.0);
}
#[test]
fn test_correlation_features() {
let csi_data = create_test_csi_data();
let features = CorrelationFeatures::from_csi_data(&csi_data);
assert_eq!(features.matrix.dim(), (4, 4));
// Diagonal should be 1
for i in 0..4 {
assert!((features.matrix[[i, i]] - 1.0).abs() < 1e-10);
}
// Matrix should be symmetric
for i in 0..4 {
for j in 0..4 {
assert!((features.matrix[[i, j]] - features.matrix[[j, i]]).abs() < 1e-10);
}
}
}
#[test]
fn test_psd_features() {
let csi_data = create_test_csi_data();
let psd = PowerSpectralDensity::from_csi_data(&csi_data, 128);
assert_eq!(psd.values.len(), 128);
assert_eq!(psd.frequencies.len(), 128);
assert!(psd.total_power >= 0.0);
assert!(psd.peak_power >= 0.0);
}
#[test]
fn test_doppler_features() {
let history = create_test_history(20);
let features = DopplerFeatures::from_csi_history(&history, 1000.0);
assert_eq!(features.shifts.len(), 64);
}
#[test]
fn test_feature_extractor() {
let config = FeatureExtractorConfig::default();
let extractor = FeatureExtractor::new(config);
let csi_data = create_test_csi_data();
let features = extractor.extract(&csi_data);
assert_eq!(features.amplitude.mean.len(), 64);
assert_eq!(features.phase.difference.len(), 63);
assert_eq!(features.correlation.matrix.dim(), (4, 4));
assert!(features.doppler.is_none());
}
#[test]
fn test_feature_extractor_with_history() {
let config = FeatureExtractorConfig {
min_doppler_history: 10,
enable_doppler: true,
..Default::default()
};
let extractor = FeatureExtractor::new(config);
let csi_data = create_test_csi_data();
let history = create_test_history(15);
let features = extractor.extract_with_history(&csi_data, &history);
assert!(features.doppler.is_some());
assert_eq!(features.metadata.doppler_samples, Some(15));
}
#[test]
fn test_individual_extraction() {
let extractor = FeatureExtractor::default_config();
let csi_data = create_test_csi_data();
let amp = extractor.extract_amplitude(&csi_data);
assert!(!amp.mean.is_empty());
let phase = extractor.extract_phase(&csi_data);
assert!(!phase.difference.is_empty());
let corr = extractor.extract_correlation(&csi_data);
assert_eq!(corr.matrix.dim(), (4, 4));
let psd = extractor.extract_psd(&csi_data);
assert!(!psd.values.is_empty());
}
#[test]
fn test_empty_doppler_history() {
let extractor = FeatureExtractor::default_config();
let history: Vec<CsiData> = vec![];
let doppler = extractor.extract_doppler(&history);
assert!(doppler.is_none());
}
#[test]
fn test_insufficient_doppler_history() {
let config = FeatureExtractorConfig {
min_doppler_history: 10,
..Default::default()
};
let extractor = FeatureExtractor::new(config);
let history = create_test_history(5);
let doppler = extractor.extract_doppler(&history);
assert!(doppler.is_none());
}
}

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//! WiFi-DensePose Signal Processing Library
//!
//! This crate provides signal processing capabilities for WiFi-based human pose estimation,
//! including CSI (Channel State Information) processing, phase sanitization, feature extraction,
//! and motion detection.
//!
//! # Features
//!
//! - **CSI Processing**: Preprocessing, noise removal, windowing, and normalization
//! - **Phase Sanitization**: Phase unwrapping, outlier removal, and smoothing
//! - **Feature Extraction**: Amplitude, phase, correlation, Doppler, and PSD features
//! - **Motion Detection**: Human presence detection with confidence scoring
//!
//! # Example
//!
//! ```rust,no_run
//! use wifi_densepose_signal::{
//! CsiProcessor, CsiProcessorConfig,
//! PhaseSanitizer, PhaseSanitizerConfig,
//! MotionDetector,
//! };
//!
//! // Configure CSI processor
//! let config = CsiProcessorConfig::builder()
//! .sampling_rate(1000.0)
//! .window_size(256)
//! .overlap(0.5)
//! .noise_threshold(-30.0)
//! .build();
//!
//! let processor = CsiProcessor::new(config);
//! ```
pub mod csi_processor;
pub mod features;
pub mod motion;
pub mod phase_sanitizer;
// Re-export main types for convenience
pub use csi_processor::{
CsiData, CsiDataBuilder, CsiPreprocessor, CsiProcessor, CsiProcessorConfig,
CsiProcessorConfigBuilder, CsiProcessorError,
};
pub use features::{
AmplitudeFeatures, CsiFeatures, CorrelationFeatures, DopplerFeatures, FeatureExtractor,
FeatureExtractorConfig, PhaseFeatures, PowerSpectralDensity,
};
pub use motion::{
HumanDetectionResult, MotionAnalysis, MotionDetector, MotionDetectorConfig, MotionScore,
};
pub use phase_sanitizer::{
PhaseSanitizationError, PhaseSanitizer, PhaseSanitizerConfig, UnwrappingMethod,
};
/// Library version
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
/// Common result type for signal processing operations
pub type Result<T> = std::result::Result<T, SignalError>;
/// Unified error type for signal processing operations
#[derive(Debug, thiserror::Error)]
pub enum SignalError {
/// CSI processing error
#[error("CSI processing error: {0}")]
CsiProcessing(#[from] CsiProcessorError),
/// Phase sanitization error
#[error("Phase sanitization error: {0}")]
PhaseSanitization(#[from] PhaseSanitizationError),
/// Feature extraction error
#[error("Feature extraction error: {0}")]
FeatureExtraction(String),
/// Motion detection error
#[error("Motion detection error: {0}")]
MotionDetection(String),
/// Invalid configuration
#[error("Invalid configuration: {0}")]
InvalidConfig(String),
/// Data validation error
#[error("Data validation error: {0}")]
DataValidation(String),
}
/// Prelude module for convenient imports
pub mod prelude {
pub use crate::csi_processor::{CsiData, CsiProcessor, CsiProcessorConfig};
pub use crate::features::{CsiFeatures, FeatureExtractor};
pub use crate::motion::{HumanDetectionResult, MotionDetector};
pub use crate::phase_sanitizer::{PhaseSanitizer, PhaseSanitizerConfig};
pub use crate::{Result, SignalError};
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_version() {
assert!(!VERSION.is_empty());
}
}

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//! Motion Detection Module
//!
