ad-plugins-rs 0.18.4

use std::sync::Arc;

use ad_core_rs::ndarray::{NDArray, NDDataBuffer, NDDataType, NDDimension};
use ad_core_rs::ndarray_pool::NDArrayPool;
use ad_core_rs::plugin::runtime::{NDPluginProcess, ProcessResult};
use rustfft::FftPlanner;
use rustfft::num_complex::Complex;

/// FFT mode selection.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FFTMode {
    Rows1D,
    Full2D,
}

/// FFT direction (forward or inverse transform).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FFTDirection {
    Forward,
    Inverse,
}

/// Configuration for FFT processing.
pub struct FFTConfig {
    pub mode: FFTMode,
    pub direction: FFTDirection,
    /// Zero out DC component (k=0) in the output magnitudes.
    pub suppress_dc: bool,
    /// Average N frames of magnitude. 0 or 1 means no averaging.
    pub num_average: usize,
}

impl Default for FFTConfig {
    fn default() -> Self {
        Self {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Forward,
            suppress_dc: false,
            num_average: 0,
        }
    }
}

/// Smallest power of two greater than or equal to `n` (C++ `nextPow2`).
///
/// `next_pow2(0)` and `next_pow2(1)` return 1.
pub fn next_pow2(n: usize) -> usize {
    if n <= 1 {
        return 1;
    }
    let mut p = 1usize;
    while p < n {
        p <<= 1;
    }
    p
}

/// Compute 1D FFT magnitude for each row of a 2D array using rustfft.
/// Returns a Float64 array with half the *padded* width (positive frequencies
/// only). Like C++ NDPluginFFT, each row is zero-padded to the next power of
/// two before the transform, and `nFreqX = paddedWidth / 2`.
/// Magnitudes are normalized by the padded length.
pub fn fft_1d_rows(src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
    if src.dims.is_empty() {
        return None;
    }

    let width = src.dims[0].size;
    let height = if src.dims.len() >= 2 {
        src.dims[1].size
    } else {
        1
    };

    if width == 0 {
        return None;
    }

    // C++ rounds the time dimension up to the next power of two and zero-pads.
    let padded = next_pow2(width);

    let mut planner = FftPlanner::<f64>::new();
    let fft = planner.plan_fft_forward(padded);

    // C++: nFreqX = paddedWidth / 2 (only positive frequencies)
    let n_freq = padded / 2;
    if n_freq == 0 {
        return None;
    }
    let scale = 1.0 / padded as f64;

    let mut magnitudes = vec![0.0f64; n_freq * height];
    let mut row_buf = vec![Complex::new(0.0, 0.0); padded];

    for row in 0..height {
        // Fill complex buffer: real = pixel value, imag = 0; tail zero-padded.
        for c in row_buf.iter_mut() {
            *c = Complex::new(0.0, 0.0);
        }
        for i in 0..width {
            row_buf[i] = Complex::new(src.data.get_as_f64(row * width + i).unwrap_or(0.0), 0.0);
        }

        fft.process(&mut row_buf);

        // Compute magnitudes (normalized by padded N, only first half)
        for i in 0..n_freq {
            magnitudes[row * n_freq + i] = row_buf[i].norm() * scale;
        }

        if suppress_dc {
            magnitudes[row * n_freq] = 0.0;
        }
    }

    let dims = if height > 1 {
        vec![NDDimension::new(n_freq), NDDimension::new(height)]
    } else {
        vec![NDDimension::new(n_freq)]
    };
    let mut arr = NDArray::new(dims, NDDataType::Float64);
    arr.data = NDDataBuffer::F64(magnitudes);
    arr.unique_id = src.unique_id;
    arr.timestamp = src.timestamp;
    arr.attributes = src.attributes.clone();
    Some(arr)
}

/// Compute 2D FFT magnitude using separable row-then-column FFT via rustfft.
pub fn fft_2d(src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
    if src.dims.len() < 2 {
        return None;
    }

    let src_w = src.dims[0].size;
    let src_h = src.dims[1].size;

    if src_w == 0 || src_h == 0 {
        return None;
    }

    // C++ zero-pads each dimension to the next power of two.
    let w = next_pow2(src_w);
    let h = next_pow2(src_h);

    let mut planner = FftPlanner::<f64>::new();
    let fft_row = planner.plan_fft_forward(w);
    let fft_col = planner.plan_fft_forward(h);

    // Step 1: Row FFTs — build a padded w×h complex buffer (zero-padded).
    let mut data = vec![Complex::new(0.0, 0.0); w * h];
    let mut row_buf = vec![Complex::new(0.0, 0.0); w];

    for row in 0..src_h {
        for c in row_buf.iter_mut() {
            *c = Complex::new(0.0, 0.0);
        }
        for i in 0..src_w {
            row_buf[i] = Complex::new(src.data.get_as_f64(row * src_w + i).unwrap_or(0.0), 0.0);
        }
        fft_row.process(&mut row_buf);
        data[row * w..(row * w + w)].copy_from_slice(&row_buf);
    }

    // Step 2: Column FFTs
    let mut col_buf = vec![Complex::new(0.0, 0.0); h];

    for col in 0..w {
        // Extract column
        for row in 0..h {
            col_buf[row] = data[row * w + col];
        }
        fft_col.process(&mut col_buf);
        // Write back
        for row in 0..h {
            data[row * w + col] = col_buf[row];
        }
    }

    // Step 3: Compute magnitudes (half spectrum, normalized by padded N*M)
    let n_freq_x = w / 2;
    let n_freq_y = h / 2;
    if n_freq_x == 0 || n_freq_y == 0 {
        return None;
    }
    let scale = 1.0 / (w * h) as f64;

    let mut magnitudes = vec![0.0f64; n_freq_x * n_freq_y];
    for fy in 0..n_freq_y {
        for fx in 0..n_freq_x {
            magnitudes[fy * n_freq_x + fx] = data[fy * w + fx].norm() * scale;
        }
    }

    if suppress_dc {
        magnitudes[0] = 0.0;
    }

    let dims = vec![NDDimension::new(n_freq_x), NDDimension::new(n_freq_y)];
    let mut arr = NDArray::new(dims, NDDataType::Float64);
    arr.data = NDDataBuffer::F64(magnitudes);
    arr.unique_id = src.unique_id;
    arr.timestamp = src.timestamp;
    arr.attributes = src.attributes.clone();
    Some(arr)
}

