Software
What to install to plan an observation or work with 7DT data, and what produced the data in the first place.
The three packages
| Package | What it does | Who installs it |
|---|---|---|
| supy | Target visibility from El Sauce, tile lookup by coordinate, and filter and detector response simulation for the 7DT bands. | Anyone planning an observation or interpreting a band |
| Py7DT | The reduction pipeline: preprocessing, astrometry, photometric calibration, coaddition and difference imaging, at survey throughput. | Anyone reprocessing data rather than using the products |
| uniphot | Not yet described here. It is not in the project’s public GitHub organization, and this page will carry it once its purpose and repository are confirmed. | — |
supy
supy is a collection of Python utilities for members and users of the 7DT survey. It covers the tasks that come up before and after an observation rather than the reduction itself: working out whether a target is observable, finding which tiles cover a position or a gravitational-wave localization region, and simulating the response of the filter set.
It is installed from source. Documentation, including worked examples for each module, is published at sdt-supy.readthedocs.io.
git clone https://github.com/7DimensionalTelescope/supy.git
cd supy
pip install .| Observer | Target visibility and altitude from El Sauce, including StarAlt-style plots |
|---|---|
| Tiles | Tile lookup by coordinate, matching against a localization region, and tile plotting |
| Simulator | Filter and detector response simulation for the 7DT bands |
| const | Instrument and site constants used by the other modules |
Py7DT: running the pipeline yourself
Beyond its pipeline role, Py7DT is structured for offline reuse. Researchers inside and outside the 7DT team can run the same codebase to reprocess data with custom configurations, resuming from any stage of the reduction, and choose for themselves how far to trust the standard products. Images are passed through the pipeline as string paths with metadata in FITS headers and YAML files, rather than wrapped in a bespoke data model, which keeps products inspectable outside the pipeline and lowers the cost of learning to process 7DT data.
Py7DT uses a rolling-release version scheme in which the last digit is incremented whenever a scientific decision changes how data are processed. That version is recorded in every configuration file and in the process status database, so any product can be traced to the code that made it and reprocessed in bulk when the code changes. What a reduced file contains, keyword by keyword, is on the data format page.
The full technical description is in Hyun et al., Py7DT: Data Reduction Pipeline of the 7-Dimensional Telescope (Proc. SPIE 14155-12) — see publications.