tulip_rs 0.1.15

//use crate::common::validate_inputs;
use crate::common_simd::options::{validate_inputs, validate_options};
use crate::indicators::simd_indicators::road_train::{Asset, Driver, PrimeMover};
use crate::indicators::simd_indicators::wilders_simd::calc_simd;
use crate::indicators::wilders::{
    init_state, min_data, multiplier, output_length, IndicatorState, INPUTS_WIDTH, OPTIONS_WIDTH,
};
use crate::types::IndicatorError;
use std::simd::Simd;

/// SIMD driver for the Wilder's Smoothing (WILDERS) indicator, processing `N` option-set lanes per scheduling epoch.
struct WildersDriver {}

impl Driver<f64, (f64, f64)> for WildersDriver {
    /// Processes one epoch of output bars for `N` option-set lanes simultaneously using SIMD.
    fn next_run<const N: usize>(
        &mut self,
        inputs: Vec<Vec<&[f64]>>,
        mut outputs: Vec<Vec<&mut [f64]>>,
        mut states: Vec<&mut f64>,
        options: Vec<Option<&(f64, f64)>>,
    ) {
        let len = outputs[0][0].len();

        // Optimization 1: Direct array construction instead of collect+try_into
        let mut wilders = Simd::<f64, N>::from_array(std::array::from_fn(|i| unsafe {
            **states.get_unchecked(i)
        }));

        let multipliers = {
            let mut multipliers = ([0.0; N], [0.0; N]);
            for (lane, option) in options.iter().enumerate() {
                if let Some(&multiplier) = option {
                    //println!("{:?}", outputs[lane][0].len());
                    multipliers.0[lane] = multiplier.0;
                    multipliers.1[lane] = multiplier.1;
                }
            }
            (
                Simd::from_array(multipliers.0),
                Simd::from_array(multipliers.1),
            )
        };

        // Optimization 2: Pre-compute all input and output pointers
        let input_ptrs = crate::extract_input_ptrs!(inputs, N, real_ptrs);
        let output_ptrs = crate::extract_output_ptrs!(outputs, N, sma_line_ptr);

        // Optimization 3: Simplified main loop with pre-computed offsets
        for i in 0..len {
            let real = crate::extract_simd_inputs_at_index_splat!(i, N,
                new @ input_ptrs
            );

            wilders = calc_simd(wilders, real, multipliers);

            crate::write_simd_at_indices!(N, i,
                output_ptrs => wilders
            );
        }

        // Update states efficiently
        let final_wilders = wilders.to_array();
        for (i, state) in states.iter_mut().enumerate().take(N) {
            **state = final_wilders[i];
        }
    }
}

/// Calculates Wilder's Smoothing (WILDERS) for one shared asset across `N` different
/// option sets simultaneously using SIMD parallelism.
///
/// Uses the [`PrimeMover`] scheduler to batch option sets into SIMD-width groups.
///
/// # Arguments
/// * `inputs` - Shared input data: `inputs[0]` is `&[f64]` containing `real` (price series).
/// * `options` - An array of `N` option sets; `options[i]` is `&[f64; OPTIONS_WIDTH]` containing
///   `[period]` for option set `i`.
/// * `optional_outputs` - Unused; WILDERS has no optional outputs.
///
/// # Returns
/// `Ok((outputs, states))` where `outputs[i][0]` is `wilders` for option set `i`
/// and `states[i]` is the final [`IndicatorState`] for option set `i`.
/// Returns `Err(IndicatorError)` if any input slice is too short or any option set is invalid.
pub fn indicator_by_options<const N: usize>(
    inputs: &[&[f64]; INPUTS_WIDTH],
    options: &[&[f64; OPTIONS_WIDTH]; N],
    _optional_outputs: Option<&[bool]>,
) -> Result<(Vec<Vec<Vec<f64>>>, Vec<IndicatorState>), IndicatorError> {
    validate_inputs::<OPTIONS_WIDTH>(inputs, options, min_data)?;
    validate_options(options, None)?;
    let params: [(f64, f64); N] = std::array::from_fn(|i| multiplier(options[i][0] as usize));

    let mut road_train = PrimeMover::<N, f64, (f64, f64)>::new();
    let mut output_buffers: Vec<Vec<Vec<f64>>> = (0..N)
        .map(|i| {
            vec![{
                let capacity = output_length(inputs[0].len(), options[i]);
                crate::uninit_vec!(f64, capacity)
            }]
        })
        .collect();

    for (i, option) in options.iter().enumerate() {
        let period = option[0] as usize;
        let asset_inputs = vec![inputs[0]];

        let (state, _) = init_state(inputs[0], period);
        unsafe {
            // Get a mutable reference to the output buffer for this asset
            let output_buffer = &mut output_buffers[i][0];
            let asset_outputs = vec![std::slice::from_raw_parts_mut(
                output_buffer.as_mut_ptr(),
                output_buffer.len(),
            )];

            road_train.add_asset(Asset::new(
                asset_inputs,
                asset_outputs,
                i,
                period,
                0,
                state,
                Some(&params[i]),
            ));
        }
    }
    let mut driver = WildersDriver {};
    let wilders = road_train.drive(&mut driver);

    let mut states = Vec::with_capacity(N);
    for (&wilder, multipliers) in wilders.iter().zip(params.into_iter()) {
        states.push(IndicatorState::new(wilder, multipliers));
    }
    Ok((output_buffers, states))
}