//! This module provides motion detection and human presence detection
//! capabilities based on CSI features.
use crate::features::{AmplitudeFeatures, CorrelationFeatures, CsiFeatures, PhaseFeatures};
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::VecDeque;
/// Motion score with component breakdown
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionScore {
/// Overall motion score (0.0 to 1.0)
pub total: f64,
/// Variance-based motion component
pub variance_component: f64,
/// Correlation-based motion component
pub correlation_component: f64,
/// Phase-based motion component
pub phase_component: f64,
/// Doppler-based motion component (if available)
pub doppler_component: Option<f64>,
}
impl MotionScore {
/// Create a new motion score
pub fn new(
variance_component: f64,
correlation_component: f64,
phase_component: f64,
doppler_component: Option<f64>,
) -> Self {
// Calculate weighted total
let total = if let Some(doppler) = doppler_component {
0.3 * variance_component
+ 0.2 * correlation_component
+ 0.2 * phase_component
+ 0.3 * doppler
} else {
0.4 * variance_component + 0.3 * correlation_component + 0.3 * phase_component
};
Self {
total: total.clamp(0.0, 1.0),
variance_component,
correlation_component,
phase_component,
doppler_component,
}
}
/// Check if motion is detected above threshold
pub fn is_motion_detected(&self, threshold: f64) -> bool {
self.total >= threshold
}
}
/// Motion analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionAnalysis {
/// Motion score
pub score: MotionScore,
/// Temporal variance of motion
pub temporal_variance: f64,
/// Spatial variance of motion
pub spatial_variance: f64,
/// Estimated motion velocity (arbitrary units)
pub estimated_velocity: f64,
/// Motion direction estimate (radians, if available)
pub motion_direction: Option<f64>,
/// Confidence in the analysis
pub confidence: f64,
}
/// Human detection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HumanDetectionResult {
/// Whether a human was detected
pub human_detected: bool,
/// Detection confidence (0.0 to 1.0)
pub confidence: f64,
/// Motion score
pub motion_score: f64,
/// Raw (unsmoothed) confidence
pub raw_confidence: f64,
/// Timestamp of detection
pub timestamp: DateTime<Utc>,
/// Detection threshold used
pub threshold: f64,
/// Detailed motion analysis
pub motion_analysis: MotionAnalysis,
/// Additional metadata
#[serde(default)]
pub metadata: DetectionMetadata,
}
/// Metadata for detection results
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct DetectionMetadata {
/// Number of features used
pub features_used: usize,
/// Processing time in milliseconds
pub processing_time_ms: Option<f64>,
/// Whether Doppler was available
pub doppler_available: bool,
/// History length used
pub history_length: usize,
}
/// Configuration for motion detector
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MotionDetectorConfig {
/// Human detection threshold (0.0 to 1.0)
pub human_detection_threshold: f64,
/// Motion detection threshold (0.0 to 1.0)
pub motion_threshold: f64,
/// Temporal smoothing factor (0.0 to 1.0)
/// Higher values give more weight to previous detections
pub smoothing_factor: f64,
/// Minimum amplitude indicator threshold
pub amplitude_threshold: f64,
/// Minimum phase indicator threshold
pub phase_threshold: f64,
/// History size for temporal analysis
pub history_size: usize,
/// Enable adaptive thresholding
pub adaptive_threshold: bool,
/// Weight for amplitude indicator
pub amplitude_weight: f64,
/// Weight for phase indicator
pub phase_weight: f64,
/// Weight for motion indicator
pub motion_weight: f64,
}
impl Default for MotionDetectorConfig {
fn default() -> Self {
Self {
human_detection_threshold: 0.8,
motion_threshold: 0.3,
smoothing_factor: 0.9,
amplitude_threshold: 0.1,
phase_threshold: 0.05,
history_size: 100,
adaptive_threshold: false,
amplitude_weight: 0.4,
phase_weight: 0.3,
motion_weight: 0.3,
}
}
}
impl MotionDetectorConfig {
/// Create a new builder
pub fn builder() -> MotionDetectorConfigBuilder {
MotionDetectorConfigBuilder::new()
}
}
/// Builder for MotionDetectorConfig
#[derive(Debug, Default)]
pub struct MotionDetectorConfigBuilder {
config: MotionDetectorConfig,
}
impl MotionDetectorConfigBuilder {
/// Create new builder
pub fn new() -> Self {
Self {
config: MotionDetectorConfig::default(),
}
}
/// Set human detection threshold
pub fn human_detection_threshold(mut self, threshold: f64) -> Self {
self.config.human_detection_threshold = threshold;
self
}
/// Set motion threshold
pub fn motion_threshold(mut self, threshold: f64) -> Self {
self.config.motion_threshold = threshold;
self
}
/// Set smoothing factor
pub fn smoothing_factor(mut self, factor: f64) -> Self {
self.config.smoothing_factor = factor;
self
}
/// Set amplitude threshold
pub fn amplitude_threshold(mut self, threshold: f64) -> Self {
self.config.amplitude_threshold = threshold;
self
}
/// Set phase threshold
pub fn phase_threshold(mut self, threshold: f64) -> Self {
self.config.phase_threshold = threshold;
self
}
/// Set history size
pub fn history_size(mut self, size: usize) -> Self {
self.config.history_size = size;
self
}
/// Enable adaptive thresholding
pub fn adaptive_threshold(mut self, enable: bool) -> Self {
self.config.adaptive_threshold = enable;
self
}
/// Set indicator weights
pub fn weights(mut self, amplitude: f64, phase: f64, motion: f64) -> Self {
self.config.amplitude_weight = amplitude;
self.config.phase_weight = phase;
self.config.motion_weight = motion;
self
}
/// Build configuration
pub fn build(self) -> MotionDetectorConfig {
self.config
}
}
/// Motion detector for human presence detection
#[derive(Debug)]
pub struct MotionDetector {
config: MotionDetectorConfig,
previous_confidence: f64,
motion_history: VecDeque<MotionScore>,
detection_count: usize,
total_detections: usize,
baseline_variance: Option<f64>,
}
impl MotionDetector {
/// Create a new motion detector
pub fn new(config: MotionDetectorConfig) -> Self {
Self {
motion_history: VecDeque::with_capacity(config.history_size),
config,
