leosatpy 0.1.1

LEOSatpy is a highly-automated end-to-end pipeline for the reduction, calibration, and analysis of Low Earth Orbit Satellite observations from various telescopes.

LEOSatpy (Low Earth Orbit satellite python) is an end-to-end pipeline to process and analyse satellite trail observations from various telescopes.

The pipeline is written in Python 3 and provides the following functionalities:

Module	Function
reduceSatObs	Full reduction of raw-FITS images including bias, dark, and flat reduction.
calibrateSatObs	WCS calibration, i.e. plate solving, using GAIA DR3 positions via the Astroquery tool.
analyseSatObs	Satellite trail(s) detection and aperture photometry using comparison stars from the GSC v2.4.3 catalog.

The full documentation for LEOSatpy can be found here.

LEOSatpy is distributed under the GNU General Public License v3. See the LICENSE file for the precise terms and conditions.

Currently supported telescopes:

• 0.6-metre Chakana telescope at the Ckoirama Observatory of the Universidad de Antofagasta, Antofagasta, Chile.

• 0.9-metre Small and Moderate Aperture Research Telescope System (SMARTS) at Cerro Tololo Inter-american Observatory (CTIO), Chile.

• 1.54-metre Danish telescope at the La Silla Observatory, Chile.

• 0.28-metre DDOTI (Deca-Degree Optical Transient Imager) telescopes at the Observatorio Astronómico Nacional (OAN) in Sierra San Pedro Martír (SPM), Baja California, México.

• 0.5-metre MOSS telescope at the Oukaïmeden Observatory, Morocco.

• 0.6-metre telescope of the Chungbuk National University Observatory in Jincheon, South Korea.

• 1.23-metre telescope at the Calar Alto Observatory, Spain.

Note

If you want your telescope added to the list, please contact Jeremy Tregloan-Reed.

How to use LEOSatpy

The LEOSatpy pipeline is written for use with Python >=3.9. To avoid unnecessary interference with the Python installed on the system, it is recommended to create a new Python environment to run LEOSatpy, using for example conda.

A new conda environment can be created and activated with:

$ conda create -n leosatpy_env python=3.9
$ conda activate leosatpy_env

Installation

LEOSatpy is available on PyPI, and can be installed using pip:

$ (leosatpy_env) pip install leosatpy

Alternatively, the latest release of LEOSatpy is also available from the GitHub repository.

1. Clone the repository using git:

$ (leosatpy_env) git clone https://github.com/CLEOsat-group/leosatpy.git

2. Download the zip file from the GitHub repository:

   Navigate to the main page of the repository. Click on the “Code” button, then click “Download ZIP”.

Once cloned or downloaded and extracted, LEOSatpy can be installed from anywhere by typing:

$ (leosatpy_env) pip install -e PATH/TO/CLONED/GITHUB

or by navigating to the downloaded folder:

$ (leosatpy_env) cd PATH/TO/CLONED/GITHUB

and using the following command in the terminal:

$ (leosatpy_env) python setup.py install

The successful installation of LEOSatpy can be tested by trying to access the help or the version of LEOSatpy via:

$ (leosatpy_env) reduceSatObs --help

$ (leosatpy_env) reduceSatObs --version

If no error messages are shown, LEOSatpy is most likely installed correctly.

Running LEOSatpy

Prerequisites

1. Configuration

LEOSatpy comes with a configuration file, called leosatpy_config.ini, containing an extensive list of parameter that can be adjusted to modify the behaviour of LEOSatpy.

Important

Upon the first execution, a copy of the leosatpy configuration file is placed in the /home/user directory. Please modify the file as required and re-run the program.

By default, information and results for each dataset are stored in a .csv file located in the /home/user directory. The location and name of this file can be changed by modifying the following lines in the leosatpy_config.ini:

RESULT_TABLE_PATH = '~'
RESULT_TABLE_NAME = 'results_leosatpy.csv'

2. Folder structure

Although there is some degree of freedom in the nomenclature and structuring of the folder, it is highly recommended to adopt the following folder layout:

.
└── Telescope-Identifier <- free naming
    ├── YYYY-MM-DD <- recommended format
    │   ├── bias
    │   ├── flats
    │   ├── darks
    │   └── science_data <- free naming
    │       └── raw <- optional, but recommended
    ├── YYYY-MM-DD
    └── YYYY-MM-DD

The only requirement with regard to the name of the main folder is that the folder name should contain the date of observation either in the format: YYYY-MM-DD, or YYYYMMDD.

