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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.
36 lines
1.4 KiB
Markdown
36 lines
1.4 KiB
Markdown
# pray-calc-ml
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Machine learning calibration for Islamic prayer times. Fits optimal Fajr/Isha depression angles to observed mosque announcement data using weighted least-squares regression.
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Zero runtime dependencies. Works in Node.js, browsers, and any ESM/CJS bundler.
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## Pages
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- [API Reference](API-Reference) — full function signatures, parameters, return types
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- [Architecture](Architecture) — solver design, solar ephemeris, accuracy analysis
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- [Guide: Collecting Observations](Guide-Collecting-Observations) — how to record mosque times and build a dataset
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- [Guide: Integrating with pray-calc](Guide-Integrating-with-pray-calc) — calibrate then use in a full prayer time app
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## Quick start
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```bash
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npm install pray-calc-ml
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```
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```ts
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import { calibrateAngles } from 'pray-calc-ml';
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const result = calibrateAngles([
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{ date: new Date('2024-06-01'), lat: 40.71, lng: -74.01, tz: -4, fajr: 3.75, isha: 21.58 },
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{ date: new Date('2024-07-01'), lat: 40.71, lng: -74.01, tz: -4, fajr: 3.80, isha: 21.55 },
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{ date: new Date('2024-08-01'), lat: 40.71, lng: -74.01, tz: -4, fajr: 4.12, isha: 21.15 },
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{ date: new Date('2024-09-01'), lat: 40.71, lng: -74.01, tz: -4, fajr: 4.55, isha: 20.67 },
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]);
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console.log(result.angles); // { fajrAngle: 15.2, ishaAngle: 14.8 }
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console.log(result.rmsMinutes); // 0.31
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```
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## Repository
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[github.com/acamarata/pray-calc-ml](https://github.com/acamarata/pray-calc-ml)
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