sfalmanac 1.12.3

Creates the daily pages of a Nautical Almanac using Skyfield

SFalmanac is a Python 3 script that creates the daily pages of the Nautical Almanac (based on the UT timescale). The generated tables are needed for celestial navigation with a sextant. Although you are strongly advised to purchase the official Nautical Almanac, this program will reproduce the tables with no warranty or guarantee of accuracy.

      Multiprocessing version for increased performance! 

SFalmanac-Py3 can now employ multiprocessing (if your processor has multiple cores) reducing the processing time. Single-processing is also available as an option if required. Testing has been successfully performed on Windows 10 and Ubuntu 20.04 LTS. (No testing done on Mac OS.) Compared to single-processing, data processing (excluding conversion from TEX to PDF)

• … of a 6-day Nautical Almanac is 4x faster on Windows 10; 2x faster on Linux.

• … of 6-day Event Time Tables is almost 5x faster on Windows 10; 3x faster on Linux.

Windows 10 uses up to 8 threads; Linux uses up to 12 threads in parallel. Testing was performed on a PC with an AMD Ryzen 7 3700X 8-Core (16 threads) Processor. Windows & Mac OS spawn new processes; Linux forks new processes (the code is compatible with both techniques and will also run on CPUs with fewer cores/threads).

Quick Overview

This is the PyPI edition of SFalmanac-Py3 (a Changelog can be viewed here). Version numbering follows the scheme Major.Minor.Patch, whereby the Patch number represents some small correction to the intended release.

NOTE: Version numbering in PyPI restarted from 1.0 as the previous well-tested versions that exist since early 2019 were never published as PyPI packages.

The astronomical library employed is: Skyfield.

SFalmanac uses the Hipparcos catalog as its star database. If a current version of Skyfield (>= 1.31) is used, you have two options (which one, you specify by manually editing config.py):

• if “useIERS = False”, the built-in UT1 tables in the installed version of Skyfield will be employed.

• if “useIERS = True”, for optimal accuracy (specifically for all GHA data), Earth orientation data from IERS (International Earth Rotation and Reference Systems Service) is downloaded and then used until it ‘expires’. It expires after a chosen number of days (also specifiable in config.py). Note that IERS specifies the range of Earth Orientation Parameter (EOP) data from 2nd January 1973 and is updated weekly. Refer to the IERS web site for current information.

Software Requirements

Nearly all of the astronomical computation is done by the Skyfield library.

Typesetting is done typically by MiKTeX or TeX Live.

Here are the requirements/recommendations:

• python >= 3.4 (the latest version is recommended)

• skyfield >= v1.48 (the latest is recommended; see the Skyfield Changelog)

• numpy < 2.0.0 (only for Skyfield versions < 1.48)

• pandas >= 1.0 (to decode the Hipparcos star catalog)

• pandas >= 2.2.2 (if numpy version >= 2.0.0)

• MiKTeX  or  TeX Live

Installation

Install a TeX/LaTeX program on your operating system so that pdflatex is available.

Ensure that the pip Python installer tool is installed. You may check that the latest version of SFalmanac is installed:

python -m pip uninstall sfalmanac
python -m pip install sfalmanac

Installing SFalmanac ensures that Skyfield and Pandas (and their dependencies) are also installed. If previous versions of SFalmanac were installed, consider upgrading Skyfield and Pandas thus:

python -m pip install --upgrade skyfield pandas

Thereafter run it with:

python -m sfalmanac

On a POSIX system (Linux or Mac OS), use python3 instead of python in the commands above.

This PyPI edition also supports installing and running in a venv virtual environment.

Finally check or change the settings in config.py. Its location is printed immediately whenever SFalmanac runs.

Guidelines for Linux & Mac OS

Quote from Chris Johnson:

It’s best to not use the system-provided Python directly. Leave that one alone since the OS can change it in undesired ways.

The best practice is to configure your own Python version(s) and manage them on a per-project basis using venv (for Python 3). This eliminates all dependency on the system-provided Python version, and also isolates each project from other projects on the machine.

Each project can have a different Python point version if needed, and gets its own site_packages directory so pip-installed libraries can also have different versions by project. This approach is a major problem-avoider.