source_extraction

trap.source_extraction.rms(data: ndarray) → ndarray[source]

Returns the RMS of the data about the median. :param data: a numpy array

trap.source_extraction.subregion(data: ndarray, reduction_factor: float = 2) → ndarray[source]

Take the center region of the data where the size of the center region is defined by reduction_factor.

Parameters:

  • data (np.ndarray) – The image data to clip.

  • reduction_factor (float) – The factor by which to decrease the image. This applies to each axis, so if reduction_factor=2 and the image is of shape 100x100, then the shape of the clipped center region is 50x50. This effectively reduces the image size by a factor 4 (10.000 pixels to 2500 pixels). If reduction_factor=1, the original 100x100 pixels are returned. It is also possibly to supply a floating point number. For the same 100x100 image, if a reduction_factor=1.5 is supplied, the resulting slice is of shape (67,67). The way to make sense of this is that 100/1.5=66.6667.

Examples

>>> import numpy as np
>>> data = np.array([
...     [0, 1, 2, 1, 0],
...     [1, 2, 3, 2, 1],
...     [2, 3, 4, 3, 2],
...     [1, 2, 3, 2, 1],
...     [0, 1, 2, 1, 0]
... ])
>>> subregion(data, 2)
array([[2, 3, 2],
       [3, 4, 3],
       [2, 3, 2]])
Returns:

numpy.ndarray

Return type:

A slice of the original data at the center of the data.

trap.source_extraction.clip(data: ndarray, sigma: float = 3) → ndarray[source]

Remove all values above a threshold from the array. Uses iterative clipping at sigma value until nothing more is getting clipped.

Parameters:

  • data (numpy.ndarray) – The data to clip

  • sigma (float) – The amount of standard deviations that determine the threshold for clipping

Returns:

The data with the high values removed

Return type:

np.ndarray

trap.source_extraction.read_pyse_image(path: str, freq_bands: FrequencyBands | None = None, margin=0, radius=1500, back_size_x=50, back_size_y=50, reduction_factor_for_rms=4, rms_min=0, rms_max=1, force_beam=True, ew_sys_err=0, ns_sys_err=0, deblend_nthresh=0, **pyse_conf) → Tuple[ImageData, dict, bool][source]

Read an image with PySE that can be used in functions like sources_from_fits_pyse() and force_fit(). Some basic image metadata is also read and calculated. The metadata fields are:

  • rms

    The rms of the inner region of the image. The size of this inner region is determined by reduction_factor_for_rms.

  • rms_min

    Lowest value of the RMS background map. Note that this is different from rms which uses the center region of the image rather than a RMS background map.

  • rms_max

    Largest value of the RMS background map. Note that this is different from rms which uses the center region of the image rather than a RMS background map.

  • freq_eff

    Effective frequency of the image in Hz. That is, the mean frequency of all the visibility data which comprises this image.

  • freq_bw

    The frequency bandwidth of this image in Hz.

  • taustart_ts

    Timestamp of the start of the observaion.

  • url

    Location of the image

  • rb_smaj

    The semi-major axis of the restoring beam, in degrees.

  • rb_smin

    The semi-minor axis of the restoring beam, in degrees.

  • rb_pa

    The position angle of the restoring beam (from north to east to the major axis), in degrees.

  • centre_ra

    The central right-ascention coordinate (J2000) (or pointing centre) of the region, in degrees.

  • centre_dec

    The central declination coordinate (J2000) (or pointing centre) of the region, in degrees.

  • xtr_radius

    The radius of the circular mask used for source extraction, in degrees.

The image is also checked for quality. The quality check involves a check for the values where the image is rejected if it has all nodta values (nan, inf, -inf) or if the rms is below the rms_min or above the rms_max values. These min and max values are set in the input arguments.

Parameters:

  • fits_path (str) – The path to the .fits file containing the image of which the sources are to be extracted.

  • margin (int) – The margin in pixels from the edge of the image within which sources are ignored. This exclusion area combines with radius.

  • radius (int,) – The radius in pixels around the center of the image, outside of which sources are ignored. This exclusion area combines with margin.

  • back_size_x (int) – Widht of the background boxes as uses in SEP. See https://sep.readthedocs.io/en/v1.1.x/api/sep.Background.html#sep.Background

  • back_size_y (int) – Height of the background boxes as used in SEP. See https://sep.readthedocs.io/en/v1.1.x/api/sep.Background.html#sep.Background

  • reduction_factor_for_rms (float) – The reduction_factor passed to subregion() used to slice the data at the center before calculating the rms.

  • ew_sys_err (float) – Systematic errors in units of arcseconds which augment the sourcefinder-measured errors on source positions when performing source association. These variables refer to an absolute angular error along an east-west and north-south axis respectively. (NB Although these values are stored during the source-extraction process, they affect the source-association process.)

  • ns_sys_err (float) – Same as ew_sys_err but in perpendicular direction

Returns: (sourcefinder.image.ImageData, dict, bool)
A tuple containing:

  • A PySE image that can be used for source extraction

  • Metadata of the image

  • Whether the image is rejected or not

trap.source_extraction.sources_from_fits_pyse(pyse_im: ImageData, *, rejected: bool = False, detection_threshold: float = 8, analysis_threshold: float = 3, deblend_nthresh: float = 0) → DataFrame[source]

Extract sources from an image using PySE.

Parameters:

  • pyse_im (sourcefinder.image.ImageData) – The pyse image as read using read_pyse_image()

  • detection_threshold (float) – The detection threshold, as a multiple of the RMS noise. At least one pixel in a source must exceed this value for it to be regarded as significant.

  • analysis_threshold (float) – Analysis threshold, as a multiple of the RMS noise. All the pixels within the island that exceed this will be used when fitting the source.

  • deblend_nthresh (int) – Number of subthresholds to use for deblending. Set to 0 to disable.

Returns:

A dataframe where each row is an obtained source. The columns contain the attributes of the corces.

Column names with explenation:

ra [deg]: float

Right ascension coordinate of the source

dec [deg]: float

Declination coordinate of the source

ra_fit_err [deg]: float

1-sigma error from the gaussian fit in right ascension. Note that for a source located towards the poles the ra_fit_err increases with absolute declination.

dec_fit_err [deg]: float

1-sigma error from the gaussian fit in declination

peak_flux [Jy]: float peak_flux_err [Jy]: float int_flux [Jy]: float int_flux_err [Jy]: float significance_detection_level: float semimajor_axis [arcsec]: float semiminor_axis [arcsec]: float parallactic_angle [deg]: float ew_sys_err [arcsec]: float

Telescope dependent systematic error in east-west direction.

ns_sys_err [arcsec]: float

Telescope dependent systematic error in north-south direction.

error_radius [arcsec]: float

A pessimistic on-sky position error estimate in arcsec.

gaussian_fit: bool chisq: float reduced_chisq: float

Return type:

pandas.DataFrame

trap.source_extraction.force_fit(pyse_im: ImageData, positions: ndarray, ew_sys_err: float = 10, ns_sys_err: float = 10) → Tuple[DataFrame, ndarray][source]

Fit the specified locations using PySE.

Parameters:

  • pyse_im (sourcefinder.image.ImageData) – The pyse image as read using read_pyse_image()

  • positions (np.ndarray) – A numpy array with the positions of the sources to be force fitted in the form [[ra_1, dec_1], [ra_2, dec_2]]

  • [arcsec] (ns_sys_err) – Telescope dependent systematic error in east-west direction.

  • [arcsec] – Telescope dependent systematic error in north-south direction.

Returns:

A dataframe with the force-fitted sources

Return type:

pd.DataFrame