quantrs2-device 0.1.3

//! Spatial correlation and geographical analysis for noise modeling

use std::collections::HashMap;
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::Complex64;
use crate::DeviceResult;
use super::types::*;
use super::config::*;

/// Spatial noise analysis coordinator
#[derive(Debug, Clone)]
pub struct SpatialAnalyzer {
    config: SciRS2NoiseConfig,
}

impl SpatialAnalyzer {
    /// Create new spatial analyzer
    pub fn new(config: SciRS2NoiseConfig) -> Self {
        Self { config }
    }

    /// Perform comprehensive spatial analysis
    pub fn analyze_spatial_correlations(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &HashMap<String, Array2<f64>>,
    ) -> DeviceResult<SpatialNoiseModel> {
        let covariance_structure = self.analyze_spatial_covariance(qubit_positions, noise_data)?;
        let basis_functions = self.compute_spatial_basis_functions(qubit_positions)?;
        let kriging_models = self.build_kriging_models(qubit_positions, noise_data)?;
        let spatial_clusters = self.perform_spatial_clustering(qubit_positions, noise_data)?;
        let anisotropy = self.analyze_anisotropy(qubit_positions, noise_data)?;

        Ok(SpatialNoiseModel {
            covariance_structure,
            basis_functions,
            kriging_models,
            spatial_clusters,
            anisotropy,
        })
    }

    /// Analyze spatial covariance structure
    fn analyze_spatial_covariance(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &HashMap<String, Array2<f64>>,
    ) -> DeviceResult<SpatialCovariance> {
        let num_qubits = qubit_positions.nrows();
        let mut distance_matrix = Array2::zeros((num_qubits, num_qubits));

        // Compute pairwise distances
        for i in 0..num_qubits {
            for j in 0..num_qubits {
                let pos_i = qubit_positions.row(i);
                let pos_j = qubit_positions.row(j);
                let dist = ((pos_i[0] - pos_j[0]).powi(2) + (pos_i[1] - pos_j[1]).powi(2)).sqrt();
                distance_matrix[[i, j]] = dist;
            }
        }

        let mut covariance_functions = HashMap::new();
        let mut range_parameters = HashMap::new();
        let mut nugget_effects = HashMap::new();

        for (noise_type, data) in noise_data {
            let (cov_func, range_param, nugget) = self.fit_covariance_function(&distance_matrix, data)?;
            covariance_functions.insert(noise_type.clone(), cov_func);
            range_parameters.insert(noise_type.clone(), range_param);
            nugget_effects.insert(noise_type.clone(), nugget);
        }

        Ok(SpatialCovariance {
            distance_matrix,
            covariance_functions,
            range_parameters,
            nugget_effects,
            covariance_type: CovarianceType::Exponential,
        })
    }

    /// Fit covariance function to spatial data
    fn fit_covariance_function(
        &self,
        distance_matrix: &Array2<f64>,
        noise_data: &Array2<f64>,
    ) -> DeviceResult<(Array2<f64>, f64, f64)> {
        let num_qubits = distance_matrix.nrows();
        let num_samples = noise_data.ncols();

        // Compute empirical covariance matrix
        let mut empirical_cov = Array2::zeros((num_qubits, num_qubits));
        for i in 0..num_qubits {
            for j in 0..num_qubits {
                let mut cov_sum = 0.0;
                for t in 0..num_samples {
                    cov_sum += noise_data[[i, t]] * noise_data[[j, t]];
                }
                empirical_cov[[i, j]] = cov_sum / num_samples as f64;
            }
        }

        // Fit exponential covariance model: C(h) = σ² * exp(-h/θ) + δ
        let range_param = self.estimate_range_parameter(distance_matrix, &empirical_cov)?;
        let nugget_effect = self.estimate_nugget_effect(&empirical_cov)?;

        // Generate fitted covariance matrix
        let mut fitted_cov = Array2::zeros((num_qubits, num_qubits));
        for i in 0..num_qubits {
            for j in 0..num_qubits {
                let dist = distance_matrix[[i, j]];
                if i == j {
                    fitted_cov[[i, j]] = empirical_cov[[i, i]] + nugget_effect;
                } else {
                    fitted_cov[[i, j]] = empirical_cov[[i, i]] * (-dist / range_param).exp();
                }
            }
        }

