photutils/segmentation/properties.py
def _data_cutout_maskzeroed_double(self):
        """
        A 2D cutout from the (background-subtracted) (filtered) data,
        where pixels outside of the source segment and masked pixels are
        set to zero.

        Invalid values (e.g. NaNs or infs) are set to zero.  Negative
        data values are also set to zero because negative pixels
        (especially at large radii) can result in image moments that
        result in negative variances.  The cutout image is double
        precision, which is required for scikit-image's Cython-based
        moment functions.
        """

        cutout = self.make_cutout(self._filtered_data, masked_array=False)
        cutout = np.where(np.isfinite(cutout), cutout, 0.)
        cutout = np.where(cutout > 0, cutout, 0.)    # negative pixels -> 0

        return (cutout * ~self._cutout_total_mask).astype(np.float64)

        A 2D cutout from the (background-subtracted) (filtered) data,
        where pixels outside of the source segment and masked pixels are
        set to zero.

        Invalid values (e.g. NaNs or infs) are set to zero.  Negative
        data values are also set to zero because negative pixels
        (especially at large radii) can result in image moments that
        result in negative variances.  The cutout image is double
        precision, which is required for scikit-image's Cython-based
        moment functions.
        
A 2D cutout from the (background-subtracted) (filtered) data,
where pixels outside of the source segment and masked pixels are
set to zero.

Invalid values  are set to zero.  Negative
data values are also set to zero because negative pixels
(especially at large radii) can result in image moments that
result in negative variances.  The cutout image is double
precision, which is required for scikit-image's Cython-based
moment functions.
A 2D cutout from the ( background - subtracted ) ( filtered ) data where pixels outside of the source segment and masked pixels are set to zero . Invalid values are set to zero . Negative data values are also set to zero because negative pixels ( especially at large radii ) can result in image moments that result in negative variances . The cutout image is double precision which is required for scikit - image ' s Cython - based moment functions .
def _data_cutout_maskzeroed_double(self):
        cutout = self.make_cutout(self._filtered_data, masked_array=False)
        cutout = np.where(np.isfinite(cutout), cutout, 0.)
        cutout = np.where(cutout > 0, cutout, 0.)    
        return (cutout * ~self._cutout_total_mask).astype(np.float64)
def _data_cutout_maskzeroed_double ( self ) : cutout = self . make_cutout ( self . _filtered_data , masked_array = False ) cutout = np . where ( np . isfinite ( cutout ) , cutout , 0. ) cutout = np . where ( cutout > 0 , cutout , 0. ) return ( cutout * ~ self . _cutout_total_mask ) . astype ( np . float64 )
A 2D cutout from the (background-subtracted) (filtered) data,
where pixels outside of the source segment and masked pixels are
set to zero.
A 2D cutout from the ( background - subtracted ) ( filtered ) data where pixels outside of the source segment and masked pixels are set to zero .
"""
        A 2D cutout from the (background-subtracted) (filtered) data,
        where pixels outside of the source segment and masked pixels are
        set to zero.

        Invalid values (e.g. NaNs or infs) are set to zero.  Negative
        data values are also set to zero because negative pixels
        (especially at large radii) can result in image moments that
        result in negative variances.  The cutout image is double
        precision, which is required for scikit-image's Cython-based
        moment functions.
        """ # negative pixels -> 0
self