Source finding package for radio continuum data powered by U-Net segmentation algorithm.
The project is available on PyPI, to install latest stable release use:
pip install continunetTo install version in development, use:
pip install git+https://github.com/hstewart93/continunetContinUNet has a Python 3.9 minimum requirement.
If you want to contribute to the repository, install as follows:
Once you have cloned down this repository using git clone, cd into the app directory:
git clone git@github.com:hstewart93/continunet.git
cd continunetCreate a virtual environment for development, if you are using bash:
python3 -m venv venv
source venv/bin/activate
pip install -e .[dev,ci]To exit the virtual environment use deactivate.
This project used the black auto formatter which can be run on git commit along with flake8 if you install pre-commit. To do this run the following in your terminal from within your virtual environment.
pre-commit installNow pre-commit hooks should run on git commit.
To run the test suite use pytest.
The package currently support .FITS type images. To perform source finding you can import the finder module:
from continunet.finder import FinderLoad your image file:
finder = Finder("<filepath>")To produce a source catalogue and populate the Finder instance:
sources = finder.find()If you want to calculate the model map and residuals image as part of source finding, use the Finder.find() method with generate_maps=True:
sources = finder.find(generate_maps=True)
model_map = finder.model_map
residuals = finder.residualsAlternatively, manually calculate model map and residual images using:
model_map = finder.get_model_map()
residuals = finder.get_residuals()Useful available attributes of the Finder object are:
finder.sources # cleaned source catalogue
finder.reconstructed_image # predicted image reconstructed by unet module
finder.segmentation_map # predicted segmentation map
finder.model_map # model map of cleaned predicted sources
finder.residuals # residual image as numpy array
finder.raw_sources # sources from labelled segmentation map before cleaningExport source catalogue using finder.export_sources as .csv by default or .FITS by setting export_fits=True:
finder.export_sources("<filepath>", export_fits=<Boolean>)Source parameters extracted are:
| Parameter | Description |
|---|---|
x_location_original |
x coordinate of the source from the cutout used for inference |
y_location_original |
y coordinate of the source from the cutout used for inference |
orientation |
orientation of source ellipse in radians |
major_axis |
major axis of source ellipse |
minor_axis |
minor axis of source ellipse |
flux_density_uncorrected |
total intensity of the segmented source region before beam corrections applied |
label |
class label in predicted segmentation map |
x_location |
x coordinate of the source in the original input image dimensions |
y_location |
y coordinate of the source in the original input image dimensions |
right_ascension |
RA coordinate of the source in the original input image dimensions |
declination |
Dec coordinate of the source in the original input image dimensions |
area |
area of source ellipse |
position_angle |
position angle of source ellipse in degrees |
correction_factor |
correction factor applied to flux density measurement to account for undersampling of synthesised beam |
flux_density |
corrected flux density |
ContinUNet is subject to ongoing development. To see the backlog of features and bug fixes please go to the project board. Please raise any feature requests or bugs as issues.
The following features will be added in the next release: