ballistics_engine/

monte_carlo.rs

//! Monte Carlo simulation support with statistical analysis
//!
//! This module provides core statistical functions for Monte Carlo trajectory analysis,
//! including CEP (Circular Error Probable), confidence ellipses, and trajectory evaluation.
//!
//! MBA-157: Upstreamed from ballistics_rust for shared use across the ecosystem

use crate::atmosphere::calculate_atmosphere;
use crate::fast_trajectory::{fast_integrate, FastIntegrationParams};
use crate::wind::WindSock;
use crate::BallisticInputs;
use nalgebra::Vector3;

/// Simple trajectory output for Monte Carlo analysis
#[derive(Debug, Clone)]
pub struct TrajectoryOutput {
    pub drop: f64,       // meters
    pub wind_drift: f64, // meters
    pub time: f64,       // seconds
    pub velocity: f64,   // m/s
    pub energy: f64,     // joules
    pub mach: f64,       // mach number
    pub spin_drift: f64, // meters
    pub distance: f64,   // meters
}

/// Solve trajectory for Monte Carlo run
///
/// This function evaluates a single trajectory with the given inputs and returns
/// simplified output suitable for statistical analysis.
pub fn solve_trajectory_for_monte_carlo(
    inputs: &BallisticInputs,
) -> Result<TrajectoryOutput, String> {
    // BallisticInputs is SI-canonical (meters, m/s, kg, radians).
    let target_distance_m = inputs.target_distance; // meters
    let muzzle_velocity_mps = inputs.muzzle_velocity; // m/s
    let mass_kg = inputs.bullet_mass; // kg

    // Guard a non-finite or non-positive target distance: los_y (and the wind segment /
    // params.horiz) divide by or scale with target_distance_m, so 0/NaN/negative/+inf would
    // yield a silently-NaN-or-inf result that poisons mean/stddev/CEP aggregation. The
    // is_finite() check also rejects +inf, which `> 0.0` alone lets through. Engine default
    // is 100 m.
    if !(target_distance_m.is_finite() && target_distance_m > 0.0) {
        return Err("target_distance must be a positive, finite distance".to_string());
    }

    // Calculate atmosphere at altitude. resolve_station_pressure / _temperature keep explicit
    // values authoritative (no altitude double-count) but return None when left at the sea-level
    // default, so altitude drives BOTH the base station pressure AND the lapse-rate temperature
    // via the ICAO standard (this base_ratio feeds the fast/Monte-Carlo kernel's base_density).
    // Matches calculate_air_density.
    let (resolved_temp_c, resolved_pressure_hpa) = crate::atmosphere::resolve_station_conditions(
        inputs.temperature,
        inputs.pressure,
        inputs.altitude,
    );
    let (air_density, speed_of_sound) = calculate_atmosphere(
        inputs.altitude, // meters
        Some(resolved_temp_c),
        Some(resolved_pressure_hpa),
        // BallisticInputs.humidity is a 0-1 fraction; calculate_atmosphere expects 0-100 percent
        // (matching AtmosphericConditions.humidity). Passing the raw fraction under-applied
        // humidity 100x. Centralized in humidity_percent() (MBA-722).
        inputs.humidity_percent(),
    );

    // Create wind segments. WindSock expects (speed_kmh, angle_deg, until_distance_m);
    // convert from the SI fields (m/s, radians) at this boundary.
    let wind_segments = vec![(
        inputs.wind_speed * 3.6,        // m/s -> km/h
        inputs.wind_angle.to_degrees(), // radians -> degrees
        target_distance_m * 2.0,        // wind extends beyond target
    )];
    let wind_sock = WindSock::new(wind_segments);

    // Set up initial state
    let muzzle_angle_rad = inputs.muzzle_angle;
    // McCoy: X=downrange, Y=vertical, Z=lateral
    let initial_velocity = Vector3::new(
        muzzle_velocity_mps * muzzle_angle_rad.cos(),
        muzzle_velocity_mps * muzzle_angle_rad.sin(),
        0.0,
    );

    let initial_position = Vector3::new(0.0, inputs.sight_height, 0.0); // meters
    let mut initial_state_array = [0.0; 6];
    initial_state_array[0..3].copy_from_slice(&[
        initial_position.x,
        initial_position.y,
        initial_position.z,
    ]);
    initial_state_array[3..6].copy_from_slice(&[
        initial_velocity.x,
        initial_velocity.y,
        initial_velocity.z,
    ]);

    // Create integration params. fast_integrate's atmo_params is
    // (base_alt_m, base_temp_c, base_press_hpa, base_ratio) — NOT
    // (temp, pressure, density, sound). Packing it wrong scrambled the base
    // density (~417 kg/m^3) and produced ~340x drag. base_ratio is the
    // density relative to 1.225 (get_local_atmosphere returns base_ratio*1.225
    // at the base altitude), so derive it from the computed air density.
    let base_ratio = air_density / 1.225;
    let params = FastIntegrationParams {
        initial_state: initial_state_array,
        t_span: (0.0, 30.0),
        horiz: target_distance_m,
        vert: 0.0, // Target at ground level
        atmo_params: (
            inputs.altitude,
            resolved_temp_c,
            resolved_pressure_hpa,
            base_ratio,
        ),
    };

