Coordinate systems in astronomy can be complicated, AstroPhot is no different. Here we explain how coordinate systems are handled to help you avoid possible pitfalls.


There are three main coordinate systems to think about.

  1. world coordinates are the classic (RA, DEC) that many astronomical sources are represented in. These should always be used in degree units as far as AstroPhot is concerned.

  2. plane coordinates are the tangent plane on which AstroPhot performs its calculations. Working on a plane makes everything linear and does not introduce a noticible effect for small enough images. In the tangent plane everything should be represented in arcsecond units.

  3. pixel coordinates are specific to each image, they start at (0,0) in the center of the [0,0] indexed pixel. These are effectively unitless, a step of 1 in pixel coordinates is the same as changing an index by 1. Though image array indexing is flipped so pixel coordinate (3,10) represents the center of the index [10,3] pixel. It is a convention for most images that the first axis indexes vertically and the second axis indexis horizontally, if this is not the case for oyur images you can apply a transpose before passing the data to AstroPhot. Also, in the pixel coordinate system the values are represented by floating point numbers and so (1.3,2.8) is a valid pixel coordinate that is just partway between pixel centers.

Tranformations exist in AstroPhot for converting world to/from plane and for converting plane to/from pixel. The best way to interface with these is to use the image.window.world_to_plane for any AstroPhot image object (you may similarly swap world, plane, and pixel).

One gotcha to keep in mind with regards to world_to_plane and plane_to_world is that AstroPhot needs to know the reference (RA_0, DEC_0) where the tangent plane meets with the celestial sphere. You can set this by including reference_radec = (RA_0, DEC_0) as an argument in an image you create. If a reference is not given, then one will be assumed based on available information. Note that if you are doing simultaneous multi-image analysis you should ensure that the reference_radec is same for all images!

Projection Systems

AstroPhot currently implements three coordinate reference systems: Gnomonic, Orthographic, and Steriographic. The default projection is the Gnomonic, which represents the perspective of an observer at the center of a sphere projected onto a plane. For the exact implementation by AstroPhot see the Wolfram MathWorld page.

On small scales the choice of projection doesn’t matter. For very large images the effect may be detectable, though it is likely insignificant compared to other effects in an image. Just like the reference_radec you can choose your projection system in an image you construct by passing projection = 'gnomonic' as an argument. Just like with the reference coordinate, for images to “talk” to each other they should have the same projection.

If you really want to change the projection after an image has been created (warning, this may cause serious missalignments between images), you can force it to update with:

image.window.projection = 'steriographic'

which would change the projection to steriographic. The image won’t recompute its position in the new projection system, it will just use new equations going forward. Hence the potential to seriously mess up your image alignmnt if this is done after some calculations have already been performed.

Talking to the world

If you have images with WCS information then you will want to use this to map images onto the same tangent plane. Often this will take the form of information in a FITS file, which can easily be accessed using Astropy like:

from import fits
from astropy.wcs import WCS
hdu ="myimage.fits")
data = hdu[0].data
wcs = WCS(hdu[0].header)

That is somewhat described in the basics section, however there are some more features you can take advantage of. When creating an image in AstroPhot, you need to tell it some basic properties so that the image knows how to place itself in the tangent plane. Using the Astropy WCS object above you can recover the reference coordinates of the image in (RA, DEC), for an example Astropy wcs object you could accomplish this with:

ra, dec = wcs.wcs.crval

meaning that you know the world position of the reference RA, Dec of the image WCS. To have AstroPhot place the image at the right location in the tangent plane you can use the wcs argument when constructing the image:

image = ap.image.Target_Image(
    data = data,
    reference_radec = (ra, dec),
    wcs = wcs,

AstroPhot will set the reference RA, DEC to these coordinates and also set the image in the correct position. A more explicit alternative is to just say what the reference coordinate should be. That would look something like:

image = ap.image.Target_Image(
    data = data,
    pixelscale = pixelscale,
    reference_radec = (ra,dec),
    reference_imagexy = (x, y),

which uniquely defines the position of the image in the coordinate system. Remember that the reference_radec should be the same for all images in a multi-image analysis, while reference_imagexy specifies the position of a particular image. Another similar option is to set center_radec like:

image = ap.image.Target_Image(
    data = data,
    pixelscale = pixelscale,
    reference_radec = (ra,dec),
    center_radec = (c_ra, c_dec),