The program will select the search path for the calibration data based on the obs date from the science data header and the names of folder in the given path. Possible formats are, e.g., 20221110, 2022-11-20, tel_20221011_satxy, 2022-11-26_satxy_obs1, etc.

Note

The program can detect and handle if the name of the folder does not corresponds to the observation date. However, the difference in date should not exceed 7 days. For example, data observed on 2022-11-11 UTC might be located in a folder named 2022-11-10. <– This is detected.

It is furthermore recommended to separate the raw calibration files, i.e., bias, darks, and flats, from the science observation files and place them into separate folder.

Once all programs have been executed, the final folder structure should look like this:

.
└── Telescope-Identifier
    ├── YYYY-MM-DD
    │   ├── bias
    │   ├── flats
    │   ├── darks
    │   ├── master_calibs
    │   └── science_data (e.g., STARLINK)
    │       ├── auxiliary
    │       ├── calibrated
    │       ├── catalogs
    │       ├── figures
    │       │   └── Sat-ID (e.g., STARLINK-3568)
    │       ├── raw
    │       └── reduced
    ├── YYYY-MM-DD
    └── YYYY-MM-DD

Attention!

To prevent unexpected behaviour during the program execution, please also check and make sure that:

• the raw FITS-files contain data

• FITS-header keywords (e.g., IMAGETYP of bias, flats, or science files) are correctly labeled

• corresponding raw FITS calibration images are available (e.g., binning, exposure time, filter)

LEOSatpy is now ready for use.

Reduction

The reduction of all raw FITS-files in a folder can be performed via the following line:

$ (leosatpy_env) reduceSatObs PATH/TO/DATA

LEOSatpy also accepts relative paths and multiple inputs, for example:

$ (leosatpy_env) reduceSatObs ../Telescope-Identifier/YYYY-MM-DD/

$ (leosatpy_env) reduceSatObs PATH/TO/DATA/NIGHT_1 PATH/TO/DATA/NIGHT_2

To reduce all data from a telescope at once with:

$ (leosatpy_env) reduceSatObs PATH/TO/TELESCOPE/DATA

Note

The usage of partial and multiple inputs as shown above also works for the other programs in the package.

Astrometric calibration

To apply the astrometric calibration type:

$ (leosatpy_env) calibrateSatObs PATH/TO/DATA

Satellite trail detection and analysis

To run the satellite detection and analysis on all files in the input type:

$ (leosatpy_env) analyseSatObs PATH/TO/DATA

Citing LEOSatpy

When publishing data processed and analysed with LEOSatpy, please cite:

Adam, C. et al., 2023 (in preparation). "Estimating the impact to astronomy from the Oneweb satellite constellation using multicolour observations".
Software pipeline available at https://github.com/CLEOsat-group/leosatpy.

Acknowledgements

Alongside the packages listed in the requirements.txt, this project uses workflows and code adopted from the following packages:

• Astrometry under the GPLv3 License, Lukas Wenzl (2022), Zenodo

• AutoPhOT under the GPLv3 License, Brennan & Fraser (2022), NASA ADS

The authors of these packages and code are gratefully acknowledged.

Special thanks go out to the following people for their ideas and contributions to the development of the LEOSat Python package:

• Jeremy Tregloan-Reed, Universidad de Atacama

• Eduardo Unda-Sanzana, Universidad de Antofagasta

• Edgar Ortiz, Universidad de Antofagasta

• Maria Isabel Romero Colmenares, Universidad de Atacama

• Sangeetha Nandakumar, Universidad de Atacama

The project would not have been possible without the help of everyone who contributed.

Feedback, questions, comments?

LEOSatpy is under active development and help with the development of new functionalities and fixing bugs is very much appreciated. In case you would like to contribute, feel free to fork the GitHub repository and to create a pull request.

If you encounter a bug or problem, please submit a new issue on the GitHub repository providing as much detail as possible (error message, operating system, Python version, etc.).

If you have further feedback, questions or comments you can also send an e-mail to Jeremy Tregloan-Reed, or Christian Adam.

Author

Christian Adam, Centro de Investigación, Tecnología, Educación y Vinculación Astronómica (CITEVA), Universidad de Antofagasta, Antofagasta, Chile