jwst.resample.ResampleStep, jwst.resample.ResampleSpecStep


resample, resample_spec

This routine will resample each input 2D image based on the WCS and distortion information, and will combine multiple resampled images into a single undistorted product. The distortion information should have been incorporated into the image using the assign_wcs step.

The resample step can take as input either:

  1. a single 2D input image

  2. an association table (in json format)

The defined parameters for the drizzle operation itself get provided by the DRIZPARS reference file (from CRDS). The exact values used depends on the number of input images being combined and the filter being used. Other information may be added as selection criteria later, but for now, only basic information is used.

The output product gets defined using the WCS information of all inputs, even if it is just a single input image. The output WCS defines a field-of-view that encompasses the undistorted footprints on the sky of all the input images with the same orientation and plate scale as the first listed input image.

This step uses the interface to the C-based cdriz routine to do the resampling via the drizzle method. The input-to-output pixel mapping is determined via a mapping function derived from the WCS of each input image and the WCS of the defined output product. This mapping function gets passed to cdriz to drive the actual drizzling to create the output product.

Context Image

In addition to image data, resample step also creates a “context image” stored in the con attribute in the output data model or 'CON' extension of the FITS file. Each pixel in the context image is a bit field that encodes information about which input image has contributed to the corresponding pixel in the resampled data array. Context image uses 32 bit integers to encode this information and hence it can keep track of only 32 input images. First bit corresponds to the first input image, second bit corrsponds to the second input image, and so on. If the number of input images is larger than 32, then it is necessary to have multiple context images (“planes”) to hold information about all input images with the first plane encoding which of the first 32 images contributed to the output data pixel, second plane representing next 32 input images (number 33-64), etc. For this reason, context array is a 3D array of the type numpy.int32 and shape (np, ny, nx) where nx and ny are dimensions of image’s data. np is the number of “planes” equal to (number of input images - 1) // 32 + 1. If a bit at position k in a pixel with coordinates (p, y, x) is 0 then input image number 32 * p + k (0-indexed) did not contribute to the output data pixel with array coordinates (y, x) and if that bit is 1 then input image number 32 * p + k did contribute to the pixel (y, x) in the resampled image.

As an example, let’s assume we have 8 input images. Then, when 'CON' pixel values are displayed using binary representation (and decimal in parenthesis), one could see values like this:

00000001 (1) - only first input image contributed to this output pixel;
00000010 (2) - 2nd input image contributed;
00000100 (4) - 3rd input image contributed;
10000000 (128) - 8th input image contributed;
10000100 (132=128+4) - 3rd and 8th input images contributed;
11001101 (205=1+4+8+64+128) - input images 1, 3, 4, 7, 8 have contributed
to this output pixel.

In order to test if a specific input image contributed to an output pixel, one needs to use bitwise operations. Using the example above, to test whether input images number 4 and 5 have contributed to the output pixel whose corresponding 'CON' value is 205 (11001101 in binary form) we can do the following:

>>> bool(205 & (1 << (5 - 1)))  # (205 & 16) = 0 (== 0 => False): did NOT contribute
>>> bool(205 & (1 << (4 - 1)))  # (205 & 8) = 8 (!= 0 => True): did contribute

In general, to get a list of all input images that have contributed to an output resampled pixel with image coordinates (x, y), and given a context array con, one can do something like this:

>>> import numpy as np
>>> np.flatnonzero([v & (1 << k) for v in con[:, y, x] for k in range(32)])

For convenience, this functionality was implemented in the decode_context() function.

Spectroscopic Data

Use the resample_spec step for spectroscopic data. The dispersion direction is needed for this case, and this is obtained from the DISPAXIS keyword. For the NIRSpec Fixed Slit mode, the resample_spec step will be skipped if the input is a rateints product, as 3D input for the mode is not supported.


A full description of the drizzling algorithm can be found in Fruchter and Hook, PASP 2002. A description of the inverse variance map method can be found in Casertano et al., AJ 2000, see Appendix A2. A description of the drizzle parameters and other useful drizzle-related resources can be found at DrizzlePac Handbook.