detect_threshold#

photutils.segmentation.detect_threshold(data, nsigma, *, background=None, error=None, mask=None, sigma_clip=SigmaClip(sigma=3.0, sigma_lower=3.0, sigma_upper=3.0, maxiters=10, cenfunc='median', stdfunc='std', grow=False))[source]#

Calculate a pixel-wise threshold image that can be used to detect sources.

This is a simple convenience function that uses sigma-clipped statistics to compute a scalar background and noise estimate. In general, one should perform more sophisticated estimates, e.g., using Background2D.

Parameters:

data2D ndarray: The 2D array of the image.
nsigmafloat: The number of standard deviations per pixel above the background for which to consider a pixel as possibly being part of a source.
backgroundfloat or 2D ndarray, optional: The background value(s) of the input data. background may either be a scalar value or a 2D array with the same shape as the input data. If the input data has been background-subtracted, then set background to 0.0 (this should be typical). If None, then a scalar background value will be estimated as the sigma-clipped image mean.
errorfloat or 2D ndarray, optional: The Gaussian 1-sigma standard deviation of the background noise in data. error should include all sources of “background” error, but exclude the Poisson error of the sources. If error is a 2D image, then it should represent the 1-sigma background error in each pixel of data. If None, then a scalar background rms value will be estimated as the sigma-clipped image standard deviation.
mask2D bool ndarray, optional: A boolean mask with the same shape as data, where a True value indicates the corresponding element of data is masked. Masked pixels are ignored when computing the image background statistics.
sigma_clipastropy.stats.SigmaClip instance, optional: A SigmaClip object that defines the sigma clipping parameters.

Returns:

threshold2D ndarray: A 2D image with the same shape (and units) as data containing the pixel-wise threshold values.