    // Solve trajectory
    let solution = fast_integrate(inputs, &wind_sock, params);

    if solution.t.is_empty() {
        return Err("Empty trajectory solution".to_string());
    }

    // Get final state
    // FastSolution.y is Vec<Vec<f64>> where y[i] is the ith state variable across all time points
    let final_idx = solution.t.len() - 1;

    let final_downrange = solution.y[0][final_idx]; // McCoy: X=downrange

    // Exclude runs that did not reach the target distance (a short/steep/subsonic shot, or any
    // residual time-budget truncation) instead of silently reporting their too-short impact
    // metrics at the target downrange, which would poison the mean / stddev / CEP aggregation.
    if final_downrange < target_distance_m * 0.999 {
        return Err("trajectory did not reach target distance".to_string());
    }

    let final_y = solution.y[1][final_idx]; // vertical
    let final_lateral = solution.y[2][final_idx]; // McCoy: Z=lateral drift

    let final_vx = solution.y[3][final_idx];
    let final_vy = solution.y[4][final_idx];
    let final_vz = solution.y[5][final_idx];

    let final_speed = (final_vx * final_vx + final_vy * final_vy + final_vz * final_vz).sqrt();
    let final_mach = final_speed / speed_of_sound;
    let final_energy = 0.5 * mass_kg * final_speed * final_speed;

    // Calculate line-of-sight drop
    let sight_height_m = inputs.sight_height; // meters
    let los_y = sight_height_m + (0.0 - sight_height_m) * (final_downrange / target_distance_m);
    let drop = los_y - final_y;

    // MBA-1134 (rank 6/10): the canonical empirical Litz spin drift is now applied INSIDE
    // fast_integrate itself (post-process on the state lateral), so `final_lateral` already
    // includes it — matching cli_api::apply_spin_drift and fast_integrate_with_segments. We
    // therefore must NOT add it a second time here (doing so double-counted the drift, ~2x).
    // We still recompute the Litz component with the SAME muzzle Sg for the reported
    // `spin_drift` field (the old `// Approximation for now` had copied total lateral).
    let spin_drift_m = if inputs.use_enhanced_spin_drift {
        let sg = crate::spin_drift::effective_sg_from_inputs(
            inputs,
            resolved_temp_c,
            resolved_pressure_hpa,
        );
        crate::spin_drift::litz_drift_meters(sg, solution.t[final_idx], inputs.is_twist_right)
    } else {
        0.0
    };

    Ok(TrajectoryOutput {
        drop,
        // final_lateral already carries the Litz spin drift (applied in fast_integrate).
        wind_drift: final_lateral,
        time: solution.t[final_idx],
        velocity: final_speed,
        energy: final_energy,
        mach: final_mach,
        spin_drift: spin_drift_m,
        distance: final_downrange,
    })
}

/// Calculate CEP (Circular Error Probable) from impact points
///
/// CEP is the radius of a circle centered at the mean point of impact,
/// within which 50% of the shots fall. It's a standard measure of precision.
pub fn calculate_cep(wind_drift_values: &[f64], drop_values: &[f64]) -> f64 {
    if wind_drift_values.len() != drop_values.len() || wind_drift_values.is_empty() {
        return 0.0;
    }

    // Calculate mean point of impact
    let mean_x = wind_drift_values.iter().sum::<f64>() / wind_drift_values.len() as f64;
    let mean_y = drop_values.iter().sum::<f64>() / drop_values.len() as f64;

    // Calculate distance from each point to mean
    let mut distances: Vec<f64> = wind_drift_values
        .iter()
        .zip(drop_values.iter())
        .map(|(x, y)| {
            let dx = x - mean_x;
            let dy = y - mean_y;
            (dx * dx + dy * dy).sqrt()
        })
        .collect();

    // Sort distances to find median (50th percentile)
    distances.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));

    // CEP is the median distance from center
    percentile(&distances, 0.50)
}

/// Calculate 95% confidence ellipse parameters using covariance matrix
///
/// Returns (center_x, center_y, semi_major_axis, semi_minor_axis, rotation_degrees)
pub fn calculate_confidence_ellipse(
    wind_drift_values: &[f64],
    drop_values: &[f64],
) -> (f64, f64, f64, f64, f64) {
    if wind_drift_values.len() != drop_values.len() || wind_drift_values.len() < 2 {
        return (0.0, 0.0, 0.0, 0.0, 0.0);
    }

    let n = wind_drift_values.len() as f64;

    // Calculate means
    let mean_x = wind_drift_values.iter().sum::<f64>() / n;
    let mean_y = drop_values.iter().sum::<f64>() / n;

