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pray-calc-ml

A Python data science project that compiles human-verified Islamic prayer sighting records and back-calculates solar depression angles. The goal is to find the real empirical patterns in how the Fajr and Isha angles vary with latitude, season, and elevation, then use machine learning to refine the DPC (Dynamic Pray Calc) algorithm in pray-calc.

Pages

  • Data Collection — how to run the pipeline, add new sources, and expand the dataset
  • ML Crunching — how to run the analysis notebook and train ML models
  • Architecture — how the pipeline works, data schema, quality filters
  • Data Sources — full citation table for all sighting records
  • Research Notes — academic paper summaries (not training data)

Quick start

git clone https://github.com/acamarata/pray-calc-ml.git
cd pray-calc-ml
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Generate datasets (requires network for OpenFajr iCal + elevation API)
python -m src.pipeline

# Or skip the elevation API:
python -m src.pipeline --no-elevation-lookup

Output: data/processed/fajr_angles.csv and data/processed/isha_angles.csv

Current dataset

Dataset Records Locations Latitude range Date range
Fajr ~4,105 35 -37.8° to 53.7° 1985-2026
Isha ~43 20+ -33.9° to 53.7° 1985-2019

Key finding

Near-equatorial sites (Malaysia, Indonesia, 2°-7°) show mean Fajr angles of 16°-17°, while high-latitude sites (Birmingham, UK, 52°N) average ~13°. Seasonality is a significant second factor — at 52°N, the Fajr angle has a ~3° peak-to-trough seasonal swing. Elevation shows a smaller but real positive correlation.

The 18° fixed angle commonly used by ISNA and MWL overstates the observed true dawn angle at virtually all well-documented sites.


Part of the acamarata Islamic computing library suite.