mirror of
https://github.com/acamarata/pray-calc-ml.git
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bbe1bf5cbc
Weighted least-squares calibration of Islamic prayer time depression angles from observed mosque announcement data. Uses golden-section search to minimize the sum of squared residuals independently for Fajr and Isha. Internal Jean Meeus solar ephemeris — zero runtime dependencies. API: calibrateAngles, scoreAngles, predictFajr, predictIsha. Full TypeScript, dual CJS/ESM via tsup. 32 ESM tests, 6 CJS tests, all passing on Node 20/22/24.
73 lines
2.3 KiB
TypeScript
73 lines
2.3 KiB
TypeScript
/**
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* Score an existing set of angles against observations.
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*
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* Use this to evaluate how well a known method (e.g. ISNA's 15°/15°)
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* fits a collection of observed mosque announcements before calibrating.
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*/
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import { predictFajr, predictIsha } from './predict.js';
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import type { Observation } from './types.js';
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export interface ScoreResult {
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/** Weighted RMS error across all observations, in minutes. */
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rmsMinutes: number;
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/** Mean signed error for Fajr in minutes (positive = predicted late). */
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fajrBiasMinutes: number;
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/** Mean signed error for Isha in minutes. */
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ishaBiasMinutes: number;
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/** Per-observation residuals in minutes. */
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residuals: Array<{ fajrMin: number | null; ishaMin: number | null }>;
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}
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/**
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* Evaluate fixed depression angles against observed prayer times.
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*
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* @param observations - Observed Fajr/Isha times.
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* @param fajrAngle - Fajr depression angle in degrees.
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* @param ishaAngle - Isha depression angle in degrees.
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*/
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export function scoreAngles(
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observations: Observation[],
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fajrAngle: number,
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ishaAngle: number,
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): ScoreResult {
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let totalWeightedSS = 0;
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let totalWeight = 0;
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let fajrWeightedBias = 0, fajrWeightCount = 0;
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let ishaWeightedBias = 0, ishaWeightCount = 0;
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const residuals = observations.map(o => {
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const w = o.weight ?? 1;
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let fajrMin: number | null = null;
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let ishaMin: number | null = null;
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if (o.fajr !== undefined) {
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const pred = predictFajr(o.date, o.lat, o.lng, o.tz, fajrAngle);
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if (isFinite(pred)) {
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fajrMin = (pred - o.fajr) * 60;
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totalWeightedSS += w * fajrMin * fajrMin;
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totalWeight += w;
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fajrWeightedBias += w * fajrMin;
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fajrWeightCount += w;
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}
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}
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if (o.isha !== undefined) {
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const pred = predictIsha(o.date, o.lat, o.lng, o.tz, ishaAngle);
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if (isFinite(pred)) {
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ishaMin = (pred - o.isha) * 60;
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totalWeightedSS += w * ishaMin * ishaMin;
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totalWeight += w;
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ishaWeightedBias += w * ishaMin;
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ishaWeightCount += w;
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}
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}
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return { fajrMin, ishaMin };
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});
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return {
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rmsMinutes: totalWeight > 0 ? Math.sqrt(totalWeightedSS / totalWeight) : 0,
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fajrBiasMinutes: fajrWeightCount > 0 ? fajrWeightedBias / fajrWeightCount : 0,
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ishaBiasMinutes: ishaWeightCount > 0 ? ishaWeightedBias / ishaWeightCount : 0,
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residuals,
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};
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}
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