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Datatypes

rerun.datatypes

class Angle

Bases: AngleExt

Datatype: Angle in either radians or degrees.

inner: float = field(converter=float) class-attribute instance-attribute

Must be one of:

  • Radians (float): Angle in radians. One turn is equal to 2π (or τ) radians. Only one of degrees or radians should be set.

  • Degrees (float): Angle in degrees. One turn is equal to 360 degrees. Only one of degrees or radians should be set.

kind: Literal['radians', 'degrees'] = field(default='radians') class-attribute instance-attribute

Possible values:

  • "radians": Angle in radians. One turn is equal to 2π (or τ) radians. Only one of degrees or radians should be set.

  • "degrees": Angle in degrees. One turn is equal to 360 degrees. Only one of degrees or radians should be set.

def __init__(rad=None, deg=None)

Create a new instance of the Angle datatype.

PARAMETER DESCRIPTION
rad

Angle in radians, specify either rad or deg.

TYPE: float | None DEFAULT: None

deg

Angle in degrees, specify either rad or deg.

TYPE: float | None DEFAULT: None

class AnnotationInfo

Bases: AnnotationInfoExt

Datatype: Annotation info annotating a class id or key-point id.

Color and label will be used to annotate entities/keypoints which reference the id. The id refers either to a class or key-point id

def __init__(id, label=None, color=None)

Create a new instance of the AnnotationInfo datatype.

PARAMETER DESCRIPTION
id

ClassId or KeypointId to which this annotation info belongs.

TYPE: int

label

The label that will be shown in the UI.

TYPE: Utf8Like | None DEFAULT: None

color

The color that will be applied to the annotated entity.

TYPE: Rgba32Like | None DEFAULT: None

class Bool

Datatype: A single boolean.

def __init__(value)

Create a new instance of the Bool datatype.

class ClassDescription

Bases: ClassDescriptionExt

Datatype: The description of a semantic Class.

If an entity is annotated with a corresponding ClassId, Rerun will use the attached AnnotationInfo to derive labels and colors.

Keypoints within an annotation class can similarly be annotated with a KeypointId in which case we should defer to the label and color for the AnnotationInfo specifically associated with the Keypoint.

Keypoints within the class can also be decorated with skeletal edges. Keypoint-connections are pairs of KeypointIds. If an edge is defined, and both keypoints exist within the instance of the class, then the keypoints should be connected with an edge. The edge should be labeled and colored as described by the class's AnnotationInfo.

Note that a ClassDescription can be directly logged using rerun.log. This is equivalent to logging a rerun.AnnotationContext containing a single ClassDescription.

def __init__(*, info, keypoint_annotations=[], keypoint_connections=[])

Create a new instance of the ClassDescription datatype.

PARAMETER DESCRIPTION
info

The AnnotationInfo for the class.

TYPE: AnnotationInfoLike

keypoint_annotations

The AnnotationInfo for all the keypoints.

TYPE: Sequence[AnnotationInfoLike] | None DEFAULT: []

keypoint_connections

The connections between keypoints.

TYPE: Sequence[KeypointPairLike] | None DEFAULT: []

class ClassDescriptionMapElem

Bases: ClassDescriptionMapElemExt

Datatype: A helper type for mapping class IDs to class descriptions.

This is internal to the AnnotationContext structure.

def __init__(class_id, class_description)

Create a new instance of the ClassDescriptionMapElem datatype.

PARAMETER DESCRIPTION
class_id

The key: the class ID.

TYPE: ClassIdLike

class_description

The value: class name, color, etc.

TYPE: ClassDescriptionLike

class ClassId

Datatype: A 16-bit ID representing a type of semantic class.

def __init__(id)

Create a new instance of the ClassId datatype.

class EntityPath

Datatype: A path to an entity in the DataStore.

def __init__(path)

Create a new instance of the EntityPath datatype.

class Float32

Datatype: A single-precision 32-bit IEEE 754 floating point number.

def __init__(value)

Create a new instance of the Float32 datatype.

class KeypointId

Datatype: A 16-bit ID representing a type of semantic keypoint within a class.

KeypointIds are only meaningful within the context of a [rerun.datatypes.ClassDescription].

Used to look up an [rerun.datatypes.AnnotationInfo] for a Keypoint within the [rerun.components.AnnotationContext].

def __init__(id)

Create a new instance of the KeypointId datatype.

class KeypointPair

Bases: KeypointPairExt

Datatype: A connection between two Keypoints.

def __init__(keypoint0, keypoint1)

Create a new instance of the KeypointPair datatype.

PARAMETER DESCRIPTION
keypoint0

The first point of the pair.

TYPE: KeypointIdLike

keypoint1

The second point of the pair.

TYPE: KeypointIdLike

class Mat3x3

Bases: Mat3x3Ext

Datatype: A 3x3 Matrix.

