Using and Designing Coordinate Frames#

In astropy.coordinates, as outlined in the Overview of astropy.coordinates Concepts, subclasses of BaseCoordinateFrame (“frame classes”) define particular coordinate frames. They can (but do not have to) contain representation objects storing the actual coordinate data. The actual coordinate transformations are defined as functions that transform representations between frame classes. This approach serves to separate high-level user functionality (see Using the SkyCoord High-Level Class) and details of how the coordinates are actually stored (see Using and Designing Coordinate Representations) from the definition of frames and how they are transformed.

Using Frame Objects#

Frames without Data#

Frame objects have two distinct (but related) uses. The first is storing the information needed to uniquely define a frame (e.g., equinox, observation time). This information is stored on the frame objects as (read-only) Python attributes, which are set when the object is first created:

>>> from astropy.coordinates import ICRS, FK5
>>> FK5(equinox='J1975')
<FK5 Frame (equinox=J1975.000)>
>>> ICRS()  # has no attributes
<ICRS Frame>
>>> FK5()  # uses default equinox
<FK5 Frame (equinox=J2000.000)>

The specific names of attributes available for a particular frame are available as the keys of the frame_attributes dictionary:

>>> FK5.frame_attributes.keys()

The defaults of the frame attributes are available as: get_frame_attr_defaults():

>>> FK5.get_frame_attr_defaults()
{'equinox': <Time object: scale='tt' format='jyear_str' value=J2000.000>}

You can access any of the attributes on a frame by using standard Python attribute access. Note that for cases like equinox, which are time inputs, if you pass in any unambiguous time string, it will be converted into an Time object (see Inferring Input Format):

>>> f = FK5(equinox='J1975')
>>> f.equinox
<Time object: scale='tt' format='jyear_str' value=J1975.000>
>>> f = FK5(equinox='2011-05-15T12:13:14')
>>> f.equinox
<Time object: scale='utc' format='isot' value=2011-05-15T12:13:14.000>

Frames with Data#

The second use for frame objects is to store actual realized coordinate data for frames like those described above. In this use, it is similar to the SkyCoord class, and in fact, the SkyCoord class internally uses the frame classes as its implementation. However, the frame classes have fewer “convenience” features, thereby streamlining the implementation of frame classes. As such, they are created similarly to SkyCoord objects. One suggested way is to use with keywords appropriate for the frame (e.g., ra and dec for equatorial systems):

>>> from astropy import units as u
>>> ICRS(ra=1.1*u.deg, dec=2.2*u.deg)  
<ICRS Coordinate: (ra, dec) in deg
    (1.1, 2.2)>
>>> FK5(ra=1.1*u.deg, dec=2.2*u.deg, equinox='J1975')  
<FK5 Coordinate (equinox=J1975.000): (ra, dec) in deg
    (1.1, 2.2)>

These same attributes can be used to access the data in the frames as Angle objects (or Angle subclasses):

>>> coo = ICRS(ra=1.1*u.deg, dec=2.2*u.deg)
>>> coo.ra  
<Longitude 1.1 deg>
>>> coo.ra.value  
<Longitude 0.07333333 hourangle>

You can use the representation_type attribute in conjunction with the representation_component_names attribute to figure out what keywords are accepted by a particular class object. The former will be the representation class in which the system is expressed (e.g., spherical for equatorial frames), and the latter will be a dictionary mapping names for that frame to the attribute name on the representation class:

>>> import astropy.units as u
>>> icrs = ICRS(1*u.deg, 2*u.deg)
>>> icrs.representation_type
<class 'astropy.coordinates...SphericalRepresentation'>
>>> icrs.representation_component_names
{'ra': 'lon', 'dec': 'lat', 'distance': 'distance'}

You can get the data in a different representation if needed:

>>> icrs.represent_as('cartesian')  
<CartesianRepresentation (x, y, z) [dimensionless]
     (0.99923861, 0.01744177, 0.0348995)>

The representation of the coordinate object can also be changed directly, as shown below. This does nothing to the object internal data which stores the coordinate values, but it changes the external view of that data in two ways: (1) the object prints itself in accord with the new representation, and (2) the available attributes change to match those of the new representation (e.g., from ra, dec, distance to x, y, z). Setting the representation_type thus changes a property of the object (how it appears) without changing the intrinsic object itself which represents a point in 3D space.:

