/**
 * Temporal Attractor Studio Tools
 * High-performance chaos analysis and Lyapunov exponent calculation
 * Integrated with sublinear solver for temporal dynamics
 */
// Tool definitions for temporal attractor analysis
export const temporalAttractorTools = [
    {
        name: 'chaos_analyze',
        description: 'Calculate Lyapunov exponent and chaos metrics from time series data using WASM-optimized algorithms',
        inputSchema: {
            type: 'object',
            properties: {
                data: {
                    type: 'array',
                    description: 'Time series data (flattened array)',
                    items: { type: 'number' }
                },
                dimensions: {
                    type: 'integer',
                    description: 'Number of dimensions per time point',
                    minimum: 1,
                    default: 3
                },
                dt: {
                    type: 'number',
                    description: 'Time step between measurements',
                    default: 0.01
                },
                k_fit: {
                    type: 'integer',
                    description: 'Points for linear fitting (Rosenstein algorithm)',
                    default: 12
                },
                theiler: {
                    type: 'integer',
                    description: 'Theiler window to exclude temporal neighbors',
                    default: 20
                },
                max_pairs: {
                    type: 'integer',
                    description: 'Maximum trajectory pairs to analyze',
                    default: 1000
                }
            },
            required: ['data']
        }
    },
    {
        name: 'temporal_delay_embed',
        description: 'Perform delay embedding for phase space reconstruction (Takens theorem)',
        inputSchema: {
            type: 'object',
            properties: {
                series: {
                    type: 'array',
                    description: 'Univariate time series',
                    items: { type: 'number' }
                },
                embedding_dim: {
                    type: 'integer',
                    description: 'Embedding dimension (typically 3-5)',
                    minimum: 2,
                    maximum: 10,
                    default: 3
                },
                tau: {
                    type: 'integer',
                    description: 'Time delay (typically 1-10)',
                    minimum: 1,
                    default: 1
                }
            },
            required: ['series']
        }
    },
    {
        name: 'temporal_predict',
        description: 'Initialize and use Echo-State Network for temporal prediction',
        inputSchema: {
            type: 'object',
            properties: {
                action: {
                    type: 'string',
                    description: 'Action to perform',
                    enum: ['init', 'train', 'predict', 'trajectory']
                },
                // For initialization
                reservoir_size: {
                    type: 'integer',
                    description: 'Number of reservoir nodes (100-1000 typical)',
                    default: 300
                },
                input_dim: {
                    type: 'integer',
                    description: 'Input dimension',
                    minimum: 1,
                    default: 3
                },
                output_dim: {
                    type: 'integer',
                    description: 'Output dimension',
                    minimum: 1,
                    default: 3
                },
                spectral_radius: {
                    type: 'number',
                    description: 'Spectral radius (< 1 for stability)',
                    default: 0.95
                },
                // For training
                inputs: {
                    type: 'array',
                    description: 'Training input data (flattened)',
                    items: { type: 'number' }
                },
                targets: {
                    type: 'array',
                    description: 'Training target data (flattened)',
                    items: { type: 'number' }
                },
                n_samples: {
                    type: 'integer',
                    description: 'Number of training samples',
                    minimum: 1
                },
                // For prediction
                input: {
                    type: 'array',
                    description: 'Current state vector for prediction',
                    items: { type: 'number' }
                },
                n_steps: {
                    type: 'integer',
                    description: 'Number of steps to predict (for trajectory)',
                    default: 1
                }
            },
            required: ['action']
        }
    },
    {
        name: 'temporal_fractal_dimension',
        description: 'Estimate fractal dimension of attractor using box-counting algorithm',
        inputSchema: {
            type: 'object',
            properties: {
                data: {
                    type: 'array',
                    description: 'Time series data (flattened)',
                    items: { type: 'number' }
                },
                dimensions: {
                    type: 'integer',
                    description: 'Number of dimensions per point',
                    minimum: 1,
                    default: 3
                }
            },
            required: ['data']
        }
    },
    {
        name: 'temporal_regime_changes',
        description: 'Detect regime changes in chaotic dynamics using sliding window analysis',
        inputSchema: {
            type: 'object',
            properties: {
