Returns - table : `~` or `None` A table of found stars. Masked pixels are ignored when searching for stars. mask : 2D bool array, optional A boolean mask with the same shape as ``data``, where a `True` value indicates the corresponding element of ``data`` is masked. Parameters - data : 2D array_like The 2D image array. abstractmethod def find_stars ( self, data, mask = None ): """ Find stars in an astronomical image. array (( xx ** 2 + yy ** 2 ) xpad ) & ( tbl > ypad ) & ( tbl < xmax ) & ( tbl < ymax )) tbl = tbl if tbl is None : return None xpos, ypos = tbl, tbl if not exclude_border : xpos -= xpad ypos -= ypad return np. arange ( - min_separation, min_separation + 1 ) xx, yy = np. astype ( bool ) else : # define a local circular footprint for the peak finder idx = np. _starfind: """ # define a local footprint for the peak finder if min_separation = 0 : # daofind if isinstance ( kernel, np. Returns - result : Nx2 `~numpy.ndarray` A Nx2 array containing the (x, y) pixel coordinates. The default is `False`, which is the mode used by IRAF's `DAOFIND`_ and `starfind`_ tasks. exclude_border : bool, optional Set to `True` to exclude sources found within half the size of the convolution kernel from the image borders.
min_separation : float, optional The minimum separation for detected objects in pixels. This threshold should be the threshold input to the star finder class multiplied by the kernel relerr. threshold : float The absolute image value above which to select sources. kernel : `_StarFinderKernel` The convolution kernel. Parameters - convolved_data : 2D array_like The convolved 2D array. find_stars ( data, mask = mask ) def _find_stars ( convolved_data, kernel, threshold, *, min_separation = 0.0, mask = None, exclude_border = False ): """ Find stars in an image. Def _call_ ( self, data, mask = None ): return self.
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