>>> from astropy.coordinates import CartesianRepresentation
>>> icrs.representation_type = CartesianRepresentation
>>> icrs  
<ICRS Coordinate: (x, y, z) [dimensionless]
    (0.99923861, 0.01744177, 0.0348995)>
>>> icrs.x  
<Quantity 0.99923861>

The representation can also be set at the time of creating a coordinate and affects the set of keywords used to supply the coordinate data. For example, to create a coordinate with Cartesian data do:

>>> ICRS(x=1*u.kpc, y=2*u.kpc, z=3*u.kpc, representation_type='cartesian')  
<ICRS Coordinate: (x, y, z) in kpc
    (1., 2., 3.)>

For more information about the use of representations in coordinates see the Representations section, and for details about the representations themselves see Using and Designing Coordinate Representations.

There are two other ways to create frame classes with coordinates. A representation class can be passed in directly at creation, along with any frame attributes required:

>>> from astropy.coordinates import SphericalRepresentation
>>> rep = SphericalRepresentation(lon=1.1*u.deg, lat=2.2*u.deg, distance=3.3*u.kpc)
>>> FK5(rep, equinox='J1975')  
<FK5 Coordinate (equinox=J1975.000): (ra, dec, distance) in (deg, deg, kpc)
    (1.1, 2.2, 3.3)>

A final way is to create a frame object from an already existing frame (either one with or without data), using the realize_frame method. This will yield a frame with the same attributes, but new data:

>>> f1 = FK5(equinox='J1975')
>>> f1
<FK5 Frame (equinox=J1975.000)>
>>> rep = SphericalRepresentation(lon=1.1*u.deg, lat=2.2*u.deg, distance=3.3*u.kpc)
>>> f1.realize_frame(rep)  
<FK5 Coordinate (equinox=J1975.000): (ra, dec, distance) in (deg, deg, kpc)
    (1.1, 2.2, 3.3)>

You can check if a frame object has data using the has_data attribute, and if it is present, it can be accessed from the data attribute:

>>> ICRS().has_data
>>> cooi = ICRS(ra=1.1*u.deg, dec=2.2*u.deg)
>>> cooi.has_data
<UnitSphericalRepresentation (lon, lat) in deg
    (1.1, 2.2)>

All of the above methods can also accept array data (in the form of class:Quantity, or other Python sequences) to create arrays of coordinates:

>>> ICRS(ra=[1.5, 2.5]*u.deg, dec=[3.5, 4.5]*u.deg)  
<ICRS Coordinate: (ra, dec) in deg
    [(1.5, 3.5), (2.5, 4.5)]>

If you pass in mixed arrays and scalars, the arrays will be broadcast over the scalars appropriately:

>>> ICRS(ra=[1.5, 2.5]*u.deg, dec=[3.5, 4.5]*u.deg, distance=5*u.kpc)  
<ICRS Coordinate: (ra, dec, distance) in (deg, deg, kpc)
    [(1.5, 3.5, 5.), (2.5, 4.5, 5.)]>

Similar broadcasting happens if you transform to another frame. For example:

>>> import numpy as np
>>> from astropy.coordinates import EarthLocation, AltAz
>>> coo = ICRS(ra=180.*u.deg, dec=51.477811*u.deg)
>>> lf = AltAz(location=EarthLocation.of_site('greenwich'),
...            obstime=['2012-03-21T00:00:00', '2012-06-21T00:00:00'])
>>> lcoo = coo.transform_to(lf)  # this can load finals2000A.all 
>>> lcoo  
<AltAz Coordinate (obstime=['2012-03-21T00:00:00.000' '2012-06-21T00:00:00.000'], location=(3980608.9024681724, -102.47522910648239, 4966861.273100675) m, pressure=0.0 hPa, temperature=0.0 deg_C, relative_humidity=0.0, obswl=1.0 micron): (az, alt) in deg
    [( 94.71264993, 89.21424259), (307.69488825, 37.98077772)]>

Above, the shapes — () for coo and (2,) for lf — were broadcast against each other. If you wish to determine the positions for a set of coordinates, you will need to make sure that the shapes allow this:

