gwcs_blot

stcal.outlier_detection.utils.gwcs_blot(median_data, median_wcs, blot_shape, blot_wcs, pix_ratio=None, fillval=0.0, pixmap_stepsize=1, pixmap_order=1)[source]

Resample the median data to recreate an input image based on the blot wcs.

Parameters:

  • median_data (numpy.ndarray) – The data to blot.

  • median_wcs (gwcs.wcs.WCS) – The wcs for the median data.

  • blot_shape (tuple of int) – The target blot data shape.

  • blot_wcs (gwcs.wcs.WCS) – The target/blotted wcs.

  • fillval (float, optional) – Fill value for missing data.

  • pixmap_stepsize (int, optional) – If pixmap_stepsize>1, perform the full WCS calculation on a sparser grid and use interpolation to fill in the rest of the pixels. This option speeds up pixel map computation by reducing the number of WCS calls, though at the cost of reduced pixel map accuracy. The loss of accuracy is typically negligible if the underlying distortion correction is smooth, but if the distortion is non-smooth, pixmap_stepsize>1 is not recommended. Large pixmap_stepsize values are automatically reduced to no more than 1/10 of image size. Passed to stcal.resample.utils.calc_pixmap. Default 1.

  • pixmap_order (int, optional) – Order of the 2D spline to interpolate the sparse pixel mapping if pixmap_stepsize>1. Supported values are: 1 (bilinear) or 3 (bicubic). This Parameter is ignored when pixmap_stepsize <= 1. Default 1.

Returns:

  • blotted (numpy.ndarray) – The blotted median data.

  • blot_img (datamodel) – Datamodel containing header and WCS to define the ‘blotted’ image