/// FFT processing engine with cached planner and optional magnitude averaging.
#[derive(Default)]
struct FFTParamIndices {
    direction: Option<usize>,
    suppress_dc: Option<usize>,
    num_average: Option<usize>,
    num_averaged: Option<usize>,
    reset_average: Option<usize>,
    time_per_point: Option<usize>,
    /// `FFTTimeSeries` waveform — the input time series (nTimeX points).
    time_series: Option<usize>,
    /// `FFTReal` waveform — real part of the spectrum (nFreqX points).
    real: Option<usize>,
    /// `FFTImaginary` waveform — imaginary part of the spectrum.
    imaginary: Option<usize>,
    /// `FFTAbsValue` waveform — magnitude of the spectrum.
    abs_value: Option<usize>,
    /// `FFTTimeAxis` waveform — `i * timePerPoint`.
    time_axis: Option<usize>,
    /// `FFTFreqAxis` waveform — frequency-axis values.
    freq_axis: Option<usize>,
}

pub struct FFTProcessor {
    config: FFTConfig,
    planner: FftPlanner<f64>,
    /// Running average magnitude buffer.
    avg_buffer: Option<Vec<f64>>,
    /// Number of frames accumulated so far.
    avg_count: usize,
    /// Cached dimensions to detect changes.
    cached_dims: Vec<usize>,
    /// Seconds per input time point (C++ `timePerPoint_`); scales the time
    /// and frequency axis waveforms.
    time_per_point: f64,
    params: FFTParamIndices,
}

impl FFTProcessor {
    pub fn new(mode: FFTMode) -> Self {
        Self {
            config: FFTConfig {
                mode,
                direction: FFTDirection::Forward,
                suppress_dc: false,
                num_average: 0,
            },
            planner: FftPlanner::new(),
            avg_buffer: None,
            avg_count: 0,
            cached_dims: Vec::new(),
            time_per_point: 1.0,
            params: FFTParamIndices::default(),
        }
    }

    pub fn with_config(config: FFTConfig) -> Self {
        Self {
            config,
            planner: FftPlanner::new(),
            avg_buffer: None,
            avg_count: 0,
            cached_dims: Vec::new(),
            time_per_point: 1.0,
            params: FFTParamIndices::default(),
        }
    }

    /// Check if dimensions changed and reset averaging state if so.
    fn check_dims_changed(&mut self, dims: &[NDDimension]) {
        let current: Vec<usize> = dims.iter().map(|d| d.size).collect();
        if current != self.cached_dims {
            self.cached_dims = current;
            self.avg_buffer = None;
            self.avg_count = 0;
        }
    }

    /// Compute FFT using cached planner for plan reuse across frames.
    fn compute_fft(&mut self, src: &NDArray) -> Option<NDArray> {
        let suppress_dc = self.config.suppress_dc;

        match (self.config.mode, self.config.direction) {
            (FFTMode::Rows1D, FFTDirection::Forward) => {
                self.compute_fft_1d_rows_forward(src, suppress_dc)
            }
            (FFTMode::Rows1D, FFTDirection::Inverse) => {
                self.compute_fft_1d_rows_inverse(src, suppress_dc)
            }
            (FFTMode::Full2D, FFTDirection::Forward) => {
                self.compute_fft_2d_forward(src, suppress_dc)
            }
            (FFTMode::Full2D, FFTDirection::Inverse) => {
                self.compute_fft_2d_inverse(src, suppress_dc)
            }
        }
    }

    /// Compute the 1D forward FFT of the first row of `src`, returning the
    /// extracted time series and the half-spectrum complex values.
    ///
    /// This drives the C++ `FFTTimeSeries`/`FFTReal`/`FFTImaginary`/
    /// `FFTAbsValue` waveform records, which in C++ are 1D arrays over the
    /// first time axis. Returns `(time_series, real, imag)` where `real`/
    /// `imag` have `padded/2` elements (nFreqX). The DC bin is zeroed in all
    /// three spectral arrays when `suppress_dc` is set (C++ behaviour).
    fn compute_row_spectrum(
        &mut self,
        src: &NDArray,
        suppress_dc: bool,
    ) -> Option<(Vec<f64>, Vec<f64>, Vec<f64>)> {
        if src.dims.is_empty() {
            return None;
        }
        let width = src.dims[0].size;
        if width == 0 {
            return None;
        }
        let padded = next_pow2(width);
        let n_freq = padded / 2;
        if n_freq == 0 {
            return None;
        }
        let fft = self.planner.plan_fft_forward(padded);

        // Extract the first row as the time series (nTimeX = width points).
        let time_series: Vec<f64> = (0..width)
            .map(|i| src.data.get_as_f64(i).unwrap_or(0.0))
            .collect();

        let mut row_buf = vec![Complex::new(0.0, 0.0); padded];
        for (i, &v) in time_series.iter().enumerate() {
            row_buf[i] = Complex::new(v, 0.0);
        }
        fft.process(&mut row_buf);

        let mut real = vec![0.0f64; n_freq];
        let mut imag = vec![0.0f64; n_freq];
        for i in 0..n_freq {
            real[i] = row_buf[i].re;
            imag[i] = row_buf[i].im;
        }
        if suppress_dc {
            real[0] = 0.0;
            imag[0] = 0.0;
        }
        Some((time_series, real, imag))
    }

