Descriptions of configuration parameters

The following parameters control PySE’s source extraction and export settings.

ImgConf attributes: configuration options for image processing and source extraction.

interpolate_order: Order of interpolation - e.g. 1 for linear - to use to derive the background mean and background standard deviation (rms) maps from the corresponding background grid values. The nodes of the background grids are centred on the subimages of size back_size_x by back_size_y.

median_filter: Size of the median filter to apply to background and RMS grids prior to interpolating. This is used to discard outliers. Use 0 to disable.

mf_threshold: Threshold (Jy/beam) used with the median filter if median_filter is non-zero. This is used to only discard outliers (i.e. extreme background mean or rms node values) beyond a certain threshold. Use 0 to disable.

rms_filter: Any interpolated background standard deviation (rms) value should be above this threshold times the median of all background standard deviation (rms) node values. This is used to avoid picking up sources towards the edges of the image where the values of the background rms map may be the result of poor interpolation, i.e. are the result of extrapolation rather than interpolation. Use 0 to disable.

deblend_mincont: Minimum flux density fraction (relative to the original, i.e. unblended, island) required for a subisland to be considered a valid deblended component.

structuring_element: The ‘structuring element’ defines island connectivity as in ‘4-connectivity’ and ‘8-connectivity’ as a Python-style nested list, e.g. ‘[[1,1,1], [1,1,1], [1,1,1]]’ for 8-connectivity and ‘[[0,1,0], [1, 1,1], [0,1,0]]’ for 4-connectivity. These two are the only reasonable choices, since the structuring element must be centrosymmetric. The structuring element is applied in scipy.ndimage.label, so check its documentation for some background on its use.

vectorized: Measure sources using a vectorized implementation of the ‘tweaked moments’ method. Compared to Gaussian fitting, this approach is much faster and more suitable for large numbers of sources. Peak spectral brightnesses remain biased downward, but generally show a smaller negative bias than Gaussian fits. The derived elliptical source axes (major and minor) tend to be biased upward, typically more so than for Gaussian fits.

nr_threads: The number of threads used to parallelize Gaussian fits to detected sources. This integer sets ‘max_workers’ in ‘concurrent.futures.ThreadpoolExecutor’. Expect speedups when using a free-threading version of Python, but this integer is probably best set to 1 if you’re not. Note: this does not change numba’s ‘NUMBA_NUM_THREADS’ for the numerous parallel numba operations in PySE.

margin: Margin in pixels to ignore near the edges of the image, i.e. sources within this margin will not be detected.

radius: Radius in pixels (from image center) considered valid, i.e. sources beyond this radius will not be detected.

back_size_x: Subimage size for estimation of background node values (X- direction). The nodes are centred on the subimages.

back_size_y: Subimage size for estimation of background node values (Y- direction). The nodes are centred on the subimages.

grid: Background subimage size used as fallback for back_size_x and back_size_y. If both are not set, this implies back_size_x=backsize_y=grid, i.e. the subimages are squares.

eps_ra: Calibration uncertainty in right ascension (degrees), see equation 27a of the NVSS paper.

eps_dec: Calibration uncertainty in declination (degrees), see equation 27b of the NVSS paper.

clean_bias: Clean bias to subtract from the peak brightnesses (Jy/beam), see parapagraph 5.2.5 and equation 34 of the NVSS paper.

clean_bias_error: 1-sigma uncertainty in clean bias (Jy/beam), see parapagraph 5.2.5 and equation 37 of the NVSS paper.

frac_flux_cal_error: Intensity-proportional calibration uncertainty, see paragraph 5.2.5 and equation 37 of the NVSS paper.

alpha_maj1: First exponent for scaling errors along the fitted major axis, see equation 26 and paragraph 5.2.3 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

alpha_maj2: Second exponent for scaling errors along the fitted major axis, see equation 26 and paragraph 5.2.3 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

alpha_min1: First exponent for scaling errors along the fitted minor axis and for scaling errors in the position angle, see equation 26 and paragraph 5.2.3 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

alpha_min2: Second exponent for scaling errors along the fitted minor axis and for scaling errors in the position angle, see equation 26 and paragraph 5.2.3 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

alpha_brightness1: First exponent for scaling errors in peak brightness, see equation 26 and paragraph 5.2.5 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

alpha_brightness2: Second exponent for scaling errors in peak brightness, see equation 26 and paragraph 5.2.5 of the NVSS paper and equation 41 and paragraph 3 of Condon’s (1997) “Errors in Elliptical Gaussian Fits”.