>>> coo2 = ICRS(ra=[180., 225., 270.]*u.deg, dec=[51.5, 0., 51.5]*u.deg)
>>> coo2.transform_to(lf)
Traceback (most recent call last):
ValueError: operands could not be broadcast together...
>>> coo2.shape
>>> lf.shape
>>> lf2 = lf[:, np.newaxis]
>>> lf2.shape
(2, 1)
>>> coo2.transform_to(lf2)  
<AltAz Coordinate (obstime=[['2012-03-21T00:00:00.000' '2012-03-21T00:00:00.000'
 ['2012-06-21T00:00:00.000' '2012-06-21T00:00:00.000'
  '2012-06-21T00:00:00.000']], location=(3980608.90246817, -102.47522911, 4966861.27310068) m, pressure=0.0 hPa, temperature=0.0 deg_C, relative_humidity=0.0, obswl=1.0 micron): (az, alt) in deg
    [[( 93.09845155, 89.21613119), (126.85789646, 25.46600543),
      ( 51.37993224, 37.1853252 )],
     [(307.71713691, 37.99437664), (231.37407858, 26.36768334),
      ( 85.42187562, 89.6929799 )]]>


Frames without data have a shape that is determined by their frame attributes. For frames with data, the shape always is that of the data; any non-scalar attributes are broadcast to have matching shapes (as can be seen for obstime in the last line above).

Coordinate values in a array-valued frame object can be modified in-place (added in astropy 4.1). This requires that the new values be set from an another frame object that is equivalent in all ways except for the actual coordinate data values. In this way, no frame transformations are required and the item setting operation is extremely robust.

To modify an array of coordinates use the same syntax for a numpy array:

>>> coo1 = ICRS([1, 2] * u.deg, [3, 4] * u.deg)
>>> coo2 = ICRS(10 * u.deg, 20 * u.deg)
>>> coo1[0] = coo2
>>> coo1
<ICRS Coordinate: (ra, dec) in deg
    [(10., 20.), ( 2.,  4.)]>

This method is relatively slow because it requires setting from an existing frame object and it performs extensive validation to ensure that the operation is valid. For some applications it may be necessary to take a different lower-level approach which is described in the section Fast In-Place Modification of Coordinates.


You may be tempted to try an apparently obvious way of modifying a frame object in place by updating the component attributes directly, for example coo1.ra[1] = 40 * u.deg. However, while this will appear to give a correct result it does not actually modify the underlying representation data. This is related to the current implementation of performance-based caching. The current cache implementation is similarly unable to handle in-place changes to the representation (.data) or frame attributes such as .obstime.

Transforming between Frames#

To transform a frame object with data into another frame, use the transform_to method of an object, and provide it the frame you wish to transform to. This frame should be a frame object (with or without coordinate data). If you wish to use all default frame attributes, you can instantiate the frame class with no arguments (i.e., empty parentheses):

>>> cooi = ICRS(1.5*u.deg, 2.5*u.deg)
>>> cooi.transform_to(FK5())  
<FK5 Coordinate (equinox=J2000.000): (ra, dec) in deg
    (1.50000661, 2.50000238)>
>>> cooi.transform_to(FK5(equinox='J1975'))  
<FK5 Coordinate (equinox=J1975.000): (ra, dec) in deg
    (1.17960348, 2.36085321)>

The Reference/API includes a list of all of the frames built into astropy.coordinates, as well as the defined transformations between them. Any transformation that has a valid path, even if it passes through other frames, can be transformed too. To programmatically check for or manipulate transformations, see the TransformGraph documentation.

Defining a New Frame#

Implementing a new frame class that connects to the astropy.coordinates infrastructure can be done by subclassing BaseCoordinateFrame. Some guidance and examples are given below, but detailed instructions for creating new frames are given in the docstring of BaseCoordinateFrame.

All frame classes must specify a default representation for the coordinate positions by, at minimum, defining a default_representation class attribute (see Using and Designing Coordinate Representations for more information about the supported Representation objects).


To create a new frame that, by default, expects to receive its coordinate data in spherical coordinates, we would create a subclass as follows:

>>> from astropy.coordinates import BaseCoordinateFrame
>>> import astropy.coordinates.representation as r
>>> class MyFrame1(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation

Already, this is a valid frame class:

>>> fr = MyFrame1(1*u.deg, 2*u.deg)
>>> fr 
<MyFrame1 Coordinate: (lon, lat) in deg
    (1., 2.)>
>>> fr.lon 
<Longitude 1. deg>

However, as we have defined it above, (1) the coordinate component names will be the same as used in the specified default_representation (in this case, lon, lat, and distance for longitude, latitude, and distance, respectively), (2) this frame does not have any additional attributes or metadata, (3) this frame does not support transformations to any other coordinate frame, and (4) this frame does not support velocity data. We can address each of these points by seeing some other ways of customizing frame subclasses.

