Название: Design, Development and Analysis of Lunar Crescent Visibility Criterion With Python
Автор: Muhamad Syazwan Bin Faid, Mohd Saiful Anwar Mohd Nawawi, Mohd Hafiz Mohd Saadon
Издательство: CRC Press
Год: 2025
Страниц: 144
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

The analysis of lunar crescent visibility criterion is vital in providing a comparative insight for predicting the visibility and suitability for Hijri calendar determination. While there have been previous attempts to measure the performance of lunar crescent visibility criterion, these attempts only apply to a singular analysis and not a comparative examination.

Design, Development and Analysis of Lunar Crescent Visibility Criterion with Python explores the development of an analysis tool for lunar crescent visibility criterion using an integrated lunar crescent visibility database. The analysis tool, called HilalPy and HilalObs, was developed in the form of a Python library, so that it can be integrated into other software and webpages to enable deployment into various operating systems. This book will provide useful insights for the future development of lunar crescent visibility criterion, particularly for calendrical purposes.

This chapter discusses the development of an analysis tool for the lunar crescent visibility criteria using an integrated lunar crescent observation database. This chapter is divided into four parts: Data collection, data calculation, data analysis, and tool development. Data collection details the data mining methodology in collecting the lunar crescent observation database from literature. Data calculation details the recalculation of the data using the latest astrometry library, Skyfield. Next, data analysis details the Python library used to analyze the data and the types of the analysis that can be conducted on the data. Methodology chapter concludes with the design and development of a Python package for the analysis tool for the lunar crescent visibility criteria using integrated lunar crescent sighting database and the steps in utilizing the Python library.

Key Features:

- Presents an analysis tool for lunar crescent visibility based on an integrated lunar crescent report database.
- Offers researchers and practitioners the capability to perform comparative analyses of the percentage of reliability in lunar crescent visibility criteria, thereby contributing to the construction of robust criteria for determining the Hijri calendar.
- Provides a comprehensive resource for researchers, students, policymakers, and practitioners involved in the determination of the Hijri calendar.








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