previous_confidence: 0.0,
detection_count: 0,
total_detections: 0,
baseline_variance: None,
}
}
/// Create with default configuration
pub fn default_config() -> Self {
Self::new(MotionDetectorConfig::default())
}
/// Get configuration
pub fn config(&self) -> &MotionDetectorConfig {
&self.config
}
/// Analyze motion patterns from CSI features
pub fn analyze_motion(&self, features: &CsiFeatures) -> MotionAnalysis {
// Calculate variance-based motion score
let variance_score = self.calculate_variance_score(&features.amplitude);
// Calculate correlation-based motion score
let correlation_score = self.calculate_correlation_score(&features.correlation);
// Calculate phase-based motion score
let phase_score = self.calculate_phase_score(&features.phase);
// Calculate Doppler-based score if available
let doppler_score = features.doppler.as_ref().map(|d| {
// Normalize Doppler magnitude to 0-1 range
(d.mean_magnitude / 100.0).clamp(0.0, 1.0)
});
let motion_score = MotionScore::new(variance_score, correlation_score, phase_score, doppler_score);
// Calculate temporal and spatial variance
let temporal_variance = self.calculate_temporal_variance();
let spatial_variance = features.amplitude.variance.iter().sum::<f64>()
/ features.amplitude.variance.len() as f64;
// Estimate velocity from Doppler if available
let estimated_velocity = features
.doppler
.as_ref()
.map(|d| d.mean_magnitude)
.unwrap_or(0.0);
// Motion direction from phase gradient
let motion_direction = if features.phase.gradient.len() > 0 {
let mean_grad: f64 =
features.phase.gradient.iter().sum::<f64>() / features.phase.gradient.len() as f64;
Some(mean_grad.atan())
} else {
None
};
// Calculate confidence based on signal quality indicators
let confidence = self.calculate_motion_confidence(features);
MotionAnalysis {
score: motion_score,
temporal_variance,
spatial_variance,
estimated_velocity,
motion_direction,
confidence,
}
}
/// Calculate variance-based motion score
fn calculate_variance_score(&self, amplitude: &AmplitudeFeatures) -> f64 {
let mean_variance = amplitude.variance.iter().sum::<f64>() / amplitude.variance.len() as f64;
// Normalize using baseline if available
if let Some(baseline) = self.baseline_variance {
let ratio = mean_variance / (baseline + 1e-10);
(ratio - 1.0).max(0.0).tanh()
} else {
// Use heuristic normalization
(mean_variance / 0.5).clamp(0.0, 1.0)
}
}
/// Calculate correlation-based motion score
fn calculate_correlation_score(&self, correlation: &CorrelationFeatures) -> f64 {
let n = correlation.matrix.dim().0;
if n < 2 {
return 0.0;
}
// Calculate mean deviation from identity matrix
let mut deviation_sum = 0.0;
let mut count = 0;
for i in 0..n {
for j in 0..n {
let expected = if i == j { 1.0 } else { 0.0 };
deviation_sum += (correlation.matrix[[i, j]] - expected).abs();
count += 1;
}
}
let mean_deviation = deviation_sum / count as f64;
mean_deviation.clamp(0.0, 1.0)
}
/// Calculate phase-based motion score
fn calculate_phase_score(&self, phase: &PhaseFeatures) -> f64 {
// Use phase variance and coherence
let mean_variance = phase.variance.iter().sum::<f64>() / phase.variance.len() as f64;
let coherence_factor = 1.0 - phase.coherence.abs();
// Combine factors
let score = 0.5 * (mean_variance / 0.5).clamp(0.0, 1.0) + 0.5 * coherence_factor;
score.clamp(0.0, 1.0)
}
/// Calculate temporal variance from motion history
fn calculate_temporal_variance(&self) -> f64 {
if self.motion_history.len() < 2 {
return 0.0;
}
let scores: Vec<f64> = self.motion_history.iter().map(|m| m.total).collect();
let mean: f64 = scores.iter().sum::<f64>() / scores.len() as f64;
let variance: f64 = scores.iter().map(|s| (s - mean).powi(2)).sum::<f64>() / scores.len() as f64;
variance.sqrt()
}
/// Calculate confidence in motion detection
fn calculate_motion_confidence(&self, features: &CsiFeatures) -> f64 {
let mut confidence = 0.0;
let mut weight_sum = 0.0;
// Amplitude quality indicator
let amp_quality = (features.amplitude.dynamic_range / 2.0).clamp(0.0, 1.0);
confidence += amp_quality * 0.3;
weight_sum += 0.3;
// Phase coherence indicator
let phase_quality = features.phase.coherence.abs();
confidence += phase_quality * 0.3;
weight_sum += 0.3;
// Correlation consistency indicator
let corr_quality = (1.0 - features.correlation.correlation_spread).clamp(0.0, 1.0);
confidence += corr_quality * 0.2;
weight_sum += 0.2;
// Doppler quality if available
if let Some(ref doppler) = features.doppler {
let doppler_quality = (doppler.spread / doppler.mean_magnitude.max(1.0)).clamp(0.0, 1.0);
confidence += (1.0 - doppler_quality) * 0.2;
weight_sum += 0.2;
}
if weight_sum > 0.0 {
confidence / weight_sum
} else {
0.0
}
}
/// Calculate detection confidence from features and motion score
fn calculate_detection_confidence(&self, features: &CsiFeatures, motion_score: f64) -> f64 {
// Amplitude indicator
let amplitude_mean = features.amplitude.mean.iter().sum::<f64>()
/ features.amplitude.mean.len() as f64;
let amplitude_indicator = if amplitude_mean > self.config.amplitude_threshold {
1.0
} else {
0.0
};
// Phase indicator
let phase_std = features.phase.variance.iter().sum::<f64>().sqrt()
/ features.phase.variance.len() as f64;
let phase_indicator = if phase_std > self.config.phase_threshold {
1.0
} else {
0.0
};
// Motion indicator
let motion_indicator = if motion_score > self.config.motion_threshold {
1.0
} else {
0.0
};
// Weighted combination
let confidence = self.config.amplitude_weight * amplitude_indicator
+ self.config.phase_weight * phase_indicator
+ self.config.motion_weight * motion_indicator;
confidence.clamp(0.0, 1.0)
}
/// Apply temporal smoothing (exponential moving average)
fn apply_temporal_smoothing(&mut self, raw_confidence: f64) -> f64 {
let smoothed = self.config.smoothing_factor * self.previous_confidence
+ (1.0 - self.config.smoothing_factor) * raw_confidence;
self.previous_confidence = smoothed;
smoothed
}
/// Detect human presence from CSI features
pub fn detect_human(&mut self, features: &CsiFeatures) -> HumanDetectionResult {
// Analyze motion
let motion_analysis = self.analyze_motion(features);