    // Calculate covariance matrix elements
    let mut cov_xx = 0.0;
    let mut cov_yy = 0.0;
    let mut cov_xy = 0.0;

    for (x, y) in wind_drift_values.iter().zip(drop_values.iter()) {
        let dx = x - mean_x;
        let dy = y - mean_y;
        cov_xx += dx * dx;
        cov_yy += dy * dy;
        cov_xy += dx * dy;
    }

    cov_xx /= n - 1.0;
    cov_yy /= n - 1.0;
    cov_xy /= n - 1.0;

    // Calculate eigenvalues of covariance matrix
    // For 2x2 matrix: [[cov_xx, cov_xy], [cov_xy, cov_yy]]
    let trace = cov_xx + cov_yy;
    let det = cov_xx * cov_yy - cov_xy * cov_xy;
    let discriminant = (trace * trace / 4.0 - det).max(0.0).sqrt();

    let lambda1 = trace / 2.0 + discriminant; // Larger eigenvalue
    let lambda2 = trace / 2.0 - discriminant; // Smaller eigenvalue

    // 95% confidence interval chi-square value for 2 DOF is 5.991
    let scale_factor = 5.991_f64.sqrt();
    let semi_major = lambda1.max(0.0).sqrt() * scale_factor;
    let semi_minor = lambda2.max(0.0).sqrt() * scale_factor;

    // Calculate rotation angle (angle of major axis)
    let rotation_rad = if cov_xy.abs() < 1e-10 {
        if cov_xx >= cov_yy {
            0.0
        } else {
            std::f64::consts::PI / 2.0
        }
    } else {
        ((lambda1 - cov_xx) / cov_xy).atan()
    };

    let rotation_deg = rotation_rad.to_degrees();

    (mean_x, mean_y, semi_major, semi_minor, rotation_deg)
}

/// Sample points for visualization (limit to avoid huge payloads)
pub fn sample_points_for_visualization(
    wind_drift_values: &[f64],
    drop_values: &[f64],
    max_points: usize,
) -> Vec<(f64, f64)> {
    let n = wind_drift_values.len();
    if n == 0 {
        return Vec::new();
    }

    if n <= max_points {
        // Return all points
        wind_drift_values
            .iter()
            .zip(drop_values.iter())
            .map(|(x, y)| (*x, *y))
            .collect()
    } else {
        // Sample evenly spaced points
        let step = n as f64 / max_points as f64;
        (0..max_points)
            .map(|i| {
                let idx = (i as f64 * step) as usize;
                (wind_drift_values[idx], drop_values[idx])
            })
            .collect()
    }
}

/// Calculate percentile from sorted values
pub fn percentile(sorted_values: &[f64], p: f64) -> f64 {
    if sorted_values.is_empty() {
        return 0.0;
    }

    if sorted_values.len() == 1 {
        return sorted_values[0];
    }

    // Clamp p to [0,1]: percentile is public (callable from ballistics_rust / FFI). p > 1 made
    // upper_idx exceed the slice and panic; p < 0 silently returned a wrong value.
    let p = p.clamp(0.0, 1.0);
    let rank = p * (sorted_values.len() - 1) as f64;
    let lower_idx = rank.floor() as usize;
    let upper_idx = rank.ceil() as usize;
    let fraction = rank - lower_idx as f64;

    if lower_idx == upper_idx {
        sorted_values[lower_idx]
    } else {
        sorted_values[lower_idx] * (1.0 - fraction) + sorted_values[upper_idx] * fraction
    }
}

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

    #[test]
    fn test_calculate_cep() {
        let wind_drift = vec![0.0, 1.0, -1.0, 0.5, -0.5];
        let drop = vec![0.0, 0.5, -0.5, 1.0, -1.0];

        let cep = calculate_cep(&wind_drift, &drop);
        assert!(cep > 0.0);
        assert!(cep < 2.0); // Reasonable range
    }

    #[test]
    fn test_calculate_confidence_ellipse() {
        let wind_drift = vec![0.0, 1.0, -1.0, 0.5, -0.5];
        let drop = vec![0.0, 0.5, -0.5, 1.0, -1.0];

        let (cx, cy, major, minor, _rotation) = calculate_confidence_ellipse(&wind_drift, &drop);

        // Center should be near origin
        assert!(cx.abs() < 0.5);
        assert!(cy.abs() < 0.5);

        // Axes should be positive
        assert!(major > 0.0);
        assert!(minor > 0.0);
        assert!(major >= minor); // Major axis should be >= minor axis
    }

    #[test]
    fn test_sample_points() {
        let wind_drift = vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0];
        let drop = vec![0.0, 0.1, 0.2, 0.3, 0.4, 0.5];

        let sampled = sample_points_for_visualization(&wind_drift, &drop, 3);
        assert_eq!(sampled.len(), 3);
    }

    #[test]
    fn test_percentile() {
        let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];

        assert_eq!(percentile(&values, 0.0), 1.0);
        assert_eq!(percentile(&values, 0.5), 3.0);
        assert_eq!(percentile(&values, 1.0), 5.0);
    }
}