        Ok((fitted_cov, range_param, nugget_effect))
    }

    /// Estimate range parameter for spatial correlation
    fn estimate_range_parameter(
        &self,
        distance_matrix: &Array2<f64>,
        covariancematrix: &Array2<f64>,
    ) -> DeviceResult<f64> {
        // Use method of moments or maximum likelihood estimation
        // For simplicity, use empirical estimate
        let mut sum_dist_cov = 0.0;
        let mut sum_cov = 0.0;
        let mut count = 0;

        let num_qubits = distance_matrix.nrows();
        for i in 0..num_qubits {
            for j in i+1..num_qubits {
                let dist = distance_matrix[[i, j]];
                let cov = covariancematrix[[i, j]];
                if cov > 0.0 && dist > 0.0 {
                    sum_dist_cov += dist * cov.ln();
                    sum_cov += cov;
                    count += 1;
                }
            }
        }

        if count > 0 {
            Ok(-sum_dist_cov / sum_cov)
        } else {
            Ok(1.0) // Default value
        }
    }

    /// Estimate nugget effect (measurement error)
    fn estimate_nugget_effect(&self, covariancematrix: &Array2<f64>) -> DeviceResult<f64> {
        // Estimate from diagonal vs off-diagonal elements
        let num_qubits = covariancematrix.nrows();
        let mut diag_mean = 0.0;
        let mut off_diag_mean = 0.0;
        let mut off_diag_count = 0;

        for i in 0..num_qubits {
            diag_mean += covariancematrix[[i, i]];
            for j in 0..num_qubits {
                if i != j {
                    off_diag_mean += covariancematrix[[i, j]];
                    off_diag_count += 1;
                }
            }
        }

        diag_mean /= num_qubits as f64;
        if off_diag_count > 0 {
            off_diag_mean /= off_diag_count as f64;
        }

        Ok((diag_mean - off_diag_mean).max(0.0))
    }

    /// Compute spatial basis functions
    fn compute_spatial_basis_functions(
        &self,
        qubit_positions: &Array2<f64>,
    ) -> DeviceResult<SpatialBasisFunctions> {
        let num_qubits = qubit_positions.nrows();

        // Compute radial basis functions
        let radial_functions = self.compute_radial_basis_functions(qubit_positions)?;

        // Compute polynomial basis functions
        let polynomial_functions = self.compute_polynomial_basis_functions(qubit_positions)?;

        // Compute wavelet basis functions (simplified)
        let wavelet_functions = self.compute_wavelet_basis_functions(qubit_positions)?;

        Ok(SpatialBasisFunctions {
            radial_functions,
            polynomial_functions,
            wavelet_functions,
            basis_type: BasisType::Mixed,
        })
    }

    /// Compute radial basis functions
    fn compute_radial_basis_functions(
        &self,
        qubit_positions: &Array2<f64>,
    ) -> DeviceResult<Array2<f64>> {
        let num_qubits = qubit_positions.nrows();
        let mut rbf_matrix = Array2::zeros((num_qubits, num_qubits));

        // Use Gaussian RBF with adaptive bandwidth
        let bandwidth = self.estimate_rbf_bandwidth(qubit_positions)?;

        for i in 0..num_qubits {
            for j in 0..num_qubits {
                let pos_i = qubit_positions.row(i);
                let pos_j = qubit_positions.row(j);
                let dist_sq = (pos_i[0] - pos_j[0]).powi(2) + (pos_i[1] - pos_j[1]).powi(2);
                rbf_matrix[[i, j]] = (-dist_sq / (2.0 * bandwidth.powi(2))).exp();
            }
        }

        Ok(rbf_matrix)
    }

    /// Estimate RBF bandwidth
    fn estimate_rbf_bandwidth(&self, qubit_positions: &Array2<f64>) -> DeviceResult<f64> {
        let num_qubits = qubit_positions.nrows();
        let mut distances = Vec::new();

        for i in 0..num_qubits {
            for j in i+1..num_qubits {
                let pos_i = qubit_positions.row(i);
                let pos_j = qubit_positions.row(j);
                let dist = ((pos_i[0] - pos_j[0]).powi(2) + (pos_i[1] - pos_j[1]).powi(2)).sqrt();
                distances.push(dist);
            }
        }

        distances.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
        let median_distance = if distances.is_empty() {
            1.0
        } else {
            distances[distances.len() / 2]
        };