    /// Frequency-axis values for `n_freq` bins (C++ `createAxisArrays`):
    /// `freqStep = 0.5 / timePerPoint / (nFreqX - 1)`.
    fn freq_axis(&self, n_freq: usize) -> Vec<f64> {
        if n_freq <= 1 {
            return vec![0.0; n_freq];
        }
        let tpp = if self.time_per_point > 0.0 {
            self.time_per_point
        } else {
            1.0
        };
        let step = 0.5 / tpp / (n_freq - 1) as f64;
        (0..n_freq).map(|i| i as f64 * step).collect()
    }

    /// Time-axis values for `n_time` points: `i * timePerPoint`.
    fn time_axis(&self, n_time: usize) -> Vec<f64> {
        let tpp = if self.time_per_point > 0.0 {
            self.time_per_point
        } else {
            1.0
        };
        (0..n_time).map(|i| i as f64 * tpp).collect()
    }

    fn compute_fft_1d_rows_forward(&mut self, src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
        if src.dims.is_empty() {
            return None;
        }

        let width = src.dims[0].size;
        let height = if src.dims.len() >= 2 {
            src.dims[1].size
        } else {
            1
        };

        if width == 0 {
            return None;
        }

        // C++ zero-pads the time series to the next power of two.
        let padded = next_pow2(width);
        let fft = self.planner.plan_fft_forward(padded);

        // C++: nFreqX = paddedWidth / 2 (only positive frequencies)
        let n_freq = padded / 2;
        if n_freq == 0 {
            return None;
        }
        let scale = 1.0 / padded as f64;

        let mut magnitudes = vec![0.0f64; n_freq * height];
        let mut row_buf = vec![Complex::new(0.0, 0.0); padded];

        for row in 0..height {
            for c in row_buf.iter_mut() {
                *c = Complex::new(0.0, 0.0);
            }
            for i in 0..width {
                row_buf[i] = Complex::new(src.data.get_as_f64(row * width + i).unwrap_or(0.0), 0.0);
            }
            fft.process(&mut row_buf);
            for i in 0..n_freq {
                magnitudes[row * n_freq + i] = row_buf[i].norm() * scale;
            }
            if suppress_dc {
                magnitudes[row * n_freq] = 0.0;
            }
        }

        let dims = if height > 1 {
            vec![NDDimension::new(n_freq), NDDimension::new(height)]
        } else {
            vec![NDDimension::new(n_freq)]
        };
        let mut arr = NDArray::new(dims, NDDataType::Float64);
        arr.data = NDDataBuffer::F64(magnitudes);
        arr.unique_id = src.unique_id;
        arr.timestamp = src.timestamp;
        arr.attributes = src.attributes.clone();
        Some(arr)
    }

    fn compute_fft_1d_rows_inverse(&mut self, src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
        if src.dims.is_empty() {
            return None;
        }

        let width = src.dims[0].size;
        let height = if src.dims.len() >= 2 {
            src.dims[1].size
        } else {
            1
        };

        if width == 0 {
            return None;
        }

        let fft = self.planner.plan_fft_inverse(width);
        let scale = 1.0 / width as f64;

        // An inverse transform of a real-valued spectrum yields signed real
        // samples: take the real part, not the modulus, so negative samples
        // survive a forward->inverse round trip.
        let mut samples = vec![0.0f64; width * height];
        let mut row_buf = vec![Complex::new(0.0, 0.0); width];

        for row in 0..height {
            for i in 0..width {
                row_buf[i] = Complex::new(src.data.get_as_f64(row * width + i).unwrap_or(0.0), 0.0);
            }
            if suppress_dc {
                row_buf[0] = Complex::new(0.0, 0.0);
            }
            fft.process(&mut row_buf);
            for (i, c) in row_buf.iter().enumerate() {
                samples[row * width + i] = c.re * scale;
            }
        }

        let dims = src.dims.clone();
        let mut arr = NDArray::new(dims, NDDataType::Float64);
        arr.data = NDDataBuffer::F64(samples);
        arr.unique_id = src.unique_id;
        arr.timestamp = src.timestamp;
        arr.attributes = src.attributes.clone();
        Some(arr)
    }

    fn compute_fft_2d_forward(&mut self, src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
        if src.dims.len() < 2 {
            return None;
        }

        let src_w = src.dims[0].size;
        let src_h = src.dims[1].size;

        if src_w == 0 || src_h == 0 {
            return None;
        }

        // C++ zero-pads each dimension to the next power of two.
        let w = next_pow2(src_w);
        let h = next_pow2(src_h);

        let fft_row = self.planner.plan_fft_forward(w);
        let fft_col = self.planner.plan_fft_forward(h);

        let mut data = vec![Complex::new(0.0, 0.0); w * h];
        let mut row_buf = vec![Complex::new(0.0, 0.0); w];

        for row in 0..src_h {
            for c in row_buf.iter_mut() {
                *c = Complex::new(0.0, 0.0);
            }
            for i in 0..src_w {
                row_buf[i] = Complex::new(src.data.get_as_f64(row * src_w + i).unwrap_or(0.0), 0.0);
            }
            fft_row.process(&mut row_buf);
            data[row * w..(row * w + w)].copy_from_slice(&row_buf);
        }

        let mut col_buf = vec![Complex::new(0.0, 0.0); h];
        for col in 0..w {
            for row in 0..h {
                col_buf[row] = data[row * w + col];
            }
            fft_col.process(&mut col_buf);
            for row in 0..h {
                data[row * w + col] = col_buf[row];
            }
        }

