// Time Series API Implementation (InfluxDB-like operations)
import { CBDAdapter } from '../cbd-adapter.js';
import { TimeSeriesQuery, TimeSeriesPoint, CNDError } from '../types.js';

export class TimeSeriesAPI implements TimeSeriesQuery {
  private rangeStart?: Date;
  private rangeEnd?: Date;
  private tagFilters: Record<string, string> = {};

  constructor(
    private metric: string,
    private adapter: CBDAdapter
  ) { }

  async insert(
    timestamp: Date,
    value: number,
    tags?: Record<string, string>
  ): Promise<void> {
    try {
      await this.adapter.storeTimeSeries(this.metric, timestamp, value, tags || {});
    } catch (error) {
      throw new CNDError(
        `Time series insert failed: ${error}`,
        'TIMESERIES_INSERT_ERROR',
        { metric: this.metric, timestamp, value, tags }
      );
    }
  }

  range(start: Date, end: Date): TimeSeriesAPI {
    this.rangeStart = start;
    this.rangeEnd = end;
    return this;
  }

  where(tags: Record<string, string>): TimeSeriesAPI {
    this.tagFilters = { ...this.tagFilters, ...tags };
    return this;
  }

  async aggregate(
    func: 'avg' | 'sum' | 'min' | 'max' | 'count',
    interval?: string
  ): Promise<TimeSeriesPoint[]> {
    try {
      if (!this.rangeStart || !this.rangeEnd) {
        throw new CNDError(
          'Time range is required for aggregation',
          'TIMESERIES_RANGE_REQUIRED',
          { func, interval }
        );
      }

      const points = await this.adapter.queryTimeSeries(
        this.metric,
        this.rangeStart,
        this.rangeEnd,
        this.tagFilters
      );

      return this.performAggregation(points, func, interval);
    } catch (error) {
      throw new CNDError(
        `Time series aggregation failed: ${error}`,
        'TIMESERIES_AGGREGATION_ERROR',
        { metric: this.metric, func, interval, range: [this.rangeStart, this.rangeEnd] }
      );
    }
  }

  async latest(): Promise<TimeSeriesPoint | null> {
    try {
      const endTime = new Date();
      const startTime = new Date(endTime.getTime() - 24 * 60 * 60 * 1000); // Last 24 hours

      const points = await this.adapter.queryTimeSeries(
        this.metric,
        startTime,
        endTime,
        this.tagFilters
      );

      if (points.length === 0) return null;

      return points.reduce((latest, current) =>
        current.timestamp > latest.timestamp ? current : latest
      );
    } catch (error) {
      throw new CNDError(
        `Time series latest failed: ${error}`,
        'TIMESERIES_LATEST_ERROR',
        { metric: this.metric, tags: this.tagFilters }
      );
    }
  }

  // Additional time series operations
  async insertBatch(points: Array<{
    timestamp: Date;
    value: number;
    tags?: Record<string, string>;
  }>): Promise<void> {
    try {
      for (const point of points) {
        await this.insert(point.timestamp, point.value, point.tags);
      }
    } catch (error) {
      throw new CNDError(
        `Time series batch insert failed: ${error}`,
        'TIMESERIES_BATCH_INSERT_ERROR',
        { metric: this.metric, batchSize: points.length }
      );
    }
  }

  async query(options?: {
    start?: Date;
    end?: Date;
    tags?: Record<string, string>;
    limit?: number;
    orderBy?: 'asc' | 'desc';
  }): Promise<TimeSeriesPoint[]> {
    try {
      const start = options?.start || this.rangeStart || new Date(Date.now() - 24 * 60 * 60 * 1000);
      const end = options?.end || this.rangeEnd || new Date();
      const tags = { ...this.tagFilters, ...(options?.tags || {}) };

      let points = await this.adapter.queryTimeSeries(this.metric, start, end, tags);

      // Apply ordering
      if (options?.orderBy === 'desc') {
        points.sort((a, b) => b.timestamp.getTime() - a.timestamp.getTime());
      } else {
        points.sort((a, b) => a.timestamp.getTime() - b.timestamp.getTime());
      }

      // Apply limit
      if (options?.limit) {
        points = points.slice(0, options.limit);
      }

      return points;
    } catch (error) {
      throw new CNDError(
        `Time series query failed: ${error}`,
        'TIMESERIES_QUERY_ERROR',
        { metric: this.metric, options }
      );
    }
  }

  async delete(start: Date, end: Date, tags?: Record<string, string>): Promise<number> {
    try {
      // In real implementation, this would delete points from CBD Engine
      console.log(`Time Series API: Deleting points from ${this.metric}`, {
        start, end, tags
      });
      return 0;
    } catch (error) {
      throw new CNDError(
        `Time series delete failed: ${error}`,
        'TIMESERIES_DELETE_ERROR',
        { metric: this.metric, start, end, tags }
      );
    }
  }

  // Statistical operations
  async stats(options?: {
    start?: Date;
    end?: Date;
    tags?: Record<string, string>;
  }): Promise<{
    count: number;
    min: number;
    max: number;
    avg: number;
    sum: number;
    stdDev: number;
  }> {
    try {
      const points = await this.query(options);

      if (points.length === 0) {
        return { count: 0, min: 0, max: 0, avg: 0, sum: 0, stdDev: 0 };
      }

      const values = points.map(p => p.value);
      const count = values.length;
      const sum = values.reduce((a, b) => a + b, 0);
      const avg = sum / count;
      const min = Math.min(...values);
      const max = Math.max(...values);

      const variance = values.reduce((acc, val) => acc + Math.pow(val - avg, 2), 0) / count;
      const stdDev = Math.sqrt(variance);

