semantic-ds-toolkit/dist/src/operators/grain-adjuster.d.ts

Performance-first semantic layer for modern data stacks - Stable Column Anchors & intelligent inference

export type TimeGrain = 'millisecond' | 'second' | 'minute' | 'hour' | 'day' | 'week' | 'month' | 'quarter' | 'year' | 'auto';
export type AdjustmentStrategy = 'floor' | 'ceil' | 'round' | 'nearest';
export interface GrainAdjustmentResult {
    originalTimestamps: Date[];
    adjustedTimestamps: Date[];
    sourceGrain: TimeGrain;
    targetGrain: TimeGrain;
    strategy: AdjustmentStrategy;
    gapsFound: number;
    statisticsPreserved: boolean;
}
export interface GrainStatistics {
    mostCommonInterval: number;
    intervalVariance: number;
    detectedGrain: TimeGrain;
    confidence: number;
    regularityScore: number;
}
export declare class GrainAdjuster {
    private grainMilliseconds;
    detectGrain(timestamps: Date[]): TimeGrain;
    private intervalToGrain;
    adjustGrain(timestamps: Date[], sourceGrain: TimeGrain, targetGrain: TimeGrain, strategy?: AdjustmentStrategy): Promise<GrainAdjustmentResult>;
    private adjustSingleTimestamp;
    private alignToNearestCalendarBoundary;
    private countGaps;
    private checkStatisticsPreservation;
    getGrainStatistics(timestamps: Date[]): GrainStatistics;
    private getMostCommonInterval;
    private calculateVariance;
    private calculateConfidence;
    private calculateRegularityScore;
    createTimeGrid(startTime: Date, endTime: Date, grain: TimeGrain): Date[];
    getGrainMilliseconds(grain: TimeGrain): number;
    isValidGrain(grain: string): grain is TimeGrain;
    getAvailableGrains(): TimeGrain[];
    compareGrains(grain1: TimeGrain, grain2: TimeGrain): number;
    optimizeGrainForDataset(timestamps: Date[], targetDataPoints?: number): Promise<{
        recommendedGrain: TimeGrain;
        reasoning: string;
    }>;
    private getCoarserGrain;
}
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