        // C++: nFreqX = paddedX/2, nFreqY = paddedY/2; normalize by padded N*M
        let n_freq_x = w / 2;
        let n_freq_y = h / 2;
        if n_freq_x == 0 || n_freq_y == 0 {
            return None;
        }
        let scale = 1.0 / (w * h) as f64;

        let mut magnitudes = vec![0.0f64; n_freq_x * n_freq_y];
        for fy in 0..n_freq_y {
            for fx in 0..n_freq_x {
                magnitudes[fy * n_freq_x + fx] = data[fy * w + fx].norm() * scale;
            }
        }

        if suppress_dc {
            magnitudes[0] = 0.0;
        }

        let dims = vec![NDDimension::new(n_freq_x), NDDimension::new(n_freq_y)];
        let mut arr = NDArray::new(dims, NDDataType::Float64);
        arr.data = NDDataBuffer::F64(magnitudes);
        arr.unique_id = src.unique_id;
        arr.timestamp = src.timestamp;
        arr.attributes = src.attributes.clone();
        Some(arr)
    }

    fn compute_fft_2d_inverse(&mut self, src: &NDArray, suppress_dc: bool) -> Option<NDArray> {
        if src.dims.len() < 2 {
            return None;
        }

        let w = src.dims[0].size;
        let h = src.dims[1].size;

        if w == 0 || h == 0 {
            return None;
        }

        let fft_row = self.planner.plan_fft_inverse(w);
        let fft_col = self.planner.plan_fft_inverse(h);
        let scale = 1.0 / (w * h) as f64;

        let mut data = vec![Complex::new(0.0, 0.0); w * h];
        for i in 0..w * h {
            data[i] = Complex::new(src.data.get_as_f64(i).unwrap_or(0.0), 0.0);
        }

        if suppress_dc {
            data[0] = Complex::new(0.0, 0.0);
        }

        let mut col_buf = vec![Complex::new(0.0, 0.0); h];
        for col in 0..w {
            for row in 0..h {
                col_buf[row] = data[row * w + col];
            }
            fft_col.process(&mut col_buf);
            for row in 0..h {
                data[row * w + col] = col_buf[row];
            }
        }

        let mut row_buf = vec![Complex::new(0.0, 0.0); w];
        for row in 0..h {
            row_buf.copy_from_slice(&data[row * w..(row * w + w)]);
            fft_row.process(&mut row_buf);
            data[row * w..(row * w + w)].copy_from_slice(&row_buf);
        }

        // Inverse transform yields signed real samples: keep the real part.
        let samples: Vec<f64> = data.iter().map(|c| c.re * scale).collect();

        let dims = vec![NDDimension::new(w), NDDimension::new(h)];
        let mut arr = NDArray::new(dims, NDDataType::Float64);
        arr.data = NDDataBuffer::F64(samples);
        arr.unique_id = src.unique_id;
        arr.timestamp = src.timestamp;
        arr.attributes = src.attributes.clone();
        Some(arr)
    }

    /// Apply magnitude averaging using exponential moving average (matching C++).
    ///
    /// C++: `FFTAbsValue_[j] = FFTAbsValue_[j] * oldFraction + new[j] * newFraction`
    /// where `oldFraction = 1 - 1/numAveraged`, `newFraction = 1/numAveraged`.
    fn apply_averaging(&mut self, magnitudes: &[f64]) -> Vec<f64> {
        let num_avg = self.config.num_average;
        if num_avg <= 1 {
            return magnitudes.to_vec();
        }

        let buf = self
            .avg_buffer
            .get_or_insert_with(|| vec![0.0; magnitudes.len()]);

        // Reset if buffer size changed
        if buf.len() != magnitudes.len() {
            *buf = vec![0.0; magnitudes.len()];
            self.avg_count = 0;
        }

        self.avg_count += 1;
        // Cap at num_average for the weighting
        let n = self.avg_count.min(num_avg) as f64;
        let new_fraction = 1.0 / n;
        let old_fraction = 1.0 - new_fraction;

        // C++ exponential moving average
        for (b, &m) in buf.iter_mut().zip(magnitudes.iter()) {
            *b = *b * old_fraction + m * new_fraction;
        }

        buf.clone()
    }
}

impl NDPluginProcess for FFTProcessor {
    fn process_array(&mut self, array: &NDArray, _pool: &NDArrayPool) -> ProcessResult {
        use ad_core_rs::plugin::runtime::ParamUpdate;

        self.check_dims_changed(&array.dims);

        let result = self.compute_fft(array);
        let mut updates = Vec::new();
        if let Some(idx) = self.params.num_averaged {
            updates.push(ParamUpdate::int32(idx, self.avg_count as i32));
        }

        // Emit the C++ NDPluginFFT waveform records. On a forward transform
        // these are the time series, the real/imaginary/abs spectrum, and
        // the time/frequency axes (C++ doFFTCallbacks / createAxisArrays).
        // The inverse transform has no spectrum to publish.
        //
        // `apply_averaging` advances the EMA state, so it must be invoked at
        // most once per frame. The averaged FFTAbsValue waveform and the
        // averaged NDArray output therefore share a single averaging pass.
        let mut averaged_mags: Option<Vec<f64>> = None;
        if self.config.direction == FFTDirection::Forward {
            let suppress_dc = self.config.suppress_dc;
            if let Some((time_series, real, imag)) = self.compute_row_spectrum(array, suppress_dc) {
                let n_time = time_series.len();
                let n_freq = real.len();
                if let Some(idx) = self.params.time_series {
                    updates.push(ParamUpdate::float64_array(idx, time_series));
                }
                if let Some(idx) = self.params.real {
                    updates.push(ParamUpdate::float64_array(idx, real));
                }
                if let Some(idx) = self.params.imaginary {
                    updates.push(ParamUpdate::float64_array(idx, imag));
                }
                if let Some(idx) = self.params.time_axis {
                    updates.push(ParamUpdate::float64_array(idx, self.time_axis(n_time)));
                }
                if let Some(idx) = self.params.freq_axis {
                    updates.push(ParamUpdate::float64_array(idx, self.freq_axis(n_freq)));
                }
            }
        }