// Add to history
if self.motion_history.len() >= self.config.history_size {
self.motion_history.pop_front();
}
self.motion_history.push_back(motion_analysis.score.clone());
// Calculate detection confidence
let raw_confidence =
self.calculate_detection_confidence(features, motion_analysis.score.total);
// Apply temporal smoothing
let smoothed_confidence = self.apply_temporal_smoothing(raw_confidence);
// Get effective threshold (adaptive if enabled)
let threshold = if self.config.adaptive_threshold {
self.calculate_adaptive_threshold()
} else {
self.config.human_detection_threshold
};
// Determine detection
let human_detected = smoothed_confidence >= threshold;
self.total_detections += 1;
if human_detected {
self.detection_count += 1;
}
let metadata = DetectionMetadata {
features_used: 4, // amplitude, phase, correlation, psd
processing_time_ms: None,
doppler_available: features.doppler.is_some(),
history_length: self.motion_history.len(),
};
HumanDetectionResult {
human_detected,
confidence: smoothed_confidence,
motion_score: motion_analysis.score.total,
raw_confidence,
timestamp: Utc::now(),
threshold,
motion_analysis,
metadata,
}
}
/// Calculate adaptive threshold based on recent history
fn calculate_adaptive_threshold(&self) -> f64 {
if self.motion_history.len() < 10 {
return self.config.human_detection_threshold;
}
let scores: Vec<f64> = self.motion_history.iter().map(|m| m.total).collect();
let mean: f64 = scores.iter().sum::<f64>() / scores.len() as f64;
let std: f64 = {
let var: f64 = scores.iter().map(|s| (s - mean).powi(2)).sum::<f64>() / scores.len() as f64;
var.sqrt()
};
// Threshold is mean + 1 std deviation, clamped to reasonable range
(mean + std).clamp(0.3, 0.95)
}
/// Update baseline variance (for calibration)
pub fn calibrate(&mut self, features: &CsiFeatures) {
let mean_variance =
features.amplitude.variance.iter().sum::<f64>() / features.amplitude.variance.len() as f64;
self.baseline_variance = Some(mean_variance);
}
/// Clear calibration
pub fn clear_calibration(&mut self) {
self.baseline_variance = None;
}
/// Get detection statistics
pub fn get_statistics(&self) -> DetectionStatistics {
DetectionStatistics {
total_detections: self.total_detections,
positive_detections: self.detection_count,
detection_rate: if self.total_detections > 0 {
self.detection_count as f64 / self.total_detections as f64
} else {
0.0
},
history_size: self.motion_history.len(),
is_calibrated: self.baseline_variance.is_some(),
}
}
/// Reset detector state
pub fn reset(&mut self) {
self.previous_confidence = 0.0;
self.motion_history.clear();
self.detection_count = 0;
self.total_detections = 0;
}
/// Get previous confidence value
pub fn previous_confidence(&self) -> f64 {
self.previous_confidence
}
}
/// Detection statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectionStatistics {
/// Total number of detection attempts
pub total_detections: usize,
/// Number of positive detections
pub positive_detections: usize,
/// Detection rate (0.0 to 1.0)
pub detection_rate: f64,
/// Current history size
pub history_size: usize,
/// Whether detector is calibrated
pub is_calibrated: bool,
}
#[cfg(test)]
mod tests {
use super::*;
use crate::csi_processor::CsiData;
use crate::features::FeatureExtractor;
use ndarray::Array2;
fn create_test_csi_data(motion_level: f64) -> CsiData {
let amplitude = Array2::from_shape_fn((4, 64), |(i, j)| {
1.0 + motion_level * 0.5 * ((i + j) as f64 * 0.1).sin()
});
let phase = Array2::from_shape_fn((4, 64), |(i, j)| {
motion_level * 0.3 * ((i + j) as f64 * 0.15).sin()
});
CsiData::builder()
.amplitude(amplitude)
.phase(phase)
.frequency(5.0e9)
.bandwidth(20.0e6)
.snr(25.0)
.build()
.unwrap()
}
fn create_test_features(motion_level: f64) -> CsiFeatures {
let csi_data = create_test_csi_data(motion_level);
let extractor = FeatureExtractor::default_config();
extractor.extract(&csi_data)
}
#[test]
fn test_motion_score() {
let score = MotionScore::new(0.5, 0.6, 0.4, None);
assert!(score.total > 0.0 && score.total <= 1.0);
assert_eq!(score.variance_component, 0.5);
assert_eq!(score.correlation_component, 0.6);
assert_eq!(score.phase_component, 0.4);
}
#[test]
fn test_motion_score_with_doppler() {
let score = MotionScore::new(0.5, 0.6, 0.4, Some(0.7));
assert!(score.total > 0.0 && score.total <= 1.0);
assert_eq!(score.doppler_component, Some(0.7));
}
#[test]
fn test_motion_detector_creation() {
let config = MotionDetectorConfig::default();
let detector = MotionDetector::new(config);
assert_eq!(detector.previous_confidence(), 0.0);
}
#[test]
fn test_motion_analysis() {
let detector = MotionDetector::default_config();
let features = create_test_features(0.5);
let analysis = detector.analyze_motion(&features);
assert!(analysis.score.total >= 0.0 && analysis.score.total <= 1.0);
assert!(analysis.confidence >= 0.0 && analysis.confidence <= 1.0);
}
#[test]
fn test_human_detection() {
let config = MotionDetectorConfig::builder()
.human_detection_threshold(0.5)
.smoothing_factor(0.5)
.build();
let mut detector = MotionDetector::new(config);
let features = create_test_features(0.8);
let result = detector.detect_human(&features);
assert!(result.confidence >= 0.0 && result.confidence <= 1.0);
assert!(result.motion_score >= 0.0 && result.motion_score <= 1.0);
}
#[test]
fn test_temporal_smoothing() {
let config = MotionDetectorConfig::builder()
.smoothing_factor(0.9)
.build();
let mut detector = MotionDetector::new(config);
// First detection with low confidence
let features_low = create_test_features(0.1);
let result1 = detector.detect_human(&features_low);
// Second detection with high confidence should be smoothed
let features_high = create_test_features(0.9);
let result2 = detector.detect_human(&features_high);
// Due to smoothing, result2.confidence should be between result1 and raw
assert!(result2.confidence >= result1.confidence);
}
#[test]
fn test_calibration() {
let mut detector = MotionDetector::default_config();
let features = create_test_features(0.5);
assert!(!detector.get_statistics().is_calibrated);
detector.calibrate(&features);
