Goodman Photometry
Routines to perform automatic astrometry and photometry of Goodman imaging observations.
The codes were initially based on STDPipe (https://github.com/karpov-sv/stdpipe) and adapted for Goodman HST.
Features
- Performs automatic astrometry to add celestial WCS to FITS files
- Calculates photometric zero points using Gaia-DR2 catalog
- Includes auxiliary functions for data processing
- Provides both command-line and Python API interfaces
Installation
To install the package, run:
pip install goodman-photometry
Prerequisites
- Python 3.10+
- Required dependencies:
- astropy
- astroplan
- ccdproc
- cython
- matplotlib
- numpy
- packaging
- pandas
- requests
- scipy
- statsmodels
- astroquery
- sip_tpv
- setuptools
Usage
Command Line Interface
The package provides command-line scripts for processing observations:
#Process astrometry
redastrometry -i input.fits -o output.fits
# Process photometry
redphotometry -i input.fits -o output.fits
Python API
You can also use the package as a library in your Python code: from goodman_photometry import Astrometry, Photometry
Initialize astrometry processor
# The values of the parameters are set to the default values. So an empty call will work as well.
astrometry = Astrometry(
catalog_name='gaiadr2',
magnitude_threshold=17,
scamp_flag=1,
color_map='Blues_r',
save_plots=False,
save_scamp_plots=False,
save_intermediary_files=False,
debug=False
)
# Process the FITS file
astrometry('input.fits', 'output.fits')
# Initialize photometry processor, it will use the default values for the parameters.
photometry = Photometry()
photometry.process('input.fits', 'output.fits')
Contributing
- Fork the repository
- Create a feature branch: git checkout -b feature-name
- Commit your changes: git commit -m "description"
- Push to the branch: git push origin feature-name
- Open a Pull Request
Please make sure to:
- Include tests for new functionality
- Update documentation
- Follow PEP8 style guidelines
Contact Information
For questions, bug reports, or suggestions, please contact:
- Felipe Navarete - felipe.navarete@noirlab.edu
- Simón Torres - simon.torres@noirlab.edu
Project Links
- Homepage: https://github.com/soar-telescope/goodman_photometry
- Bug Reports: https://github.com/soar-telescope/goodman_photometry/issues
- Source Code: https://github.com/soar-telescope/goodman_photometry
License
This project is licensed under the BSD License. See the LICENSE file for details.
Metadata
Release files for goodman-photometry 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| goodman_photometry-1.1.0.tar.gz | 97.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| goodman_photometry-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 161.6 kB
Release files / goodman_photometry-1.1.0.tar.gz
| Download URL | goodman_photometry-1.1.0.tar.gz |
|---|---|
| Size | 97.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
1fa725d263a2c1f62ea95f4d0ad2c0d81a745f54fbb1851784e62dfc3b892f92
|
|
BLAKE2b-256 checksum How to use checksums |
64df64efdeef04f2d3ab26e6dbeabf0c4fcacdaa87d253644fd0f5d9623d4bc4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 6, 2025.
Transparency logRelease files / goodman_photometry-1.1.0-py3-none-any.whl
| Download URL | goodman_photometry-1.1.0-py3-none-any.whl |
|---|---|
| Size | 64.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
99c968ce0fbaf41120639c7a84d04d5c5b8debcb8ecb9eb63ddf0a57d5fdc198
|
|
BLAKE2b-256 checksum How to use checksums |
a8fc465b29f5116867f999bfde6a1163d9e337c95f9c3b3b9cd617edb24981f7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 6, 2025.
Transparency log