        match result {
            Some(mut out) => {
                if self.config.num_average > 1 {
                    if let NDDataBuffer::F64(ref mags) = out.data {
                        let averaged = self.apply_averaging(mags);
                        averaged_mags = Some(averaged.clone());
                        out.data = NDDataBuffer::F64(averaged);
                    }
                }
                // FFTAbsValue waveform mirrors the (possibly averaged) NDArray
                // magnitude buffer — for 1D forward this is the half-spectrum
                // magnitude that the NDArray output already carries.
                if self.config.direction == FFTDirection::Forward {
                    if let Some(idx) = self.params.abs_value {
                        let abs = match (&averaged_mags, &out.data) {
                            (Some(avg), _) => avg.clone(),
                            (None, NDDataBuffer::F64(mags)) => mags.clone(),
                            _ => Vec::new(),
                        };
                        if !abs.is_empty() {
                            updates.push(ParamUpdate::float64_array(idx, abs));
                        }
                    }
                }
                let mut r = ProcessResult::arrays(vec![Arc::new(out)]);
                r.param_updates = updates;
                r
            }
            None => ProcessResult::sink(updates),
        }
    }

    fn plugin_type(&self) -> &str {
        "NDPluginFFT"
    }

    fn register_params(
        &mut self,
        base: &mut asyn_rs::port::PortDriverBase,
    ) -> asyn_rs::error::AsynResult<()> {
        use asyn_rs::param::ParamType;
        base.create_param("FFT_TIME_PER_POINT", ParamType::Float64)?;
        base.create_param("FFT_TIME_AXIS", ParamType::Float64Array)?;
        base.create_param("FFT_FREQ_AXIS", ParamType::Float64Array)?;
        base.create_param("FFT_DIRECTION", ParamType::Int32)?;
        base.create_param("FFT_SUPPRESS_DC", ParamType::Int32)?;
        base.create_param("FFT_NUM_AVERAGE", ParamType::Int32)?;
        base.create_param("FFT_NUM_AVERAGED", ParamType::Int32)?;
        base.create_param("FFT_RESET_AVERAGE", ParamType::Int32)?;
        base.create_param("FFT_TIME_SERIES", ParamType::Float64Array)?;
        base.create_param("FFT_REAL", ParamType::Float64Array)?;
        base.create_param("FFT_IMAGINARY", ParamType::Float64Array)?;
        base.create_param("FFT_ABS_VALUE", ParamType::Float64Array)?;

        self.params.direction = base.find_param("FFT_DIRECTION");
        self.params.suppress_dc = base.find_param("FFT_SUPPRESS_DC");
        self.params.num_average = base.find_param("FFT_NUM_AVERAGE");
        self.params.num_averaged = base.find_param("FFT_NUM_AVERAGED");
        self.params.reset_average = base.find_param("FFT_RESET_AVERAGE");
        self.params.time_per_point = base.find_param("FFT_TIME_PER_POINT");
        self.params.time_series = base.find_param("FFT_TIME_SERIES");
        self.params.real = base.find_param("FFT_REAL");
        self.params.imaginary = base.find_param("FFT_IMAGINARY");
        self.params.abs_value = base.find_param("FFT_ABS_VALUE");
        self.params.time_axis = base.find_param("FFT_TIME_AXIS");
        self.params.freq_axis = base.find_param("FFT_FREQ_AXIS");
        Ok(())
    }

    fn on_param_change(
        &mut self,
        reason: usize,
        params: &ad_core_rs::plugin::runtime::PluginParamSnapshot,
    ) -> ad_core_rs::plugin::runtime::ParamChangeResult {
        if Some(reason) == self.params.direction {
            self.config.direction = if params.value.as_i32() == 0 {
                FFTDirection::Forward
            } else {
                FFTDirection::Inverse
            };
        } else if Some(reason) == self.params.suppress_dc {
            self.config.suppress_dc = params.value.as_i32() != 0;
        } else if Some(reason) == self.params.num_average {
            self.config.num_average = params.value.as_i32().max(0) as usize;
        } else if Some(reason) == self.params.reset_average {
            if params.value.as_i32() != 0 {
                self.avg_buffer = None;
                self.avg_count = 0;
            }
        } else if Some(reason) == self.params.time_per_point {
            // Scales the FFTTimeAxis / FFTFreqAxis waveforms.
            let v = params.value.as_f64();
            if v > 0.0 {
                self.time_per_point = v;
            }
        }
        ad_core_rs::plugin::runtime::ParamChangeResult::updates(vec![])
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_fft_1d_dc() {
        // Constant signal: DC component should dominate
        let mut arr = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..8 {
                v[i] = 1.0;
            }
        }

        let result = fft_1d_rows(&arr, false).unwrap();
        // Output is half spectrum: N/2 = 4 bins
        assert_eq!(result.dims[0].size, 4);
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC component normalized by N: 8/8 = 1.0
            assert!((v[0] - 1.0).abs() < 1e-10);
            // Other components should be ~0
            assert!(v[1].abs() < 1e-10);
        }
    }

    #[test]
    fn test_fft_1d_sine() {
        // Sine wave at frequency 1: peak at k=1 and k=N-1
        let n = 16;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..n {
                v[i] = (2.0 * std::f64::consts::PI * i as f64 / n as f64).sin();
            }
        }

        let result = fft_1d_rows(&arr, false).unwrap();
        // Output is N/2 = 8 bins
        assert_eq!(result.dims[0].size, 8);
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC should be ~0
            assert!(v[0].abs() < 1e-10);
            // Peak at k=1, normalized by N: magnitude = N/2 / N = 0.5
            assert!((v[1] - 0.5).abs() < 1e-10);
            // k=2 should be small
            assert!(v[2].abs() < 1e-10);
        }
    }

    #[test]
    fn test_fft_2d_dimensions() {
        let arr = NDArray::new(
            vec![NDDimension::new(4), NDDimension::new(4)],
            NDDataType::UInt8,
        );
        let result = fft_2d(&arr, false).unwrap();
        // Half spectrum: 4/2 x 4/2 = 2x2
        assert_eq!(result.dims[0].size, 2);
        assert_eq!(result.dims[1].size, 2);
        assert_eq!(result.data.data_type(), NDDataType::Float64);
    }

    #[test]
    fn test_fft_1d_suppress_dc() {
        // Constant signal: DC component should be suppressed
        let mut arr = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..8 {
                v[i] = 1.0;
            }
        }

        let result = fft_1d_rows(&arr, true).unwrap();
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC component should be zeroed out
            assert!((v[0]).abs() < 1e-15);
            // Other components should still be ~0 for constant signal
            assert!(v[1].abs() < 1e-10);
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_fft_2d_suppress_dc() {
        // 4x4 constant array, suppress_dc should zero out [0,0]
        let mut arr = NDArray::new(
            vec![NDDimension::new(4), NDDimension::new(4)],
            NDDataType::Float64,
        );
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for val in v.iter_mut() {
                *val = 3.0;
            }
        }

        let result = fft_2d(&arr, true).unwrap();
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC at [0,0] should be zeroed
            assert!((v[0]).abs() < 1e-15);
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_fft_2d_known_dc() {
        // 4x4 constant=2.0 => DC = 4*4*2 = 32, normalized by 4*4 = 16 => 2.0
        let mut arr = NDArray::new(
            vec![NDDimension::new(4), NDDimension::new(4)],
            NDDataType::Float64,
        );
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for val in v.iter_mut() {
                *val = 2.0;
            }
        }

        let result = fft_2d(&arr, false).unwrap();
        // Half spectrum: 2x2
        assert_eq!(result.dims[0].size, 2);
        assert_eq!(result.dims[1].size, 2);
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC normalized by N*M: 32 / 16 = 2.0
            assert!((v[0] - 2.0).abs() < 1e-10, "DC = {}, expected 2", v[0]);
            // All other bins should be ~0
            for i in 1..v.len() {
                assert!(v[i].abs() < 1e-10, "bin {} = {}, expected ~0", i, v[i]);
            }
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_fft_1d_known_cosine_peaks() {
        // Cosine at frequency 3 in N=16: peaks at k=3 and k=N-3=13
        let n = 16;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..n {
                v[i] = (2.0 * std::f64::consts::PI * 3.0 * i as f64 / n as f64).cos();
            }
        }

        let result = fft_1d_rows(&arr, false).unwrap();
        // Half spectrum: 8 bins
        assert_eq!(result.dims[0].size, 8);
        if let NDDataBuffer::F64(ref v) = result.data {
            // DC should be ~0
            assert!(v[0].abs() < 1e-10);
            // k=3 should have magnitude N/2 / N = 8/16 = 0.5
            assert!(
                (v[3] - 0.5).abs() < 1e-10,
                "k=3 magnitude = {}, expected 0.5",
                v[3]
            );
            // Other bins in first half should be ~0
            for k in [1, 2, 4, 5, 6, 7] {
                assert!(
                    v[k].abs() < 1e-10,
                    "k={} magnitude = {}, expected ~0",
                    k,
                    v[k]
                );
            }
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_processor_with_config() {
        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Forward,
            suppress_dc: true,
            num_average: 0,
        };
        let mut proc = FFTProcessor::with_config(config);
        let pool = NDArrayPool::new(0);

        let mut arr = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..8 {
                v[i] = 5.0;
            }
        }

        let result = proc.process_array(&arr, &pool);
        assert_eq!(result.output_arrays.len(), 1);
        if let NDDataBuffer::F64(ref v) = result.output_arrays[0].data {
            // suppress_dc: DC should be 0
            assert!(v[0].abs() < 1e-15);
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_processor_averaging() {
        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Forward,
            suppress_dc: false,
            num_average: 2,
        };
        let mut proc = FFTProcessor::with_config(config);
        let pool = NDArrayPool::new(0);

        // Frame 1: constant = 2.0 => DC magnitude (normalized) = 2.0
        let mut arr1 = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr1.data {
            for i in 0..8 {
                v[i] = 2.0;
            }
        }

        // Frame 2: constant = 4.0 => DC magnitude (normalized) = 4.0
        let mut arr2 = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr2.data {
            for i in 0..8 {
                v[i] = 4.0;
            }
        }

        let r1 = proc.process_array(&arr1, &pool);
        assert_eq!(r1.output_arrays.len(), 1);
        // After 1 frame: exponential avg with N=1, so output = 2.0
        if let NDDataBuffer::F64(ref v) = r1.output_arrays[0].data {
            assert!((v[0] - 2.0).abs() < 1e-10, "partial avg DC = {}", v[0]);
        }

        let r2 = proc.process_array(&arr2, &pool);
        assert_eq!(r2.output_arrays.len(), 1);
        // After 2 frames: exp avg = 2.0*(1-1/2) + 4.0*(1/2) = 1.0 + 2.0 = 3.0
        if let NDDataBuffer::F64(ref v) = r2.output_arrays[0].data {
            assert!((v[0] - 3.0).abs() < 1e-10, "averaged DC = {}", v[0]);
        }
    }

    #[test]
    fn test_processor_averaging_dimension_change_resets() {
        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Forward,
            suppress_dc: false,
            num_average: 3,
        };
        let mut proc = FFTProcessor::with_config(config);
        let pool = NDArrayPool::new(0);

        // Frame 1: width=8
        let mut arr1 = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr1.data {
            for i in 0..8 {
                v[i] = 1.0;
            }
        }
        let _ = proc.process_array(&arr1, &pool);
        assert_eq!(proc.avg_count, 1);

        // Frame 2: width=4 — dimension change should reset
        let mut arr2 = NDArray::new(vec![NDDimension::new(4)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr2.data {
            for i in 0..4 {
                v[i] = 1.0;
            }
        }
        let _ = proc.process_array(&arr2, &pool);
        // After dimension change, avg_count should be 1 (reset + one new frame)
        assert_eq!(proc.avg_count, 1);
    }

