Satellite Information Familiarization Tool
Satellite Information Familiarization Tool (SIFT) was designed by the Space Science and Engineering Center (SSEC) at the University of Wisconsin - Madison to support scientists during forecaster training events. It provides a graphical interface for visualization and basic analysis of geostationary satellite data.
SIFT is built on open source technologies like Python, OpenGL, and PyQt5. It can be run from Mac, Windows, and Linux. The SIFT application is provided as a python library called "uwsift". It can also be installed as a standalone application.
SIFT's main website is http://sift.ssec.wisc.edu/.
The Git repository where you can find SIFT's source code, issue tracker, and other documentation is on GitHub: https://github.com/ssec/sift
The project wiki with some in-depth usage and installation instructions can also be found on GitHub: https://github.com/ssec/sift/wiki
Developer documentation can be found on https://sift.readthedocs.io/en/latest/.
Data Access and Reading
SIFT uses the open source python library Satpy to read input data. By using Satpy SIFT is able to read many satellite instrument file formats, but may not be able to display or understand all data formats that Satpy can read. SIFT defaults to a limited set of readers for loading satellite instrument data. This set of readers includes but is not limited to:
- GOES-R ABI Level 1b
- Himawari AHI HRIT
- Himawari AHI HSD
- GEO-KOMPSAT-2 AMI Level 1b
Other readers can be accessed from SIFT but this is considered an advanced usage right now.
SIFT can be installed as an all-in-one bundled application or the python library "uwsift" can be installed in a traditional python environment.
Detailed installation instructions can be found on the GitHub Wiki.
SIFT is an open source project welcoming all contributions. See the Contributing Guide for more information on how you can help.
Building and releasing
For instructions on how SIFT is built and packaged see the releasing instructions. Note that these instructions are mainly for SIFT developers and may require technical understanding of SIFT and the libraries it depends on.
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