mirror of
https://github.com/acamarata/pray-calc-ml.git
synced 2026-07-01 11:24:26 +00:00
3b8c665aca
Tracked: BSRN/SURFRAD processors (reference, excluded from pipeline), GaN-MN downloader, academic paper fetcher, Madrid SQM processor, ML analysis scripts (src/analyze/), umsu_medan_2024 raw sightings. Gitignored: global_extrapolator, instant_1m_injector/vectorized, massive_harvest_engine, massive_sqm_downloader, global_sqm_harvester, run_infinite_pipeline.sh, run_massive_collection.sh, search_papers.py (agent-generated experimental scripts, not part of core pipeline).
57 lines
2 KiB
Python
57 lines
2 KiB
Python
import pandas as pd
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import numpy as np
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import os
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def dpc_algorithm(row, prayer):
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# DPC strictly enforces 18.0 degrees for both
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return 18.0
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def _ms_fajr(lat):
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# Approximation of Khalid Shaukat moonsighting algorithm curve
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# 18 at equator, dropping incrementally past 30 deg to ~12.0
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abslat = abs(lat)
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if abslat <= 30:
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return 18.0
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else:
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# Linear drop from 18.0 at 30 deg down to 12.0 at 55 deg
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val = 18.0 - ((abslat - 30) * (6.0/25.0))
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return max(10.0, val)
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def ms_algorithm(row, prayer):
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if prayer == "fajr":
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return _ms_fajr(row['lat'])
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else:
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# Isha is slightly more forgiving or static
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return _ms_fajr(row['lat'])
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def run_evaluation(df, prayer):
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print(f"\n>> Algorithm Benchmark: {prayer.upper()} ({len(df)} Records)")
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empirical = df[f'{prayer}_angle'].values
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dpc_preds = np.array([dpc_algorithm(row, prayer) for _, row in df.iterrows()])
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ms_preds = np.array([ms_algorithm(row, prayer) for _, row in df.iterrows()])
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dpc_mae = np.mean(np.abs(empirical - dpc_preds))
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dpc_rmse = np.sqrt(np.mean((empirical - dpc_preds)**2))
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ms_mae = np.mean(np.abs(empirical - ms_preds))
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ms_rmse = np.sqrt(np.mean((empirical - ms_preds)**2))
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print(f"DPC (18/18 static) -> MAE: {dpc_mae:.3f}°, RMSE: {dpc_rmse:.3f}°")
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print(f"Moonsighting (Variable) -> MAE: {ms_mae:.3f}°, RMSE: {ms_rmse:.3f}°")
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best = "DPC" if dpc_mae < ms_mae else "Moonsighting Variant"
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print(f"WINNER: {best} predicts the physical reality more accurately.")
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def main():
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base_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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fajr_path = os.path.join(base_dir, "data", "processed", "fajr_angles.csv")
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isha_path = os.path.join(base_dir, "data", "processed", "isha_angles.csv")
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if os.path.exists(fajr_path):
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run_evaluation(pd.read_csv(fajr_path), "fajr")
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if os.path.exists(isha_path):
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run_evaluation(pd.read_csv(isha_path), "isha")
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if __name__ == "__main__":
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main()
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