    #[test]
    fn test_fft_1d_multirow() {
        // 2 rows, each a different constant
        let w = 4;
        let h = 2;
        let mut arr = NDArray::new(
            vec![NDDimension::new(w), NDDimension::new(h)],
            NDDataType::Float64,
        );
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            // Row 0: all 1.0
            for i in 0..w {
                v[i] = 1.0;
            }
            // Row 1: all 3.0
            for i in w..2 * w {
                v[i] = 3.0;
            }
        }

        let result = fft_1d_rows(&arr, false).unwrap();
        let n_freq = w / 2; // half spectrum
        assert_eq!(result.dims[0].size, n_freq);
        if let NDDataBuffer::F64(ref v) = result.data {
            // Row 0 DC = 4*1/4 = 1.0 (normalized by N=4)
            assert!((v[0] - 1.0).abs() < 1e-10);
            // Row 1 DC = 4*3/4 = 3.0
            assert!((v[n_freq] - 3.0).abs() < 1e-10);
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_inverse_fft_1d() {
        // IFFT of a known forward FFT should give back the original magnitudes
        // For a real constant signal, forward FFT gives [N, 0, 0, ...0]
        // IFFT of [N, 0, ...0] (real input) should give constant = 1.0 for each sample
        let n = 8;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            v[0] = 8.0; // DC = N
            // rest are 0
        }

        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Inverse,
            suppress_dc: false,
            num_average: 0,
        };
        let mut proc = FFTProcessor::with_config(config);
        let pool = NDArrayPool::new(0);

        let result = proc.process_array(&arr, &pool);
        assert_eq!(result.output_arrays.len(), 1);
        if let NDDataBuffer::F64(ref v) = result.output_arrays[0].data {
            // Each sample should be magnitude 1.0 (8/8 = 1.0 after normalization)
            for i in 0..n {
                assert!(
                    (v[i] - 1.0).abs() < 1e-10,
                    "sample {} = {}, expected 1.0",
                    i,
                    v[i]
                );
            }
        } else {
            panic!("expected F64 data");
        }
    }

    #[test]
    fn test_fft_preserves_metadata() {
        let mut arr = NDArray::new(vec![NDDimension::new(4)], NDDataType::Float64);
        arr.unique_id = 42;
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            v[0] = 1.0;
        }

        let result = fft_1d_rows(&arr, false).unwrap();
        assert_eq!(result.unique_id, 42);
        assert_eq!(result.timestamp, arr.timestamp);
    }

    #[test]
    fn test_next_pow2() {
        assert_eq!(next_pow2(0), 1);
        assert_eq!(next_pow2(1), 1);
        assert_eq!(next_pow2(2), 2);
        assert_eq!(next_pow2(3), 4);
        assert_eq!(next_pow2(5), 8);
        assert_eq!(next_pow2(8), 8);
        assert_eq!(next_pow2(100), 128);
    }

    #[test]
    fn test_fft_1d_pads_to_power_of_two() {
        // Regression: a non-power-of-2 width is zero-padded to the next
        // power of two; nFreqX = paddedWidth / 2.
        let n = 5; // -> padded to 8 -> n_freq = 4
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..n {
                v[i] = 1.0;
            }
        }
        let result = fft_1d_rows(&arr, false).unwrap();
        assert_eq!(result.dims[0].size, 4); // 8 / 2, not 5 / 2 = 2
    }

    #[test]
    fn test_fft_2d_pads_to_power_of_two() {
        // 6x3 -> padded 8x4 -> n_freq 4x2.
        let arr = NDArray::new(
            vec![NDDimension::new(6), NDDimension::new(3)],
            NDDataType::Float64,
        );
        let result = fft_2d(&arr, false).unwrap();
        assert_eq!(result.dims[0].size, 4); // 8 / 2
        assert_eq!(result.dims[1].size, 2); // 4 / 2
    }

    // ---- FFT waveform emission tests ----

    use ad_core_rs::plugin::runtime::ParamUpdate;

    /// Register the FFT params on a scratch port and return the processor.
    fn fft_proc_with_params(config: FFTConfig) -> FFTProcessor {
        let mut proc = FFTProcessor::with_config(config);
        let mut base =
            asyn_rs::port::PortDriverBase::new("FFT_TEST", 1, asyn_rs::port::PortFlags::default());
        proc.register_params(&mut base).unwrap();
        proc
    }

    /// Find a Float64Array update by param reason.
    fn find_array_update(updates: &[ParamUpdate], reason: usize) -> Option<&[f64]> {
        updates.iter().find_map(|u| match u {
            ParamUpdate::Float64Array {
                reason: r, value, ..
            } if *r == reason => Some(value.as_slice()),
            _ => None,
        })
    }

    #[test]
    fn test_fft_emits_all_waveforms() {
        // A forward FFT must emit FFTTimeSeries, FFTReal, FFTImaginary,
        // FFTAbsValue, FFTTimeAxis and FFTFreqAxis waveforms.
        let mut proc = fft_proc_with_params(FFTConfig::default());
        let pool = NDArrayPool::new(0);

        let n = 16;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..n {
                v[i] = (2.0 * std::f64::consts::PI * 3.0 * i as f64 / n as f64).cos();
            }
        }
        let result = proc.process_array(&arr, &pool);
        let u = &result.param_updates;

        // All six FFT waveforms must be present, addressed by their param
        // reasons, and carry non-empty payloads.
        for reason in [
            proc.params.time_series.unwrap(),
            proc.params.real.unwrap(),
            proc.params.imaginary.unwrap(),
            proc.params.abs_value.unwrap(),
            proc.params.time_axis.unwrap(),
            proc.params.freq_axis.unwrap(),
        ] {
            let wf = find_array_update(u, reason)
                .unwrap_or_else(|| panic!("missing waveform for reason {reason}"));
            assert!(!wf.is_empty(), "waveform {reason} is empty");
        }
        let array_updates = u
            .iter()
            .filter(|x| matches!(x, ParamUpdate::Float64Array { .. }))
            .count();
        assert_eq!(
            array_updates, 6,
            "expected 6 waveform updates, got {array_updates}"
        );
    }