Customizing Frame Component Names#

First, as mentioned in the point (1) above, some frame classes have special names for their components. For example, the ICRS frame and other equatorial frame classes often use “Right Ascension” or “RA” in place of longitude, and “Declination” or “Dec.” in place of latitude. These component name overrides, which change the frame component name defaults taken from the Representation classes, are defined by specifying a set of RepresentationMapping instances (one per component) as a part of defining an additional class attribute on a frame class: frame_specific_representation_info. This attribute must be a dictionary, and the keys should be either Representation or Differential classes (see below for a discussion about customizing behavior for velocity components, which is done with the Differential classes). Using our example frame implemented above, we can customize it to use the names “R” and “D” instead of “lon” and “lat”:

>>> from astropy.coordinates import RepresentationMapping
>>> class MyFrame2(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation
...     # Override component names (e.g., "ra" instead of "lon")
...     frame_specific_representation_info = {
...         r.SphericalRepresentation: [RepresentationMapping('lon', 'R'),
...                                     RepresentationMapping('lat', 'D')]
...     }

With this frame, we can now use the names R and D to access the frame data:

>>> fr = MyFrame2(3*u.deg, 4*u.deg)
>>> fr 
<MyFrame2 Coordinate: (R, D) in deg
    (3., 4.)>
>>> fr.R 
<Longitude 3. deg>

We can specify name mappings for any Representation class in astropy.coordinates to change the default component names. For example, the Galactic frame uses the standard longitude and latitude names “l” and “b” when used with a SphericalRepresentation, but uses the component names “x”, “y”, and “z” when the representation is changed to a CartesianRepresentation. With our example above, we could add an additional set of mappings to override the Cartesian component names to be “a”, “b”, and “c” instead of the default “x”, “y”, and “z”:

>>> class MyFrame3(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation
...     # Override component names (e.g., "ra" instead of "lon")
...     frame_specific_representation_info = {
...         r.SphericalRepresentation: [RepresentationMapping('lon', 'R'),
...                                     RepresentationMapping('lat', 'D')],
...         r.CartesianRepresentation: [RepresentationMapping('x', 'a'),
...                                     RepresentationMapping('y', 'b'),
...                                     RepresentationMapping('z', 'c')]
...     }

For any RepresentationMapping, you can also specify a default unit for the component by setting the defaultunit keyword argument.

Defining Frame Attributes#

Second, as indicated by the point (2) in the introduction above, it is often useful for coordinate frames to allow specifying frame “attributes” that may specify additional data or parameters needed in order to fully specify transformations between a given frame and some other frame. For example, the FK5 frame allows specifying an equinox that helps define the transformation between FK5 and the ICRS frame. Frame attributes are defined by creating class attributes that are instances of Attribute or its subclasses (e.g., TimeAttribute, QuantityAttribute, etc.). If attributes are defined using these classes, there is often no need to define an __init__ function, as the initializer in BaseCoordinateFrame will probably behave in the way you want. Let us now modify the above toy frame class implementation to add two frame attributes:

>>> from astropy.coordinates import TimeAttribute, QuantityAttribute
>>> class MyFrame4(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation
...     # Override component names (e.g., "ra" instead of "lon")
...     frame_specific_representation_info = {
...         r.SphericalRepresentation: [RepresentationMapping('lon', 'R'),
...                                     RepresentationMapping('lat', 'D')],
...         r.CartesianRepresentation: [RepresentationMapping('x', 'a'),
...                                     RepresentationMapping('y', 'b'),
...                                     RepresentationMapping('z', 'c')]
...     }
...     # Specify frame attributes required to fully specify the frame
...     time = TimeAttribute(default='B1950')
...     orientation = QuantityAttribute(default=42*u.deg)

Without specifying an initializer, defining these attributes tells the BaseCoordinateFrame what to expect in terms of additional arguments passed in to our subclass initializer. For example, when defining a frame instance with our subclass, we can now optionally specify values for these attributes:

>>> fr = MyFrame4(R=1*u.deg, D=2*u.deg, orientation=21*u.deg)
>>> fr 
<MyFrame4 Coordinate (time=B1950.000, orientation=21.0 deg): (R, D) in deg
    (1., 2.)>

Note that we specified both frame attributes with default values, so they are optional arguments to the frame initializer. Note also that the frame attributes now appear in the repr of the frame instance above. As a bonus, for most of the Attribute subclasses, even without defining an initializer, attributes specified as arguments will be validated. For example, arguments passed in to QuantityAttribute attributes will be checked that they have valid and compatible units with the expected attribute units. Using our frame example above, which expects an orientation with angular units, passing in a time results in an error:

>>> MyFrame4(R=1*u.deg, D=2*u.deg, orientation=55*u.microyear) 
Traceback (most recent call last):
UnitConversionError: 'uyr' (time) and 'deg' (angle) are not convertible

When defining frame attributes, you do not always have to specify a default value as long as the Attribute subclass is able to validate the input. For example, with the above frame, if the orientation does not require a default value but we still want to enforce it to have angular units, we could instead define it as:

orientation = QuantityAttribute(unit=u.deg)

In the above case, if orientation is not specified when a new frame instance is created, its value will be None: Note that it is up to the frame classes and transformation function implementations to define how to handle a None value. In most cases None should signify a special case like “use a different frame attribute for this value” or similar.

Customizing Display of Attributes#

While the default repr for coordinate frames is suitable for most cases, you may want to customize how frame attributes are displayed in certain cases. To do this you can define a method named _astropy_repr_in_frame. This method should be defined on the object that is set to the frame attribute itself, not the Attribute descriptor.


As an example of method _astropy_repr_in_frame, say you have an object Spam which you have as an attribute of your frame:

>>> class Spam:
...     def _astropy_repr_in_frame(self):
...         return "<A can of Spam>"

If your frame has this class as an attribute:

>>> from astropy.coordinates import Attribute
>>> class Egg(BaseCoordinateFrame):
...     can = Attribute(default=Spam())

When it is displayed by the frame it will use the result of _astropy_repr_in_frame:

>>> Egg()
<Egg Frame (can=<A can of Spam>)>

Defining Transformations between Frames#

As indicated by the point (3) in the introduction above, a frame class on its own is likely not very useful until transformations are defined between it and other coordinate frame classes. The key concept for defining transformations in astropy.coordinates is the “frame transform graph” (in the “graph theory” sense, not “plot”), which stores all of the transformations between the built-in frames, as well as tools for finding the shortest paths through this graph to transform from any frame to any other by composing the transformations. The power behind this concept is available to user-created frames as well, meaning that once you define even one transform from your frame to any frame in the graph, coordinates defined in your frame can be transformed to any other frame in the graph. The “frame transform graph” is available in code as astropy.coordinates.frame_transform_graph, which is an instance of the TransformGraph class.

The transformations themselves are represented as CoordinateTransform objects or their subclasses. The useful subclasses/types of transformations are:

  • FunctionTransform

    A transform that is defined as a function that takes a frame object of one frame class and returns an object of another class.

  • AffineTransform

    A transformation that includes a linear matrix operation and a translation (vector offset). These transformations are defined by a 3x3 matrix and a 3-vector for the offset (supplied as a Cartesian representation). The transformation is applied to the Cartesian representation of one frame and transforms into the Cartesian representation of the target frame.

  • StaticMatrixTransform

  • DynamicMatrixTransform

    The matrix transforms are AffineTransform transformations without a translation (i.e., only a rotation). The static version is for the case where the matrix is independent of the frame attributes (e.g., the ICRS->FK5 transformation, because ICRS has no frame attributes). The dynamic case is for transformations where the transformation matrix depends on the frame attributes of either the to or from frame.

