# CartesianRepresentation¶

class astropy.coordinates.CartesianRepresentation(x, y=None, z=None, unit=None, xyz_axis=None, differentials=None, copy=True)[source] [edit on github]

Representation of points in 3D cartesian coordinates.

Parameters: x, y, z : Quantity or array The x, y, and z coordinates of the point(s). If x, y, and z have different shapes, they should be broadcastable. If not quantity, unit should be set. If only x is given, it is assumed that it contains an array with the 3 coordinates stored along xyz_axis. unit : Unit or str If given, the coordinates will be converted to this unit (or taken to be in this unit if not given. xyz_axis : int, optional The axis along which the coordinates are stored when a single array is provided rather than distinct x, y, and z (default: 0). differentials : dict, CartesianDifferential, optional Any differential classes that should be associated with this representation. The input must either be a single CartesianDifferential instance, or a dictionary of CartesianDifferential s with keys set to a string representation of the SI unit with which the differential (derivative) is taken. For example, for a velocity differential on a positional representation, the key would be 's' for seconds, indicating that the derivative is a time derivative. copy : bool, optional If True (default), arrays will be copied rather than referenced.

Attributes Summary

 attr_classes x The ‘x’ component of the points(s). xyz Return a vector array of the x, y, and z coordinates. y The ‘y’ component of the points(s). z The ‘z’ component of the points(s).

Methods Summary

 cross(other) Cross product of two representations. dot(other) Dot product of two representations. from_cartesian(other) get_xyz([xyz_axis]) Return a vector array of the x, y, and z coordinates. mean(*args, **kwargs) Vector mean. norm() Vector norm. scale_factors() Scale factors for each component’s direction. sum(*args, **kwargs) Vector sum. to_cartesian() Convert the representation to its Cartesian form. transform(matrix) Transform the cartesian coordinates using a 3x3 matrix. unit_vectors() Cartesian unit vectors in the direction of each component.

Attributes Documentation

attr_classes = {'x': <class 'astropy.units.quantity.Quantity'>, 'y': <class 'astropy.units.quantity.Quantity'>, 'z': <class 'astropy.units.quantity.Quantity'>}
x

The ‘x’ component of the points(s).

xyz

Return a vector array of the x, y, and z coordinates.

Parameters: xyz_axis : int, optional The axis in the final array along which the x, y, z components should be stored (default: 0). xyz : Quantity With dimension 3 along xyz_axis. Note that, if possible, this will be a view.
y

The ‘y’ component of the points(s).

z

The ‘z’ component of the points(s).

Methods Documentation

cross(other)[source] [edit on github]

Cross product of two representations.

Parameters: other : representation If not already cartesian, it is converted. cross_product : CartesianRepresentation With vectors perpendicular to both self and other.
dot(other)[source] [edit on github]

Dot product of two representations.

Note that any associated differentials will be dropped during this operation.

Parameters: other : representation If not already cartesian, it is converted. dot_product : Quantity The sum of the product of the x, y, and z components of self and other.
classmethod from_cartesian(other)[source] [edit on github]
get_xyz(xyz_axis=0)[source] [edit on github]

Return a vector array of the x, y, and z coordinates.

Parameters: xyz_axis : int, optional The axis in the final array along which the x, y, z components should be stored (default: 0). xyz : Quantity With dimension 3 along xyz_axis. Note that, if possible, this will be a view.
mean(*args, **kwargs)[source] [edit on github]

Vector mean.

Returns a new CartesianRepresentation instance with the means of the x, y, and z components.

Refer to mean for full documentation of the arguments, noting that axis is the entry in the shape of the representation, and that the out argument cannot be used.

norm()[source] [edit on github]

Vector norm.

The norm is the standard Frobenius norm, i.e., the square root of the sum of the squares of all components with non-angular units.

Note that any associated differentials will be dropped during this operation.

Returns: norm : astropy.units.Quantity Vector norm, with the same shape as the representation.
scale_factors()[source] [edit on github]

Scale factors for each component’s direction.

Given unit vectors $$\hat{e}_c$$ and scale factors $$f_c$$, a change in one component of $$\delta c$$ corresponds to a change in representation of $$\delta c \times f_c \times \hat{e}_c$$.

Returns: scale_factors : dict of Quantity The keys are the component names.
sum(*args, **kwargs)[source] [edit on github]

Vector sum.

Returns a new CartesianRepresentation instance with the sums of the x, y, and z components.

Refer to sum for full documentation of the arguments, noting that axis is the entry in the shape of the representation, and that the out argument cannot be used.

to_cartesian()[source] [edit on github]

Convert the representation to its Cartesian form.

Note that any differentials get dropped.

Returns: cartrepr : CartesianRepresentation The representation in Cartesian form.
transform(matrix)[source] [edit on github]

Transform the cartesian coordinates using a 3x3 matrix.

This returns a new representation and does not modify the original one. Any differentials attached to this representation will also be transformed.

Parameters: matrix : ndarray A 3x3 transformation matrix, such as a rotation matrix.

Examples

We can start off by creating a cartesian representation object:

>>> from astropy import units as u
>>> from astropy.coordinates import CartesianRepresentation
>>> rep = CartesianRepresentation([1, 2] * u.pc,
...                               [2, 3] * u.pc,
...                               [3, 4] * u.pc)


We now create a rotation matrix around the z axis:

>>> from astropy.coordinates.matrix_utilities import rotation_matrix
>>> rotation = rotation_matrix(30 * u.deg, axis='z')


Finally, we can apply this transformation:

>>> rep_new = rep.transform(rotation)
>>> rep_new.xyz
<Quantity [[ 1.8660254 , 3.23205081],
[ 1.23205081, 1.59807621],
[ 3.        , 4.        ]] pc>

unit_vectors()[source] [edit on github]

Cartesian unit vectors in the direction of each component.

Given unit vectors $$\hat{e}_c$$ and scale factors $$f_c$$, a change in one component of $$\delta c$$ corresponds to a change in representation of $$\delta c \times f_c \times \hat{e}_c$$.

Returns: unit_vectors : The keys are the component names.