assert!(detector.get_statistics().is_calibrated);
detector.clear_calibration();
assert!(!detector.get_statistics().is_calibrated);
}
#[test]
fn test_detection_statistics() {
let mut detector = MotionDetector::default_config();
for i in 0..5 {
let features = create_test_features((i as f64) / 5.0);
let _ = detector.detect_human(&features);
}
let stats = detector.get_statistics();
assert_eq!(stats.total_detections, 5);
assert!(stats.detection_rate >= 0.0 && stats.detection_rate <= 1.0);
}
#[test]
fn test_reset() {
let mut detector = MotionDetector::default_config();
let features = create_test_features(0.5);
for _ in 0..5 {
let _ = detector.detect_human(&features);
}
detector.reset();
let stats = detector.get_statistics();
assert_eq!(stats.total_detections, 0);
assert_eq!(stats.history_size, 0);
assert_eq!(detector.previous_confidence(), 0.0);
}
#[test]
fn test_adaptive_threshold() {
let config = MotionDetectorConfig::builder()
.adaptive_threshold(true)
.history_size(20)
.build();
let mut detector = MotionDetector::new(config);
// Build up history
for i in 0..15 {
let features = create_test_features((i as f64 % 5.0) / 5.0);
let _ = detector.detect_human(&features);
}
// The adaptive threshold should now be calculated
let features = create_test_features(0.5);
let result = detector.detect_human(&features);
// Threshold should be different from default
// (this is a weak assertion, mainly checking it runs)
assert!(result.threshold > 0.0);
}
#[test]
fn test_config_builder() {
let config = MotionDetectorConfig::builder()
.human_detection_threshold(0.7)
.motion_threshold(0.4)
.smoothing_factor(0.85)
.amplitude_threshold(0.15)
.phase_threshold(0.08)
.history_size(200)
.adaptive_threshold(true)
.weights(0.35, 0.35, 0.30)
.build();
assert_eq!(config.human_detection_threshold, 0.7);
assert_eq!(config.motion_threshold, 0.4);
assert_eq!(config.smoothing_factor, 0.85);
assert_eq!(config.amplitude_threshold, 0.15);
assert_eq!(config.phase_threshold, 0.08);
assert_eq!(config.history_size, 200);
assert!(config.adaptive_threshold);
assert_eq!(config.amplitude_weight, 0.35);
assert_eq!(config.phase_weight, 0.35);
assert_eq!(config.motion_weight, 0.30);
}
#[test]
fn test_low_motion_no_detection() {
let config = MotionDetectorConfig::builder()
.human_detection_threshold(0.8)
.smoothing_factor(0.0) // No smoothing for clear test
.build();
let mut detector = MotionDetector::new(config);
// Very low motion should not trigger detection
let features = create_test_features(0.01);
let result = detector.detect_human(&features);
// With very low motion, detection should likely be false
// (depends on thresholds, but confidence should be low)
assert!(result.motion_score < 0.5);
}
#[test]
fn test_motion_history() {
let config = MotionDetectorConfig::builder()
.history_size(10)
.build();
let mut detector = MotionDetector::new(config);
for i in 0..15 {
let features = create_test_features((i as f64) / 15.0);
let _ = detector.detect_human(&features);
}
let stats = detector.get_statistics();
assert_eq!(stats.history_size, 10); // Should not exceed max
}
}

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//! Phase Sanitization Module
//!
//! This module provides phase unwrapping, outlier removal, smoothing, and noise filtering
//! for CSI phase data to ensure reliable signal processing.
use ndarray::Array2;
use serde::{Deserialize, Serialize};
use std::f64::consts::PI;
use thiserror::Error;
/// Errors that can occur during phase sanitization
#[derive(Debug, Error)]
pub enum PhaseSanitizationError {
/// Invalid configuration
#[error("Invalid configuration: {0}")]
InvalidConfig(String),
/// Phase unwrapping failed
#[error("Phase unwrapping failed: {0}")]
UnwrapFailed(String),
/// Outlier removal failed
#[error("Outlier removal failed: {0}")]
OutlierRemovalFailed(String),
/// Smoothing failed
#[error("Smoothing failed: {0}")]
SmoothingFailed(String),
/// Noise filtering failed
#[error("Noise filtering failed: {0}")]
NoiseFilterFailed(String),
/// Invalid data format
#[error("Invalid data: {0}")]
InvalidData(String),
/// Pipeline error
#[error("Sanitization pipeline failed: {0}")]
PipelineFailed(String),
}
/// Phase unwrapping method
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum UnwrappingMethod {
/// Standard numpy-style unwrapping
Standard,
/// Row-by-row custom unwrapping
Custom,
/// Itoh's method for 2D unwrapping
Itoh,
/// Quality-guided unwrapping
QualityGuided,
}
impl Default for UnwrappingMethod {
fn default() -> Self {
Self::Standard
}
}
/// Configuration for phase sanitizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhaseSanitizerConfig {
/// Phase unwrapping method
pub unwrapping_method: UnwrappingMethod,
/// Z-score threshold for outlier detection
pub outlier_threshold: f64,
/// Window size for smoothing
pub smoothing_window: usize,
/// Enable outlier removal
pub enable_outlier_removal: bool,
/// Enable smoothing
pub enable_smoothing: bool,
/// Enable noise filtering
pub enable_noise_filtering: bool,
/// Noise filter cutoff frequency (normalized 0-1)
pub noise_threshold: f64,
/// Valid phase range
pub phase_range: (f64, f64),
}
impl Default for PhaseSanitizerConfig {
fn default() -> Self {
Self {
unwrapping_method: UnwrappingMethod::Standard,
outlier_threshold: 3.0,
smoothing_window: 5,
enable_outlier_removal: true,
enable_smoothing: true,
enable_noise_filtering: false,
noise_threshold: 0.05,
phase_range: (-PI, PI),
}
}
}
impl PhaseSanitizerConfig {
/// Create a new config builder
pub fn builder() -> PhaseSanitizerConfigBuilder {
PhaseSanitizerConfigBuilder::new()
}
/// Validate configuration
pub fn validate(&self) -> Result<(), PhaseSanitizationError> {
if self.outlier_threshold <= 0.0 {
return Err(PhaseSanitizationError::InvalidConfig(
"outlier_threshold must be positive".into(),
));
}
if self.smoothing_window == 0 {
return Err(PhaseSanitizationError::InvalidConfig(
"smoothing_window must be positive".into(),
));
}
if self.noise_threshold <= 0.0 || self.noise_threshold >= 1.0 {
return Err(PhaseSanitizationError::InvalidConfig(
"noise_threshold must be between 0 and 1".into(),
