Lunar Frames¶

Epoch instant for computation of the transformation matrix. A sequence evaluates one matrix per epoch.

numpy.ndarray: 3x3 rotation matrix transforming LCI -> LFPA, shape (3, 3) for a single epoch or (n, 3, 3) for a sequence of n epochs.

Epoch instant for computation of the transformation matrix. A sequence evaluates one matrix per epoch.

numpy.ndarray: 3x3 rotation matrix transforming LFPA -> LCI, shape (3, 3) for a single epoch or (n, 3, 3) for a sequence of n epochs.

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_lci along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian LFPA position (m), shape (3,) for a single input, or the batch layout of x_lci (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LFPA position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_lfpa along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI position (m), shape (3,) for a single input, or the batch layout of x_lfpa (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_lci along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian LFPA state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_lci (shape (n, 6) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LFPA state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_lfpa along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_lfpa (shape (n, 6) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation matrix. A sequence evaluates one matrix per epoch.

numpy.ndarray: 3x3 rotation matrix transforming LCI -> LFME, shape (3, 3) for a single epoch or (n, 3, 3) for a sequence of n epochs.

Epoch instant for computation of the transformation matrix. A sequence evaluates one matrix per epoch.

numpy.ndarray: 3x3 rotation matrix transforming LFME -> LCI, shape (3, 3) for a single epoch or (n, 3, 3) for a sequence of n epochs.

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_lci along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian LFME position (m), shape (3,) for a single input, or the batch layout of x_lci (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LFME position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_lfme along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI position (m), shape (3,) for a single input, or the batch layout of x_lfme (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_lci along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian LFME state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_lci (shape (n, 6) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LFME state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_lfme along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_lfme (shape (n, 6) for a single vector with a sequence of n epochs).

numpy.ndarray: 3x3 rotation matrix transforming LFPA -> LFME

numpy.ndarray: 3x3 rotation matrix transforming LFME -> LFPA

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian ECI position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_eci along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI position (m), shape (3,) for a single input, or the batch layout of x_eci (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI position (m), shape (3,), or a batch of vectors with the 3 components along axis (for example shape (n, 3)).

The axis of x_lci along which the 3 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 3) the components lie along the last axis, so the default -1 applies; a (3, n) column layout uses axis=0.

numpy.ndarray: Cartesian ECI position (m), shape (3,) for a single input, or the batch layout of x_lci (shape (n, 3) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian ECI state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_eci along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian LCI state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_eci (shape (n, 6) for a single vector with a sequence of n epochs).

Epoch instant for computation of the transformation. A sequence evaluates one epoch per vector (or broadcasts a single vector across all epochs).

Cartesian LCI state [position (m), velocity (m/s)], shape (6,), or a batch of vectors with the 6 components along axis (for example shape (n, 6)).

The axis of x_lci along which the 6 components of a single vector lie; the remaining axes enumerate the batch. For a batch of shape (n, 6) the components lie along the last axis, so the default -1 applies; a (6, n) column layout uses axis=0.

numpy.ndarray: Cartesian ECI state [position (m), velocity (m/s)], shape (6,) for a single input, or the batch layout of x_lci (shape (n, 6) for a single vector with a sequence of n epochs).