Generally, it is not necessary to use these classes directly. Instead, use methods on the frame_transform_graph that can be used as function decorators. Define functions that either do the actual transformation (for FunctionTransform), or that compute the necessary transformation matrices to transform. Then decorate the functions to register these transformations with the frame transform graph:

from astropy.coordinates import frame_transform_graph

@frame_transform_graph.transform(DynamicMatrixTransform, ICRS, FK5)
def icrs_to_fk5(icrscoord, fk5frame):

@frame_transform_graph.transform(DynamicMatrixTransform, FK5, ICRS)
def fk5_to_icrs(fk5coord, icrsframe):

If the transformation to your coordinate frame of interest is not representable by a matrix operation, you can also specify a function to do the actual transformation, and pass the FunctionTransform class to the transform graph decorator instead:

@frame_transform_graph.transform(FunctionTransform, FK4NoETerms, FK4)
def fk4_no_e_to_fk4(fk4noecoord, fk4frame):

Furthermore, the frame_transform_graph does some caching and optimization to speed up transformations after the first attempt to go from one frame to another, and shortcuts steps where relevant (for example, combining multiple static matrix transforms into a single matrix). Hence, in general, it is better to define whatever are the most natural transformations for a user-defined frame, rather than worrying about optimizing or caching a transformation to speed up the process.

For a demonstration of how to define transformation functions that also work for transforming velocity components, see Transforming Frames with Velocities.

Supporting Velocity Data in Frames#

As alluded to by point (4) in the introduction above, the examples we have seen above mostly deal with customizing frame behavior for positional information. (For some context about how velocities are handled in astropy.coordinates, it may be useful to read the overview: Creating Frame Objects with Velocity Data.)

When defining a frame class, it is also possible to set a default_differential (analogous to default_representation), and to customize how velocity data components are named. Expanding on our custom frame example above, we can use RepresentationMapping to override Differential component names. The default Differential components are typically named after the corresponding Representation component, preceded by d_. So, for example, the longitude Differential component is, by default, d_lon. However, there are some defaults to be aware of. Here, if we set the default Differential class to also be Spherical, it will implement a set of default “nicer” names for the velocity components, mapping pm_R to d_lon, pm_D to d_lat, and radial_velocity to d_distance (taking the previously overridden longitude and latitude component names):

>>> class MyFrame4WithVelocity(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation
...     default_differential = r.SphericalDifferential
...     # Override component names (e.g., "ra" instead of "lon")
...     frame_specific_representation_info = {
...         r.SphericalRepresentation: [RepresentationMapping('lon', 'R'),
...                                     RepresentationMapping('lat', 'D')],
...         r.CartesianRepresentation: [RepresentationMapping('x', 'a'),
...                                     RepresentationMapping('y', 'b'),
...                                     RepresentationMapping('z', 'c')]
...     }
>>> fr = MyFrame4WithVelocity(R=1*u.deg, D=2*u.deg,
...                           pm_R=3*u.mas/u.yr, pm_D=4*u.mas/u.yr)
>>> fr 
<MyFrame4WithVelocity Coordinate: (R, D) in deg
    (1., 2.)
(pm_R, pm_D) in mas / yr
    (3., 4.)>

If you want to override the default “nicer” names, you can specify a new key in the frame_specific_representation_info for any of the Differential classes, for example:

>>> class MyFrame4WithVelocity2(BaseCoordinateFrame):
...     # Specify how coordinate values are represented when outputted
...     default_representation = r.SphericalRepresentation
...     default_differential = r.SphericalDifferential
...     # Override component names (e.g., "ra" instead of "lon")
...     frame_specific_representation_info = {
...         r.SphericalRepresentation: [RepresentationMapping('lon', 'R'),
...                                     RepresentationMapping('lat', 'D')],
...         r.CartesianRepresentation: [RepresentationMapping('x', 'a'),
...                                     RepresentationMapping('y', 'b'),
...                                     RepresentationMapping('z', 'c')],
...         r.SphericalDifferential: [RepresentationMapping('d_lon', 'pm1'),
...                                   RepresentationMapping('d_lat', 'pm2'),
...                                   RepresentationMapping('d_distance', 'rv')]
...     }
>>> fr = MyFrame4WithVelocity2(R=1*u.deg, D=2*u.deg,
...                           pm1=3*u.mas/u.yr, pm2=4*u.mas/u.yr)
>>> fr 
<MyFrame4WithVelocity2 Coordinate: (R, D) in deg
    (1., 2.)
(pm1, pm2) in mas / yr
    (3., 4.)>

Final Notes#

You can also define arbitrary methods for any added functionality you want your frame to have that is unique to that frame. These methods will be available in any SkyCoord that is created using your user-defined frame.

For examples of defining frame classes, the first place to look is at the source code for the frames that are included in astropy (available at astropy.coordinates.builtin_frames). These are not special-cased, but rather use all of the same API and features available to user-created frames.