));
}
Ok(())
}
}
/// Builder for PhaseSanitizerConfig
#[derive(Debug, Default)]
pub struct PhaseSanitizerConfigBuilder {
config: PhaseSanitizerConfig,
}
impl PhaseSanitizerConfigBuilder {
/// Create a new builder
pub fn new() -> Self {
Self {
config: PhaseSanitizerConfig::default(),
}
}
/// Set unwrapping method
pub fn unwrapping_method(mut self, method: UnwrappingMethod) -> Self {
self.config.unwrapping_method = method;
self
}
/// Set outlier threshold
pub fn outlier_threshold(mut self, threshold: f64) -> Self {
self.config.outlier_threshold = threshold;
self
}
/// Set smoothing window
pub fn smoothing_window(mut self, window: usize) -> Self {
self.config.smoothing_window = window;
self
}
/// Enable/disable outlier removal
pub fn enable_outlier_removal(mut self, enable: bool) -> Self {
self.config.enable_outlier_removal = enable;
self
}
/// Enable/disable smoothing
pub fn enable_smoothing(mut self, enable: bool) -> Self {
self.config.enable_smoothing = enable;
self
}
/// Enable/disable noise filtering
pub fn enable_noise_filtering(mut self, enable: bool) -> Self {
self.config.enable_noise_filtering = enable;
self
}
/// Set noise threshold
pub fn noise_threshold(mut self, threshold: f64) -> Self {
self.config.noise_threshold = threshold;
self
}
/// Set phase range
pub fn phase_range(mut self, min: f64, max: f64) -> Self {
self.config.phase_range = (min, max);
self
}
/// Build the configuration
pub fn build(self) -> PhaseSanitizerConfig {
self.config
}
}
/// Statistics for sanitization operations
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct SanitizationStatistics {
/// Total samples processed
pub total_processed: usize,
/// Total outliers removed
pub outliers_removed: usize,
/// Total sanitization errors
pub sanitization_errors: usize,
}
impl SanitizationStatistics {
/// Calculate outlier rate
pub fn outlier_rate(&self) -> f64 {
if self.total_processed > 0 {
self.outliers_removed as f64 / self.total_processed as f64
} else {
0.0
}
}
/// Calculate error rate
pub fn error_rate(&self) -> f64 {
if self.total_processed > 0 {
self.sanitization_errors as f64 / self.total_processed as f64
} else {
0.0
}
}
}
/// Phase Sanitizer for cleaning and preparing phase data
#[derive(Debug)]
pub struct PhaseSanitizer {
config: PhaseSanitizerConfig,
statistics: SanitizationStatistics,
}
impl PhaseSanitizer {
/// Create a new phase sanitizer
pub fn new(config: PhaseSanitizerConfig) -> Result<Self, PhaseSanitizationError> {
config.validate()?;
Ok(Self {
config,
statistics: SanitizationStatistics::default(),
})
}
/// Get the configuration
pub fn config(&self) -> &PhaseSanitizerConfig {
&self.config
}
/// Validate phase data format and values
pub fn validate_phase_data(&self, phase_data: &Array2<f64>) -> Result<(), PhaseSanitizationError> {
// Check if data is empty
if phase_data.is_empty() {
return Err(PhaseSanitizationError::InvalidData(
"Phase data cannot be empty".into(),
));
}
// Check if values are within valid range
let (min_val, max_val) = self.config.phase_range;
for &val in phase_data.iter() {
if val < min_val || val > max_val {
return Err(PhaseSanitizationError::InvalidData(format!(
"Phase value {} outside valid range [{}, {}]",
val, min_val, max_val
)));
}
}
Ok(())
}
/// Unwrap phase data to remove 2pi discontinuities
pub fn unwrap_phase(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
if phase_data.is_empty() {
return Err(PhaseSanitizationError::UnwrapFailed(
"Cannot unwrap empty phase data".into(),
));
}
match self.config.unwrapping_method {
UnwrappingMethod::Standard => self.unwrap_standard(phase_data),
UnwrappingMethod::Custom => self.unwrap_custom(phase_data),
UnwrappingMethod::Itoh => self.unwrap_itoh(phase_data),
UnwrappingMethod::QualityGuided => self.unwrap_quality_guided(phase_data),
}
}
/// Standard phase unwrapping (numpy-style)
fn unwrap_standard(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
let mut unwrapped = phase_data.clone();
let (_nrows, ncols) = unwrapped.dim();
for i in 0..unwrapped.nrows() {
let mut row_data: Vec<f64> = (0..ncols).map(|j| unwrapped[[i, j]]).collect();
Self::unwrap_1d(&mut row_data);
for (j, &val) in row_data.iter().enumerate() {
unwrapped[[i, j]] = val;
}
}
Ok(unwrapped)
}
/// Custom row-by-row phase unwrapping
fn unwrap_custom(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
let mut unwrapped = phase_data.clone();
let ncols = unwrapped.ncols();
for i in 0..unwrapped.nrows() {
let mut row_data: Vec<f64> = (0..ncols).map(|j| unwrapped[[i, j]]).collect();
self.unwrap_1d_custom(&mut row_data);
for (j, &val) in row_data.iter().enumerate() {
unwrapped[[i, j]] = val;
}
}
Ok(unwrapped)
}
/// Itoh's 2D phase unwrapping method
fn unwrap_itoh(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
let mut unwrapped = phase_data.clone();
let (nrows, ncols) = phase_data.dim();
// First unwrap rows
for i in 0..nrows {
let mut row_data: Vec<f64> = (0..ncols).map(|j| unwrapped[[i, j]]).collect();
Self::unwrap_1d(&mut row_data);
for (j, &val) in row_data.iter().enumerate() {
unwrapped[[i, j]] = val;
}
}
// Then unwrap columns
for j in 0..ncols {
let mut col: Vec<f64> = unwrapped.column(j).to_vec();
Self::unwrap_1d(&mut col);
for (i, &val) in col.iter().enumerate() {
unwrapped[[i, j]] = val;
}
}
Ok(unwrapped)
}
/// Quality-guided phase unwrapping
fn unwrap_quality_guided(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
// For now, use standard unwrapping with quality weighting
// A full implementation would use phase derivatives as quality metric
let mut unwrapped = phase_data.clone();
let (nrows, ncols) = phase_data.dim();
// Calculate quality map based on phase gradients
// Note: Full quality-guided implementation would use this map for ordering
let _quality = self.calculate_quality_map(phase_data);
// Unwrap starting from highest quality regions
for i in 0..nrows {
let mut row_data: Vec<f64> = (0..ncols).map(|j| unwrapped[[i, j]]).collect();
Self::unwrap_1d(&mut row_data);
for (j, &val) in row_data.iter().enumerate() {
unwrapped[[i, j]] = val;
}
}
Ok(unwrapped)
}
