OBSY is an Open Source observatory management system intended for amateur astronomers who want to automate data collection in their observatories. The system forms a meta layer over the KStars/EKOS imaging and telescope control system,
feeding nightly schedules to the EKOS Scheduler and capturing the results.
OBSY is the glue that ties together a number of sub-projects into a single cohesive system. As such development of the OBSY system will be completed to support deployment of the sub-projects, which will initially be stand alone (and useful to amateur astronomers as such) then will be integrated into OBSY. Stages will be:
- EKOS Script Integration for calibration and registration in OBSY database, including livestacking during imaging runs. Also support for calibration libraries
- Core Database tables maintainable through Django
- Target selection and scheduling setup to produce XML schedule files for EKOS nightly processing, "pretty picture" imaging, and photometry of variable stars and exoplanet transits.
- Master Control Program to examine current conditions (including weather station output, ML Cloud Detection, etc.), open the observatory and initiate EKOS Schedules, watch for possible condition changes that would require schedules to be cancelled and the roof closed.
- Automated image calibration with calibration libraries
- Automated stacking and stretching of images as appropriate (for creation of thumbnails and sample images)
- Photometry data reduction and reporting to AAVSO
- Exoplanet observations and automated data reduction
OBSY will run on Linux/Unix environments, with all development occurring on the Stellarmate X OS, which is a Debian derived distribution of Linux with bundled astronomy software and a IOS/Android app. All code is written in Python3 with web infrastructure provided by Django. The two primary components of OBSY are:
- MCP.py (Master Control Program) is a Python based service which controls all integration with INDI, KStars/EKOS, Weather, Rain, INDI-Allskycam, and machine learning based cloud detection. Initially this code will be very specific to my personal installation but will be generalized over time. Results are stored in a SQL database for use by the OBSY User interface, with assets (eg FITS files with images) stored in a Data Repository linked to the database. MCP will also initiate jobs such as automated master generation, calibration of images, and stacking of sequences of images (depending on target attributes) using Siril.
- Obsy-Web - using the SQL database populated by MCP operations, the web site provides a user interface where the user can select targets, view collected data, analyze and report on the data, and configure the overall system. Schedules are then created to drive MCP operations.
Initially the SQL Database will be SQLite but will be easily migrated to MySQL or Postgres.
Currently working on sub-projects as follows:
- OBSY - Building user interface, handling for target searching and selection, setup configuration tables (STARTED) Next will be data repository creation and management functions using initially Dropbox (because I already have an account, the functions will allow AWS etc later)
- EKOSProcessingScripts Scripts to use with EKOS to integrate with OBSY. For example a post-processing script that calibrates images, stores the FITS data files in a repository, and loads summary and image information into the OBSY database. (TESTING)
- Photometry-Pipeline Python script for processing of images via differential photometry (STARTED)
- MLCloudDetect Machine Learning based cloud detection for allsky cameras (COMPLETE)
- PythonEkosFiles Python objects for reading and writing Ekos sequence and schedule files (STARTED)
Full installation instructions will be provided when the initial release is created.