                data: {
                    type: 'array',
                    description: 'Time series data (flattened)',
                    items: { type: 'number' }
                },
                dimensions: {
                    type: 'integer',
                    description: 'Dimensions per point',
                    minimum: 1,
                    default: 3
                },
                window_size: {
                    type: 'integer',
                    description: 'Size of analysis window',
                    default: 50
                },
                stride: {
                    type: 'integer',
                    description: 'Stride between windows',
                    default: 10
                }
            },
            required: ['data']
        }
    },
    {
        name: 'temporal_generate_attractor',
        description: 'Generate known chaotic attractor data for testing',
        inputSchema: {
            type: 'object',
            properties: {
                system: {
                    type: 'string',
                    description: 'Attractor system to generate',
                    enum: ['lorenz', 'henon', 'rossler', 'chua', 'logistic']
                },
                n_points: {
                    type: 'integer',
                    description: 'Number of points to generate',
                    default: 1000
                },
                dt: {
                    type: 'number',
                    description: 'Time step (for continuous systems)',
                    default: 0.01
                },
                parameters: {
                    type: 'object',
                    description: 'System-specific parameters (e.g., sigma, rho, beta for Lorenz)',
                    additionalProperties: { type: 'number' }
                }
            },
            required: ['system']
        }
    },
    {
        name: 'temporal_interpret_chaos',
        description: 'Get human-readable interpretation of Lyapunov exponent and chaos metrics',
        inputSchema: {
            type: 'object',
            properties: {
                lambda: {
                    type: 'number',
                    description: 'Lyapunov exponent value'
                },
                dimension: {
                    type: 'number',
                    description: 'Fractal dimension (optional)'
                },
                system_name: {
                    type: 'string',
                    description: 'Name of the system being analyzed (optional)'
                }
            },
            required: ['lambda']
        }
    },
    {
        name: 'temporal_recommend_parameters',
        description: 'Get recommended analysis parameters based on data characteristics',
        inputSchema: {
            type: 'object',
            properties: {
                n_points: {
                    type: 'integer',
                    description: 'Number of data points',
                    minimum: 1
                },
                n_dims: {
                    type: 'integer',
                    description: 'Number of dimensions',
                    minimum: 1,
                    default: 3
                },
                sampling_rate: {
                    type: 'number',
                    description: 'Sampling rate in Hz',
                    default: 100
                },
                signal_type: {
                    type: 'string',
                    description: 'Type of signal',
                    enum: ['continuous', 'discrete', 'mixed'],
                    default: 'continuous'
                }
            },
            required: ['n_points']
        }
    },
    {
        name: 'temporal_attractor_pullback',
        description: 'Calculate pullback attractor dynamics and evolution',
        inputSchema: {
            type: 'object',
            properties: {
                initial_conditions: {
                    type: 'array',
                    description: 'Initial state vectors for ensemble',
                    items: {
                        type: 'array',
                        items: { type: 'number' }
                    }
                },
                ensemble_size: {
                    type: 'integer',
                    description: 'Number of trajectories in ensemble',
                    default: 100
                },
                evolution_time: {
                    type: 'number',
                    description: 'Total evolution time',
                    default: 10.0
                },
                snapshot_interval: {
                    type: 'number',
                    description: 'Time between snapshots',
                    default: 0.1
                }
            },
            required: ['initial_conditions']
        }
    },
    {
        name: 'temporal_kaplan_yorke_dimension',
        description: 'Calculate Kaplan-Yorke dimension from Lyapunov spectrum',
        inputSchema: {
            type: 'object',
            properties: {
                lyapunov_spectrum: {
                    type: 'array',
                    description: 'Array of Lyapunov exponents (sorted descending)',
                    items: { type: 'number' }
                },
                data: {
                    type: 'array',
                    description: 'Alternative: calculate spectrum from data',
                    items: { type: 'number' }
                },
                dimensions: {
                    type: 'integer',
                    description: 'Dimensions for data (if provided)',
                    default: 3
                }
            },
            required: []
        }
    }
];
// Export handler functions that will call the WASM implementation
export { temporalAttractorHandlers } from './temporal-attractor-handlers.js';