detection_thr: Detection threshold as multiple of the background standard deviation (rms) map, after the background mean values have been subtracted from the image.

analysis_thr: Analysis threshold as multiple of the background standard deviation (rms) map, after the background mean values have been subtracted from the image. Island pixels above the analysis threshold are used for the measurement of the source. The analysis threshold must be lower than or equal to the detection threshold.

fdr: Use False Detection Rate (FDR) algorithm for determining the detection threshold.

alpha: FDR alpha value (float, default 0.01) that sets an upper limit on the fraction of pixels erroneously detected as source pixels, relative to all source pixels. This requirement should be met when averaged over a large ensemble of images, but problems were encountered with alpha as low as 0.001, see paragraph 3.6 of Spreeuw’s thesis.

deblend_nthresh: Number of deblending subthresholds; 0 to disable.

bmaj: Major axis of restoring beam (degrees).

bmin: Minor axis of restoring beam (degrees).

bpa: Restoring beam position angle (degrees).

force_beam: Force source shape to align restoring beam shape (bmaj, bmin, bpa) for Gaussian fits and vetorized source measurement, i.e. when vectorized=True.

detection_image: Path to detection map. PySE will identify sources and the positions of pixels which comprise them on the detection image, but then use the corresponding pixels on the target images to perform measurements. Of course, the detection image and the target image(s) must have the same pixel dimensions. Note that only a single detection image may be specified, and the same pixels are then used on all target images. Note further that this detection-image option is incompatible with –fdr

fixed_posns: JSON __list__ of RA, Dec pairs of coordinates to measure sources at (disables blind extraction and vectorized source measurements).

fixed_posns_file: Path to JSON file with RA, Dec pairs of coordinates to measure sources at (disables blind extraction and vectorized source measurements).

ffbox: When fitting to a fixed position, a square “box” of pixels is chosen around the requested position, and the optimization procedure allows the source position to vary within that box. The size of the box may be changed with this option. Note that this parameter is given in units of the major axis of the beam in pixels.

ew_sys_err: Systematic error in east-west direction, see paragraph 5.2.3 of the NVSS paper. Note that this parameter is currently not applied in PySE, because it should be considered a final step before entering source parameters in a catalog, i.e. it is simply returned to allow for systematic positional offset cf. the NVSS. Therefore, its unit (degrees, arcseconds) is up to the user.

ns_sys_err: Systematic error in north-south direction, see paragraph 5.2.3 of the NVSS paper. Note that this parameter is currently not applied in PySE, because it should be considered a final step before entering source parameters in a catalog, i.e. it is simply returned to allow for systematic positional offset cf. the NVSS. Therefore, its unit (degrees, arcseconds) is up to the user.

remove_edge_sources: When source pixels - with values above the analysis threshold - connect with the edge of a map or with masked pixels, do not measure the source properties. Consequently, the parameters of this source will not be returned. The idea here is that, when source pixels are adjacent to edges or masked pixels, pixels needed for a measurement that is symmetrical relative to the source’s barycenter will likely be missing, which will compromise the measurement.

ExportSettings attributes: configuration options for export of source finding results.

output_dir: Directory in which to write the output files.

file_type: Output file type (default: csv). As of 20260114 this attribute does not seem to be effectuated; csv is the only supported output file type, through the ‘csv’ attribute. This attribute should provide for a range of output formats, e.g. HDF5 anc CSV and replace the ‘csv’ attribute.

skymodel: Generate a sky model.

csv: Generate a CSV text file (e.g., for TopCat).

regions: Generate DS9 region file(s).

rmsmap: Generate map with the root-mean-square (RMS) of the background noise.

sigmap: Generate a significance map, i.e. the observational data - with mean background subtracted - divided by the RMS map.

residuals: Generate a residuals map, i.e. a map where the Gaussian reconstructions of all detected sources have been subtracted from the observational data, with mean background subtracted.

islands: Generate an islands map, i.e. a map with the Gaussian reconstructions of all detected sources.

pandas_df: If True, the measured and derived source parameters will be returned as a Pandas DataFrame. If false, they will be returned as a `utility.containers.ExtractionResults` object. As of 20260114 not yet available for the command-line interface.

source_params: List of source parameters to collect.

source_params_file: List of source parameters to include in a file for storage.