    #[test]
    fn test_fft_real_imaginary_match_spectrum() {
        // For a cosine at frequency 3 in N=16, the real part peaks at bin 3
        // (cosine -> real, even) and the imaginary part is ~0 everywhere.
        let mut proc = fft_proc_with_params(FFTConfig::default());
        let real_reason = proc.params.real.unwrap();
        let imag_reason = proc.params.imaginary.unwrap();
        let abs_reason = proc.params.abs_value.unwrap();
        let ts_reason = proc.params.time_series.unwrap();
        let pool = NDArrayPool::new(0);

        let n = 16;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            for i in 0..n {
                v[i] = (2.0 * std::f64::consts::PI * 3.0 * i as f64 / n as f64).cos();
            }
        }
        let result = proc.process_array(&arr, &pool);
        let u = &result.param_updates;

        let real = find_array_update(u, real_reason).unwrap();
        let imag = find_array_update(u, imag_reason).unwrap();
        let abs = find_array_update(u, abs_reason).unwrap();
        let ts = find_array_update(u, ts_reason).unwrap();

        // n_freq = 16/2 = 8.
        assert_eq!(real.len(), 8);
        assert_eq!(imag.len(), 8);
        // Real part of a cosine: peak at bin 3 (= N/2 = 8), zero elsewhere.
        assert!((real[3] - 8.0).abs() < 1e-9, "real[3] = {}", real[3]);
        for k in [0usize, 1, 2, 4, 5, 6, 7] {
            assert!(real[k].abs() < 1e-9, "real[{k}] = {}", real[k]);
            assert!(imag[k].abs() < 1e-9, "imag[{k}] = {}", imag[k]);
        }
        // imag[3] is also ~0 for a pure cosine.
        assert!(imag[3].abs() < 1e-9, "imag[3] = {}", imag[3]);
        // FFTAbsValue at bin 3: magnitude 8 normalized by N=16 -> 0.5.
        assert!((abs[3] - 0.5).abs() < 1e-9, "abs[3] = {}", abs[3]);
        // Time series is the raw input row.
        assert_eq!(ts.len(), n);
        assert!((ts[0] - 1.0).abs() < 1e-9);
    }

    #[test]
    fn test_fft_axes_scale_with_time_per_point() {
        // FFTTimeAxis = i*timePerPoint; FFTFreqAxis step = 0.5/tpp/(nFreq-1).
        let mut proc = fft_proc_with_params(FFTConfig::default());
        let time_axis_reason = proc.params.time_axis.unwrap();
        let freq_axis_reason = proc.params.freq_axis.unwrap();
        let tpp_reason = proc.params.time_per_point.unwrap();
        let pool = NDArrayPool::new(0);

        // Set timePerPoint = 0.5 s.
        use ad_core_rs::plugin::runtime::{ParamChangeValue, PluginParamSnapshot};
        proc.on_param_change(
            tpp_reason,
            &PluginParamSnapshot {
                enable_callbacks: true,
                reason: tpp_reason,
                addr: 0,
                value: ParamChangeValue::Float64(0.5),
            },
        );

        let n = 8;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            v[0] = 1.0;
        }
        let result = proc.process_array(&arr, &pool);
        let u = &result.param_updates;

        let time_axis = find_array_update(u, time_axis_reason).unwrap();
        let freq_axis = find_array_update(u, freq_axis_reason).unwrap();

        // Time axis: 8 points stepped by 0.5.
        assert_eq!(time_axis.len(), 8);
        assert!((time_axis[1] - 0.5).abs() < 1e-12);
        assert!((time_axis[7] - 3.5).abs() < 1e-12);
        // Freq axis: 4 bins, step = 0.5 / 0.5 / (4-1) = 1/3.
        assert_eq!(freq_axis.len(), 4);
        let step = 0.5 / 0.5 / 3.0;
        assert!((freq_axis[1] - step).abs() < 1e-12);
        assert!((freq_axis[3] - 3.0 * step).abs() < 1e-12);
    }

    #[test]
    fn test_fft_inverse_emits_no_spectrum_waveforms() {
        // The inverse transform has no spectrum to publish.
        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Inverse,
            suppress_dc: false,
            num_average: 0,
        };
        let mut proc = fft_proc_with_params(config);
        let pool = NDArrayPool::new(0);
        let mut arr = NDArray::new(vec![NDDimension::new(8)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            v[0] = 8.0;
        }
        let result = proc.process_array(&arr, &pool);
        let array_updates = result
            .param_updates
            .iter()
            .filter(|x| matches!(x, ParamUpdate::Float64Array { .. }))
            .count();
        assert_eq!(
            array_updates, 0,
            "inverse FFT must not emit spectrum waveforms"
        );
    }

    #[test]
    fn test_inverse_fft_preserves_sign() {
        // Regression: the inverse transform must yield signed real samples.
        // Build a spectrum whose inverse is a signed cosine and verify the
        // output contains negative values (the old code took the modulus).
        let n = 8;
        let mut arr = NDArray::new(vec![NDDimension::new(n)], NDDataType::Float64);
        if let NDDataBuffer::F64(ref mut v) = arr.data {
            // Spectrum with a single non-DC bin: inverse is a real cosine
            // that swings negative.
            v[1] = 4.0;
            v[n - 1] = 4.0;
        }
        let config = FFTConfig {
            mode: FFTMode::Rows1D,
            direction: FFTDirection::Inverse,
            suppress_dc: false,
            num_average: 0,
        };
        let mut proc = FFTProcessor::with_config(config);
        let pool = NDArrayPool::new(0);
        let result = proc.process_array(&arr, &pool);
        if let NDDataBuffer::F64(ref v) = result.output_arrays[0].data {
            let has_negative = v.iter().any(|&x| x < -1e-6);
            assert!(
                has_negative,
                "inverse FFT must keep negative samples: {v:?}"
            );
        } else {
            panic!("expected F64 data");
        }
    }
}