      return { count, min, max, avg, sum, stdDev };
    } catch (error) {
      throw new CNDError(
        `Time series stats failed: ${error}`,
        'TIMESERIES_STATS_ERROR',
        { metric: this.metric, options }
      );
    }
  }

  async downsample(
    interval: string,
    aggregationFunc: 'avg' | 'sum' | 'min' | 'max' | 'count' = 'avg'
  ): Promise<TimeSeriesPoint[]> {
    try {
      if (!this.rangeStart || !this.rangeEnd) {
        throw new CNDError(
          'Time range is required for downsampling',
          'TIMESERIES_RANGE_REQUIRED',
          { interval, aggregationFunc }
        );
      }

      return this.aggregate(aggregationFunc, interval);
    } catch (error) {
      throw new CNDError(
        `Time series downsampling failed: ${error}`,
        'TIMESERIES_DOWNSAMPLE_ERROR',
        { metric: this.metric, interval, aggregationFunc }
      );
    }
  }

  async interpolate(
    method: 'linear' | 'cubic' | 'nearest' = 'linear',
    interval: string = '1m'
  ): Promise<TimeSeriesPoint[]> {
    try {
      const points = await this.query();

      // Simple linear interpolation implementation
      // In real implementation, this would use more sophisticated algorithms
      return this.performInterpolation(points, method, interval);
    } catch (error) {
      throw new CNDError(
        `Time series interpolation failed: ${error}`,
        'TIMESERIES_INTERPOLATION_ERROR',
        { metric: this.metric, method, interval }
      );
    }
  }

  // Forecasting and anomaly detection
  async forecast(
    periodsAhead: number,
    model: 'linear' | 'seasonal' | 'arima' = 'linear'
  ): Promise<TimeSeriesPoint[]> {
    try {
      // In real implementation, this would use forecasting algorithms in CBD Engine
      console.log(`Time Series API: Forecasting ${this.metric}`, {
        periodsAhead, model
      });
      return [];
    } catch (error) {
      throw new CNDError(
        `Time series forecasting failed: ${error}`,
        'TIMESERIES_FORECAST_ERROR',
        { metric: this.metric, periodsAhead, model }
      );
    }
  }

  async detectAnomalies(
    algorithm: 'zscore' | 'iqr' | 'isolation_forest' = 'zscore',
    threshold: number = 2
  ): Promise<Array<{ point: TimeSeriesPoint; anomalyScore: number }>> {
    try {
      const points = await this.query();

      // Simple z-score anomaly detection
      if (algorithm === 'zscore') {
        const stats = await this.stats();
        return points
          .map(point => ({
            point,
            anomalyScore: Math.abs((point.value - stats.avg) / stats.stdDev)
          }))
          .filter(result => result.anomalyScore > threshold);
      }

      return [];
    } catch (error) {
      throw new CNDError(
        `Time series anomaly detection failed: ${error}`,
        'TIMESERIES_ANOMALY_DETECTION_ERROR',
        { metric: this.metric, algorithm, threshold }
      );
    }
  }

  // Helper methods
  private performAggregation(
    points: TimeSeriesPoint[],
    func: 'avg' | 'sum' | 'min' | 'max' | 'count',
    interval?: string
  ): TimeSeriesPoint[] {
    if (!interval) {
      // Aggregate all points into one
      const value = this.calculateAggregateValue(points, func);
      return [{
        timestamp: points[0]?.timestamp || new Date(),
        value,
        tags: {}
      }];
    }

    // Group points by interval and aggregate each group
    const groups = this.groupByInterval(points, interval);
    return Object.entries(groups).map(([timestamp, groupPoints]) => ({
      timestamp: new Date(timestamp),
      value: this.calculateAggregateValue(groupPoints, func),
      tags: {}
    }));
  }

  private calculateAggregateValue(
    points: TimeSeriesPoint[],
    func: 'avg' | 'sum' | 'min' | 'max' | 'count'
  ): number {
    if (points.length === 0) return 0;

    const values = points.map(p => p.value);

    switch (func) {
      case 'avg':
        return values.reduce((a, b) => a + b, 0) / values.length;
      case 'sum':
        return values.reduce((a, b) => a + b, 0);
      case 'min':
        return Math.min(...values);
      case 'max':
        return Math.max(...values);
      case 'count':
        return values.length;
      default:
        return 0;
    }
  }

  private groupByInterval(
    points: TimeSeriesPoint[],
    interval: string
  ): Record<string, TimeSeriesPoint[]> {
    const groups: Record<string, TimeSeriesPoint[]> = {};
    const intervalMs = this.parseInterval(interval);

    for (const point of points) {
      const bucketTime = Math.floor(point.timestamp.getTime() / intervalMs) * intervalMs;
      const bucketKey = new Date(bucketTime).toISOString();

      if (!groups[bucketKey]) {
        groups[bucketKey] = [];
      }
      groups[bucketKey].push(point);
    }

    return groups;
  }

  private parseInterval(interval: string): number {
    const match = interval.match(/^(\d+)([smhd])$/);
    if (!match) return 60000; // Default to 1 minute

    const value = parseInt(match[1]);
    const unit = match[2];

    switch (unit) {
      case 's': return value * 1000;
      case 'm': return value * 60 * 1000;
      case 'h': return value * 60 * 60 * 1000;
      case 'd': return value * 24 * 60 * 60 * 1000;
      default: return 60000;
    }
  }

  private performInterpolation(
    points: TimeSeriesPoint[],
    method: 'linear' | 'cubic' | 'nearest',
    interval: string
  ): TimeSeriesPoint[] {
    // Simple linear interpolation implementation
    // In a real implementation, this would be more sophisticated
    console.log(`Time Series API: Interpolating ${this.metric}`, {
      pointCount: points.length, method, interval
    });
    return points;
  }
}