        Ok(median_distance / 2.0)
    }

    /// Compute polynomial basis functions
    fn compute_polynomial_basis_functions(
        &self,
        qubit_positions: &Array2<f64>,
    ) -> DeviceResult<Array2<f64>> {
        let num_qubits = qubit_positions.nrows();
        let poly_degree = 2; // Quadratic polynomials
        let num_terms = (poly_degree + 1) * (poly_degree + 2) / 2; // Number of terms in 2D polynomial

        let mut poly_matrix = Array2::zeros((num_qubits, num_terms));

        for i in 0..num_qubits {
            let x = qubit_positions[[i, 0]];
            let y = qubit_positions[[i, 1]];

            let mut term_idx = 0;
            for px in 0..=poly_degree {
                for py in 0..=(poly_degree - px) {
                    poly_matrix[[i, term_idx]] = x.powi(px as i32) * y.powi(py as i32);
                    term_idx += 1;
                }
            }
        }

        Ok(poly_matrix)
    }

    /// Compute wavelet basis functions (simplified implementation)
    fn compute_wavelet_basis_functions(
        &self,
        qubit_positions: &Array2<f64>,
    ) -> DeviceResult<Array2<f64>> {
        let num_qubits = qubit_positions.nrows();
        let num_scales = 3;
        let num_wavelets = num_qubits * num_scales;

        let mut wavelet_matrix = Array2::zeros((num_qubits, num_wavelets));

        // Simple Mexican hat wavelets at different scales
        for scale in 0..num_scales {
            let scale_factor = 2.0_f64.powi(scale as i32);

            for center in 0..num_qubits {
                let center_pos = qubit_positions.row(center);
                let wavelet_idx = scale * num_qubits + center;

                for i in 0..num_qubits {
                    let pos = qubit_positions.row(i);
                    let dx = (pos[0] - center_pos[0]) / scale_factor;
                    let dy = (pos[1] - center_pos[1]) / scale_factor;
                    let r_sq = dx * dx + dy * dy;

                    // Mexican hat wavelet
                    let wavelet_val = (2.0 - r_sq) * (-r_sq / 2.0).exp() / scale_factor;
                    wavelet_matrix[[i, wavelet_idx]] = wavelet_val;
                }
            }
        }

        Ok(wavelet_matrix)
    }

    /// Build kriging models for spatial interpolation
    fn build_kriging_models(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &HashMap<String, Array2<f64>>,
    ) -> DeviceResult<HashMap<String, KrigingModel>> {
        let mut kriging_models = HashMap::new();

        for (noise_type, data) in noise_data {
            let model = self.build_single_kriging_model(qubit_positions, data)?;
            kriging_models.insert(noise_type.clone(), model);
        }

        Ok(kriging_models)
    }

    /// Build single kriging model
    fn build_single_kriging_model(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &Array2<f64>,
    ) -> DeviceResult<KrigingModel> {
        let num_qubits = qubit_positions.nrows();
        let num_samples = noise_data.ncols();

        // Use the most recent sample for kriging (could be extended to use all samples)
        let current_values = noise_data.column(num_samples - 1).to_owned();

        // Estimate variogram
        let variogram = self.estimate_variogram(qubit_positions, &current_values)?;

        // Solve kriging system
        let kriging_weights = self.solve_kriging_system(qubit_positions, &variogram)?;

        Ok(KrigingModel {
            qubit_positions: qubit_positions.clone(),
            observed_values: current_values,
            variogram,
            kriging_weights,
            interpolation_method: SpatialInterpolation::Kriging,
        })
    }

    /// Estimate empirical variogram
    fn estimate_variogram(
        &self,
        qubit_positions: &Array2<f64>,
        values: &Array1<f64>,
    ) -> DeviceResult<Variogram> {
        let num_qubits = qubit_positions.nrows();
        let mut distance_variance_pairs = Vec::new();

        for i in 0..num_qubits {
            for j in i+1..num_qubits {
                let pos_i = qubit_positions.row(i);
                let pos_j = qubit_positions.row(j);
                let dist = ((pos_i[0] - pos_j[0]).powi(2) + (pos_i[1] - pos_j[1]).powi(2)).sqrt();
                let variance = 0.5 * (values[i] - values[j]).powi(2);
                distance_variance_pairs.push((dist, variance));
            }
        }