You may also have a catalogue of objects that you would like to project into the image. The easiest way to do this if you already have an image object is to call the world_to_plane functions manually. Say for example that you know the object position as an Astropy SkyCoord object, and you want to use this to set the center position of a sersic model. That would look like:

model = ap.models.AstroPhot_Model(
    name = "knowloc",
    model_type = "sersic galaxy model",
    target = image,
    parameters = {
        "center": image.window.world_to_plane(obj_pos.ra.deg, obj_pos.dec.deg),

Which will start the object at the correct position in the image given its world coordinates. As you can see, the center and in fact all parameters for AstroPhot models are defined in the tangent plane. This means that if you have optimized a model and you would like to present its position in world coordinates that can be compared with other sources, you will need to do the opposite operation:

world_position = image.window.plane_to_world(model["center"].value)

That should assign world_position the coordinates in RA and DEC (degrees), assuming that you initialized the image with a WCS or by other means ensured that the world coordinates being used are correct. If you never gave AstroPhot the information it needs, then it likely assumed a reference position of (0,0) in the world coordinate system.

Coordinate reference points

As stated earlier, there are essentially three coordinate systems in AstroPhot: world, plane, and pixel. To uniquely specify the transformation from world to plane AstroPhot keeps track of two vectors: reference_radec and reference_planexy. These variables are stored in all Image_Header objects and essentially pin down the mapping such that one coordinate will get mapped to the other. All other coordinates follow from the projection system assumed (i.e., Gnomonic). It is possible to specify these variables directly when constructing an image, or implicitly if you give some other relevant information (e.g., an Astropy WCS). AstroPhot Window objects also keep track of two more vectors: reference_imageij and reference_imagexy. These variables control where an image is placed in the tangent plane and represent a fixed point between the pixel coordinates and the tangent plane coordinates. If your pixel scale matrix includes a rotation then the rotation will be performed about this position.

All together, these reference positions define how pixels are mapped in AstroPhot. This level of generality is overkill for analyzing a single image, so AstroPhot makes reasonable assumptions about these reference points if you don’t specify them all. This makes it easy to do single image analysis without thinking too much about the coordinate systems. However, for multi-band or multi-epoch imaging it is critical to be absolutely clear about these coordinate transformations so that images can be aligned properly on the sky. As an intuitive explanation, think of reference_radec and reference_planexy as defining the coordinate system that is shared between images, while reference_imageij and reference_imagexy specify where a single image is located. As such, in multi-image analysis if you wish to use world coordinates, you should explitcitly pass the same reference_radec and reference_planexy to every image so that the same coordinate system is defined for all of them (the same tangent plane at the same point on the celestial sphere). If you aren’t going to interact with world coordinates, you can ignore those reference points entirely and it won’t affect your images.

Below is a summary of the reference coordinates and their meaning:

  1. reference_radec world coordinates on the celestial sphere (RA, DEC in degrees) where the tangent plane makes contact. This should be the same for every image in a multi-image analysis.

  2. reference_planexy tangent plane coordinates (arcsec) where it makes contact with the celesial sphere. This should typically be (0,0) though that is not stricktly enforced (it is assumed if not given). This reference coordinate should be the same for all images in a multi-image analysis.

  3. reference_imageij pixel coordinates about which the image is defined. For example in an Astropy WCS object the wcs.wcs.crpix array gives the pixel coordinate reference point for which the world coordinate mapping (wcs.wcs.crval) is defined. One may think of the referenced pixel location as being “pinned” to the tangent plane. This may be different for each image in a multi-image analysis.

  4. reference_imagexy tangent plane coordinates (arcsec) about which the image is defined. This is the pivot point about which the pixelscale matrix operates, therefore if the pixelscale matrix defines a rotation then this is the coordinate about which the rotation will be performed. This may be different for each image in a multi-image analysis.