/// Calculate quality map for quality-guided unwrapping
fn calculate_quality_map(&self, phase_data: &Array2<f64>) -> Array2<f64> {
let (nrows, ncols) = phase_data.dim();
let mut quality = Array2::zeros((nrows, ncols));
for i in 0..nrows {
for j in 0..ncols {
let mut grad_sum = 0.0;
let mut count = 0;
// Calculate local phase gradient magnitude
if j > 0 {
grad_sum += (phase_data[[i, j]] - phase_data[[i, j - 1]]).abs();
count += 1;
}
if j < ncols - 1 {
grad_sum += (phase_data[[i, j + 1]] - phase_data[[i, j]]).abs();
count += 1;
}
if i > 0 {
grad_sum += (phase_data[[i, j]] - phase_data[[i - 1, j]]).abs();
count += 1;
}
if i < nrows - 1 {
grad_sum += (phase_data[[i + 1, j]] - phase_data[[i, j]]).abs();
count += 1;
}
// Quality is inverse of gradient magnitude
if count > 0 {
quality[[i, j]] = 1.0 / (1.0 + grad_sum / count as f64);
}
}
}
quality
}
/// In-place 1D phase unwrapping
fn unwrap_1d(data: &mut [f64]) {
if data.len() < 2 {
return;
}
let mut correction = 0.0;
let mut prev_wrapped = data[0];
for i in 1..data.len() {
let current_wrapped = data[i];
// Calculate diff using original wrapped values
let diff = current_wrapped - prev_wrapped;
if diff > PI {
correction -= 2.0 * PI;
} else if diff < -PI {
correction += 2.0 * PI;
}
data[i] = current_wrapped + correction;
prev_wrapped = current_wrapped;
}
}
/// Custom 1D phase unwrapping with tolerance
fn unwrap_1d_custom(&self, data: &mut [f64]) {
if data.len() < 2 {
return;
}
let tolerance = 0.9 * PI; // Slightly less than pi for robustness
let mut correction = 0.0;
for i in 1..data.len() {
let diff = data[i] - data[i - 1] + correction;
if diff > tolerance {
correction -= 2.0 * PI;
} else if diff < -tolerance {
correction += 2.0 * PI;
}
data[i] += correction;
}
}
/// Remove outliers from phase data using Z-score method
pub fn remove_outliers(&mut self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
if !self.config.enable_outlier_removal {
return Ok(phase_data.clone());
}
// Detect outliers
let outlier_mask = self.detect_outliers(phase_data)?;
// Interpolate outliers
let cleaned = self.interpolate_outliers(phase_data, &outlier_mask)?;
Ok(cleaned)
}
/// Detect outliers using Z-score method
fn detect_outliers(&mut self, phase_data: &Array2<f64>) -> Result<Array2<bool>, PhaseSanitizationError> {
let (nrows, ncols) = phase_data.dim();
let mut outlier_mask = Array2::from_elem((nrows, ncols), false);
for i in 0..nrows {
let row = phase_data.row(i);
let mean = row.mean().unwrap_or(0.0);
let std = self.calculate_std_1d(&row.to_vec());
for j in 0..ncols {
let z_score = (phase_data[[i, j]] - mean).abs() / (std + 1e-8);
if z_score > self.config.outlier_threshold {
outlier_mask[[i, j]] = true;
self.statistics.outliers_removed += 1;
}
}
}
Ok(outlier_mask)
}
/// Interpolate outlier values using linear interpolation
fn interpolate_outliers(
&self,
phase_data: &Array2<f64>,
outlier_mask: &Array2<bool>,
) -> Result<Array2<f64>, PhaseSanitizationError> {
let mut cleaned = phase_data.clone();
let (nrows, ncols) = phase_data.dim();
for i in 0..nrows {
// Find valid (non-outlier) indices
let valid_indices: Vec<usize> = (0..ncols)
.filter(|&j| !outlier_mask[[i, j]])
.collect();
let outlier_indices: Vec<usize> = (0..ncols)
.filter(|&j| outlier_mask[[i, j]])
.collect();
if valid_indices.len() >= 2 && !outlier_indices.is_empty() {
// Extract valid values
let valid_values: Vec<f64> = valid_indices
.iter()
.map(|&j| phase_data[[i, j]])
.collect();
// Interpolate outliers
for &j in &outlier_indices {
cleaned[[i, j]] = self.linear_interpolate(j, &valid_indices, &valid_values);
}
}
}
Ok(cleaned)
}
/// Linear interpolation helper
fn linear_interpolate(&self, x: usize, xs: &[usize], ys: &[f64]) -> f64 {
if xs.is_empty() {
return 0.0;
}
// Find surrounding points
let mut lower_idx = 0;
let mut upper_idx = xs.len() - 1;
for (i, &xi) in xs.iter().enumerate() {
if xi <= x {
lower_idx = i;
}
if xi >= x {
upper_idx = i;
break;
}
}
if lower_idx == upper_idx {
return ys[lower_idx];
}
// Linear interpolation
let x0 = xs[lower_idx] as f64;
let x1 = xs[upper_idx] as f64;
let y0 = ys[lower_idx];
let y1 = ys[upper_idx];
y0 + (y1 - y0) * (x as f64 - x0) / (x1 - x0)
}
/// Smooth phase data using moving average
pub fn smooth_phase(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
if !self.config.enable_smoothing {
return Ok(phase_data.clone());
}
let mut smoothed = phase_data.clone();
let (nrows, ncols) = phase_data.dim();
// Ensure odd window size
let mut window_size = self.config.smoothing_window;
if window_size % 2 == 0 {
window_size += 1;
}
let half_window = window_size / 2;
for i in 0..nrows {
for j in half_window..ncols.saturating_sub(half_window) {
let mut sum = 0.0;
for k in 0..window_size {
sum += phase_data[[i, j - half_window + k]];
}
smoothed[[i, j]] = sum / window_size as f64;
}
}
Ok(smoothed)
}
/// Filter noise using low-pass Butterworth filter
pub fn filter_noise(&self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
if !self.config.enable_noise_filtering {
return Ok(phase_data.clone());
}
let (nrows, ncols) = phase_data.dim();
// Check minimum length for filtering
let min_filter_length = 18;
if ncols < min_filter_length {
return Ok(phase_data.clone());
}
// Simple low-pass filter using exponential smoothing
let alpha = self.config.noise_threshold;
let mut filtered = phase_data.clone();
for i in 0..nrows {
// Forward pass
for j in 1..ncols {
filtered[[i, j]] = alpha * filtered[[i, j]] + (1.0 - alpha) * filtered[[i, j - 1]];
}
// Backward pass for zero-phase filtering
for j in (0..ncols - 1).rev() {
filtered[[i, j]] = alpha * filtered[[i, j]] + (1.0 - alpha) * filtered[[i, j + 1]];
}
}
Ok(filtered)
}
/// Complete sanitization pipeline
pub fn sanitize_phase(&mut self, phase_data: &Array2<f64>) -> Result<Array2<f64>, PhaseSanitizationError> {
self.statistics.total_processed += 1;
// Validate input
self.validate_phase_data(phase_data).map_err(|e| {
self.statistics.sanitization_errors += 1;
e
})?;
// Unwrap phase
let unwrapped = self.unwrap_phase(phase_data).map_err(|e| {
self.statistics.sanitization_errors += 1;
e
})?;
// Remove outliers