        // Bin the pairs and compute empirical variogram
        distance_variance_pairs.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));

        let num_bins = 10;
        let mut bin_distances = Vec::new();
        let mut bin_variances = Vec::new();

        let max_dist = distance_variance_pairs.last().map(|p| p.0).unwrap_or(1.0);
        let bin_width = max_dist / num_bins as f64;

        for bin in 0..num_bins {
            let bin_start = bin as f64 * bin_width;
            let bin_end = (bin + 1) as f64 * bin_width;

            let bin_pairs: Vec<_> = distance_variance_pairs
                .iter()
                .filter(|(d, _)| *d >= bin_start && *d < bin_end)
                .collect();

            if !bin_pairs.is_empty() {
                let mean_dist = bin_pairs.iter().map(|(d, _)| *d).sum::<f64>() / bin_pairs.len() as f64;
                let mean_var = bin_pairs.iter().map(|(_, v)| *v).sum::<f64>() / bin_pairs.len() as f64;
                bin_distances.push(mean_dist);
                bin_variances.push(mean_var);
            }
        }

        Ok(Variogram {
            distances: Array1::from(bin_distances),
            variances: Array1::from(bin_variances),
            model_type: VariogramModel::Exponential,
            parameters: Array1::from(vec![1.0, 1.0, 0.1]), // [sill, range, nugget]
        })
    }

    /// Solve kriging system for interpolation weights
    fn solve_kriging_system(
        &self,
        qubit_positions: &Array2<f64>,
        variogram: &Variogram,
    ) -> DeviceResult<Array2<f64>> {
        let num_qubits = qubit_positions.nrows();

        // Build covariance matrix from variogram
        let mut cov_matrix = Array2::zeros((num_qubits + 1, num_qubits + 1));

        for i in 0..num_qubits {
            for j in 0..num_qubits {
                if i == j {
                    cov_matrix[[i, j]] = variogram.parameters[0]; // sill
                } else {
                    let pos_i = qubit_positions.row(i);
                    let pos_j = qubit_positions.row(j);
                    let dist = ((pos_i[0] - pos_j[0]).powi(2) + (pos_i[1] - pos_j[1]).powi(2)).sqrt();
                    let covariance = variogram.parameters[0] *
                        (1.0 - (-dist / variogram.parameters[1]).exp()) + variogram.parameters[2];
                    cov_matrix[[i, j]] = covariance;
                }
            }
            // Lagrange multiplier constraints
            cov_matrix[[i, num_qubits]] = 1.0;
            cov_matrix[[num_qubits, i]] = 1.0;
        }
        cov_matrix[[num_qubits, num_qubits]] = 0.0;

        // For now, return identity matrix (placeholder)
        Ok(Array2::eye(num_qubits))
    }

    /// Perform spatial clustering
    fn perform_spatial_clustering(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &HashMap<String, Array2<f64>>,
    ) -> DeviceResult<SpatialClusters> {
        // Simplified spatial clustering implementation
        let num_qubits = qubit_positions.nrows();
        let cluster_labels = Array1::from(vec![0; num_qubits]); // All in one cluster for now

        let mut cluster_statistics = HashMap::new();
        cluster_statistics.insert(0, ClusterStatistics {
            centroid: qubit_positions.mean_axis(scirs2_core::ndarray::Axis(0)).unwrap_or_else(|| Array1::zeros(qubit_positions.ncols())),
            size: num_qubits,
            within_cluster_variance: 1.0,
            separation_distance: 0.0,
        });

        Ok(SpatialClusters {
            cluster_labels,
            num_clusters: 1,
            cluster_statistics,
            clustering_method: ClusteringMethod::KMeans,
        })
    }

    /// Analyze anisotropy in spatial correlations
    fn analyze_anisotropy(
        &self,
        qubit_positions: &Array2<f64>,
        noise_data: &HashMap<String, Array2<f64>>,
    ) -> DeviceResult<AnisotropyAnalysis> {
        // Simplified anisotropy analysis
        let principal_directions = Array1::from(vec![0.0, std::f64::consts::PI / 2.0]);
        let anisotropy_ratios = Array1::from(vec![1.0, 1.0]);

        Ok(AnisotropyAnalysis {
            is_isotropic: true,
            principal_directions,
            anisotropy_ratios,
            confidence_ellipse: ConfidenceEllipse {
                center: Array1::from(vec![0.0, 0.0]),
                major_axis: 1.0,
                minor_axis: 1.0,
                rotation_angle: 0.0,
                confidence_level: self.config.confidence_level,
            },
        })
    }
}