bowling-analysis-system/src/bowling_analysis/metrics/calculators/ConsistencyCalculator.js

A comprehensive system for analyzing bowling techniques using video processing and metrics calculation

/**
 * @module bowling_analysis/metrics/calculators/ConsistencyCalculator
 * @description Calculator for consistency-related metrics in Phase Three
 */

/**
 * Calculate consistency-related metrics
 * @param {Object} metrics - Metrics from phases one and two
 * @param {Object} timeSeries - Time series data
 * @param {Object} events - Detected events
 * @param {Object} options - Calculator options
 * @returns {Promise<Object>} Consistency metrics
 */
async function calculate(metrics, timeSeries, events, options = {}) {
  try {
    const { debug, includeTimeSeries } = options;
    
    // Initialize result
    const result = {};
    
    // Initialize time series
    const timeSeriesData = {};
    
    // Define consistency metrics to calculate
    const consistencyMetrics = [
      'approachConsistency',
      'timingConsistency',
      'releaseConsistency',
      'followThroughConsistency',
      'footPlacementConsistency',
      'armSwingConsistency',
      'balanceConsistency',
      'movementRepeatability'
    ];
    
    // TODO: Implement actual consistency metric calculations
    // For this stub, we'll just create placeholder data
    
    // Generate placeholder values for consistency metrics
    for (const metricName of consistencyMetrics) {
      result[metricName] = Math.random(); // Value between 0 and 1
    }
    
    // Calculate overall consistency score (average of all metrics)
    const consistencyValues = Object.values(result);
    result.overallConsistency = consistencyValues.reduce((sum, val) => sum + val, 0) / consistencyValues.length;
    
    // Add time series data if requested
    if (includeTimeSeries) {
      const timeSeriesLength = timeSeries.frameIndex ? timeSeries.frameIndex.length : 0;
      
      // Only generate time series if we have frame data
      if (timeSeriesLength > 0) {
        for (const metricName of consistencyMetrics) {
          const values = Array(timeSeriesLength).fill(null);
          
          // For consistency metrics, we'd typically compare across multiple trials
          // But for this placeholder, we'll just use a constant value for each event phase
          if (events.releaseFrame && events.frontFootFrame && events.backFootFrame) {
            const releaseFrame = events.releaseFrame;
            const frontFootFrame = events.frontFootFrame;
            const backFootFrame = events.backFootFrame;
            
            // Approach phase
            const approachValue = Math.random();
            for (let i = 0; i < backFootFrame; i++) {
              values[i] = approachValue;
            }
            
            // Stride phase
            const strideValue = Math.random();
            for (let i = backFootFrame; i < frontFootFrame; i++) {
              values[i] = strideValue;
            }
            
            // Delivery phase
            const deliveryValue = Math.random();
            for (let i = frontFootFrame; i < releaseFrame; i++) {
              values[i] = deliveryValue;
            }
            
            // Follow-through phase
            const followThroughValue = Math.random();
            for (let i = releaseFrame; i < timeSeriesLength; i++) {
              values[i] = followThroughValue;
            }
          }
          
          timeSeriesData[metricName] = values;
        }
      }
      
      result.timeSeries = timeSeriesData;
    }
    
    return result;
  } catch (error) {
    console.error(`Error calculating consistency metrics: ${error.message}`);
    return {};
  }
}

module.exports = {
  calculate
};