A simple usage example

Suppose we have two images of about the same portion of the sky, and we would like to transform one of them to fit on top of the other one. Suppose we do not have WCS information, but we are confident that we could do it by eye, by matching some obvious asterisms on the two images.

In this particular use case, astroalign can be of great help to automatize the process.

After we load our images into numpy arrays, we simple choose one to be the source image and the other to be the target.

The usage for this simple most common case would be as follows:

>>> import astroalign as aa
>>> registered_image = aa.register(source, target)

registered_image is now a transformed (numpy array) image of source that will match pixel to pixel to target.

If source is a masked array, registered_image will have a mask transformed like source with pixels outside the boundary masked with True (read more in Working with masks).

Finding the transformation

In some cases it may be necessary to inspect first the transformation parameters before applying it, or we may be interested only in a star to star correspondence between the images. For those cases, we can use find_transform.

find_transform will return a scikit-image SimilarityTransform object that encapsulates the matrix transformation, and the transformation parameters. It will also return a tuple with two lists of star positions of source and its corresponding ordered star postions on the target image.:

>>> transf, (source_list, target_list) = aa.find_transform(source, target)

source and target here can be either numpy arrays of the image pixels, or any iterable (x, y) pair, corresponding to a star position.

The transformation parameters can be found in transf.rotation, transf.traslation, transf.scale and the transformation matrix in transf.params.

If the transformation is satisfactory we can apply it to the image with apply_transform. Continuing our example:

>>> if transf.rotation > MIN_ROT:
...     registered_image = aa.apply_transform(transf, source, target)

If you know the star-to-star correspondence

As a convenience, estimate_transform from scikit-image is imported to astroalign.

If for any reason you know which star corresponds to what other, you can call estimate_transform.

Let us suppose we know the correspondence:

  • (127.03, 85.98) in source –> (175.13, 111.36) in target
  • (23.11, 31.87) in source –> (0.58, 119.04) in target
  • (98.84, 142.99) in source –> (181.55, 206.49) in target
  • (150.93, 85.02) in source –> (205.60, 91.89) in target
  • (137.99, 12.88) in source –> (134.61, 7.94) in target

Then we can estimate the transform:

>>> src = np.array([(127.03, 85.98), (23.11, 31.87), (98.84, 142.99),
...                 (150.93, 85.02), (137.99, 12.88)])
>>> dst = np.array([(175.13, 111.36), (0.58, 119.04), (181.55, 206.49),
...                 (205.60, 91.89), (134.61, 7.94)])
>>> tform = aa.estimate_transform('affine', src, dst)

And apply it to an image with apply_transform or to a set of points with matrix_transform.

Applying a transformation to a set of points

As a convenience, matrix_transform from scikit-image is imported to astroalign.

To apply a known transform to a set of points, we use matrix_transform. Following the example in the previous section:

>>> dst_calc = aa.matrix_transform(src, tform.params)

dst_calc should be a 5 by 2 array similar to the dst array.

See Module Methods for more information.