let cleaned = self.remove_outliers(&unwrapped).map_err(|e| {
self.statistics.sanitization_errors += 1;
e
})?;
// Smooth phase
let smoothed = self.smooth_phase(&cleaned).map_err(|e| {
self.statistics.sanitization_errors += 1;
e
})?;
// Filter noise
let filtered = self.filter_noise(&smoothed).map_err(|e| {
self.statistics.sanitization_errors += 1;
e
})?;
Ok(filtered)
}
/// Get sanitization statistics
pub fn get_statistics(&self) -> &SanitizationStatistics {
&self.statistics
}
/// Reset statistics
pub fn reset_statistics(&mut self) {
self.statistics = SanitizationStatistics::default();
}
/// Calculate standard deviation for 1D slice
fn calculate_std_1d(&self, data: &[f64]) -> f64 {
if data.is_empty() {
return 0.0;
}
let mean: f64 = data.iter().sum::<f64>() / data.len() as f64;
let variance: f64 = data.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / data.len() as f64;
variance.sqrt()
}
}
#[cfg(test)]
mod tests {
use super::*;
use std::f64::consts::PI;
fn create_test_phase_data() -> Array2<f64> {
// Create phase data with some simulated wrapping
Array2::from_shape_fn((4, 64), |(i, j)| {
let base = (j as f64 * 0.05).sin() * (PI / 2.0);
base + (i as f64 * 0.1)
})
}
fn create_wrapped_phase_data() -> Array2<f64> {
// Create phase data that will need unwrapping
// Generate a linearly increasing phase that wraps at +/- pi boundaries
Array2::from_shape_fn((2, 20), |(i, j)| {
let unwrapped = j as f64 * 0.4 + i as f64 * 0.2;
// Proper wrap to [-pi, pi]
let mut wrapped = unwrapped;
while wrapped > PI {
wrapped -= 2.0 * PI;
}
while wrapped < -PI {
wrapped += 2.0 * PI;
}
wrapped
})
}
#[test]
fn test_config_validation() {
let config = PhaseSanitizerConfig::default();
assert!(config.validate().is_ok());
}
#[test]
fn test_invalid_config() {
let config = PhaseSanitizerConfig::builder()
.outlier_threshold(-1.0)
.build();
assert!(config.validate().is_err());
}
#[test]
fn test_sanitizer_creation() {
let config = PhaseSanitizerConfig::default();
let sanitizer = PhaseSanitizer::new(config);
assert!(sanitizer.is_ok());
}
#[test]
fn test_phase_validation() {
let config = PhaseSanitizerConfig::default();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let valid_data = create_test_phase_data();
assert!(sanitizer.validate_phase_data(&valid_data).is_ok());
// Test with out-of-range values
let invalid_data = Array2::from_elem((2, 10), 10.0);
assert!(sanitizer.validate_phase_data(&invalid_data).is_err());
}
#[test]
fn test_phase_unwrapping() {
let config = PhaseSanitizerConfig::builder()
.unwrapping_method(UnwrappingMethod::Standard)
.build();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let wrapped = create_wrapped_phase_data();
let unwrapped = sanitizer.unwrap_phase(&wrapped);
assert!(unwrapped.is_ok());
// Verify that differences are now smooth (no jumps > pi)
let unwrapped = unwrapped.unwrap();
let ncols = unwrapped.ncols();
for i in 0..unwrapped.nrows() {
for j in 1..ncols {
let diff = (unwrapped[[i, j]] - unwrapped[[i, j - 1]]).abs();
assert!(diff < PI + 0.1, "Jump detected: {}", diff);
}
}
}
#[test]
fn test_outlier_removal() {
let config = PhaseSanitizerConfig::builder()
.outlier_threshold(2.0)
.enable_outlier_removal(true)
.build();
let mut sanitizer = PhaseSanitizer::new(config).unwrap();
let mut data = create_test_phase_data();
// Insert an outlier
data[[0, 10]] = 100.0 * data[[0, 10]];
// Need to use data within valid range
let data = Array2::from_shape_fn((4, 64), |(i, j)| {
if i == 0 && j == 10 {
PI * 0.9 // Near boundary but valid
} else {
0.1 * (j as f64 * 0.1).sin()
}
});
let cleaned = sanitizer.remove_outliers(&data);
assert!(cleaned.is_ok());
}
#[test]
fn test_phase_smoothing() {
let config = PhaseSanitizerConfig::builder()
.smoothing_window(5)
.enable_smoothing(true)
.build();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let noisy_data = Array2::from_shape_fn((2, 20), |(_, j)| {
(j as f64 * 0.2).sin() + 0.1 * ((j * 7) as f64).sin()
});
let smoothed = sanitizer.smooth_phase(&noisy_data);
assert!(smoothed.is_ok());
}
#[test]
fn test_noise_filtering() {
let config = PhaseSanitizerConfig::builder()
.noise_threshold(0.1)
.enable_noise_filtering(true)
.build();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let data = create_test_phase_data();
let filtered = sanitizer.filter_noise(&data);
assert!(filtered.is_ok());
}
#[test]
fn test_complete_pipeline() {
let config = PhaseSanitizerConfig::builder()
.unwrapping_method(UnwrappingMethod::Standard)
.outlier_threshold(3.0)
.smoothing_window(3)
.enable_outlier_removal(true)
.enable_smoothing(true)
.enable_noise_filtering(false)
.build();
let mut sanitizer = PhaseSanitizer::new(config).unwrap();
let data = create_test_phase_data();
let sanitized = sanitizer.sanitize_phase(&data);
assert!(sanitized.is_ok());
let stats = sanitizer.get_statistics();
assert_eq!(stats.total_processed, 1);
}
#[test]
fn test_different_unwrapping_methods() {
let methods = vec![
UnwrappingMethod::Standard,
UnwrappingMethod::Custom,
UnwrappingMethod::Itoh,
UnwrappingMethod::QualityGuided,
];
let wrapped = create_wrapped_phase_data();
for method in methods {
let config = PhaseSanitizerConfig::builder()
.unwrapping_method(method)
.build();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let result = sanitizer.unwrap_phase(&wrapped);
assert!(result.is_ok(), "Failed for method {:?}", method);
}
}
#[test]
fn test_empty_data_handling() {
let config = PhaseSanitizerConfig::default();
let sanitizer = PhaseSanitizer::new(config).unwrap();
let empty = Array2::<f64>::zeros((0, 0));
assert!(sanitizer.validate_phase_data(&empty).is_err());
assert!(sanitizer.unwrap_phase(&empty).is_err());
}
#[test]
fn test_statistics() {
let config = PhaseSanitizerConfig::default();
let mut sanitizer = PhaseSanitizer::new(config).unwrap();
let data = create_test_phase_data();
let _ = sanitizer.sanitize_phase(&data);
let _ = sanitizer.sanitize_phase(&data);
let stats = sanitizer.get_statistics();
assert_eq!(stats.total_processed, 2);
sanitizer.reset_statistics();
let stats = sanitizer.get_statistics();
assert_eq!(stats.total_processed, 0);
}
}