Matrices in Rerun are stored as flat list of coefficients in column-major order:

            column 0       column 1       column 2
       -------------------------------------------------
row 0 | flat_columns[0] flat_columns[3] flat_columns[6]
row 1 | flat_columns[1] flat_columns[4] flat_columns[7]
row 2 | flat_columns[2] flat_columns[5] flat_columns[8]

However, construction is done from a list of rows, which follows NumPy's convention:

np.testing.assert_array_equal(
    rr.datatypes.Mat3x3([1, 2, 3, 4, 5, 6, 7, 8, 9]).flat_columns, np.array([1, 4, 7, 2, 5, 8, 3, 6, 9], dtype=np.float32)
)
np.testing.assert_array_equal(
    rr.datatypes.Mat3x3([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).flat_columns,
    np.array([1, 4, 7, 2, 5, 8, 3, 6, 9], dtype=np.float32),
)
If you want to construct a matrix from a list of columns instead, use the named columns parameter:
np.testing.assert_array_equal(
    rr.datatypes.Mat3x3(columns=[1, 2, 3, 4, 5, 6, 7, 8, 9]).flat_columns,
    np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32),
)
np.testing.assert_array_equal(
    rr.datatypes.Mat3x3(columns=[[1, 2, 3], [4, 5, 6], [7, 8, 9]]).flat_columns,
    np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32),
)

def __init__(rows=None, *, columns=None)

class Mat4x4

Bases: Mat4x4Ext

Datatype: A 4x4 Matrix.

Matrices in Rerun are stored as flat list of coefficients in column-major order:

           column 0         column 1         column 2         column 3
       --------------------------------------------------------------------
row 0 | flat_columns[0]  flat_columns[4]  flat_columns[8]  flat_columns[12]
row 1 | flat_columns[1]  flat_columns[5]  flat_columns[9]  flat_columns[13]
row 2 | flat_columns[2]  flat_columns[6]  flat_columns[10] flat_columns[14]
row 3 | flat_columns[3]  flat_columns[7]  flat_columns[11] flat_columns[15]

However, construction is done from a list of rows, which follows NumPy's convention:

np.testing.assert_array_equal(
    rr.datatypes.Mat4x4([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]).flat_columns,
    np.array([1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, 4, 8, 12, 16], dtype=np.float32),
)
np.testing.assert_array_equal(
    rr.datatypes.Mat4x4([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]).flat_columns,
    np.array([1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, 4, 8, 12, 16], dtype=np.float32),
)
If you want to construct a matrix from a list of columns instead, use the named columns parameter:
np.testing.assert_array_equal(
    rr.datatypes.Mat4x4(columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]).flat_columns,
    np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=np.float32),
)
np.testing.assert_array_equal(
    rr.datatypes.Mat4x4(columns=[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]).flat_columns,
    np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], dtype=np.float32),
)

def __init__(rows=None, *, columns=None)

class Material

Bases: MaterialExt

Datatype: Material properties of a mesh, e.g. its color multiplier.

def __init__(albedo_factor=None)

Create a new instance of the Material datatype.

PARAMETER DESCRIPTION
albedo_factor

Optional color multiplier.

TYPE: Rgba32Like | None DEFAULT: None

class Quaternion

Bases: QuaternionExt

Datatype: A Quaternion represented by 4 real numbers.

Note: although the x,y,z,w components of the quaternion will be passed through to the datastore as provided, when used in the Viewer Quaternions will always be normalized.

class Range1D

Bases: Range1DExt

Datatype: A 1D range, specifying a lower and upper bound.

def __init__(range)

Create a new instance of the Range1D datatype.

class Range2D

Datatype: An Axis-Aligned Bounding Box in 2D space, implemented as the minimum and maximum corners.

def __init__(x_range, y_range)

Create a new instance of the Range2D datatype.

PARAMETER DESCRIPTION
x_range

The range of the X-axis (usually left and right bounds).

TYPE: Range1DLike

y_range

The range of the Y-axis (usually top and bottom bounds).

TYPE: Range1DLike

class Rgba32

Bases: Rgba32Ext

Datatype: An RGBA color with unmultiplied/separate alpha, in sRGB gamma space with linear alpha.

The color is stored as a 32-bit integer, where the most significant byte is R and the least significant byte is A.

Float colors are assumed to be in 0-1 gamma sRGB space. All other colors are assumed to be in 0-255 gamma sRGB space. If there is an alpha, we assume it is in linear space, and separate (NOT pre-multiplied).

def __init__(rgba)

Create a new instance of the Rgba32 datatype.

class Rotation3D

Bases: Rotation3DExt

Datatype: A 3D rotation.

inner: Union[datatypes.Quaternion, datatypes.RotationAxisAngle] = field(converter=Rotation3DExt.inner__field_converter_override) class-attribute instance-attribute

Must be one of:

  • Quaternion (datatypes.Quaternion): Rotation defined by a quaternion.

  • AxisAngle (datatypes.RotationAxisAngle): Rotation defined with an axis and an angle.

class RotationAxisAngle

Bases: RotationAxisAngleExt

Datatype: 3D rotation represented by a rotation around a given axis.

def __init__(axis, angle=None, *, radians=None, degrees=None)

Create a new instance of the RotationAxisAngle datatype.

PARAMETER DESCRIPTION
axis

Axis to rotate around.

This is not required to be normalized. If normalization fails (typically because the vector is length zero), the rotation is silently ignored.

TYPE: Vec3DLike

angle

How much to rotate around the axis.

TYPE: AngleLike | None DEFAULT: None

radians

How much to rotate around the axis, in radians. Specify this instead of degrees or angle.

TYPE: float | None DEFAULT: None

degrees

How much to rotate around the axis, in radians. Specify this instead of radians or angle.

TYPE: float | None DEFAULT: None

class Scale3D

Bases: Scale3DExt

Datatype: 3D scaling factor, part of a transform representation.

Example
# uniform scaling
scale = rr.datatypes.Scale3D(3.)

# non-uniform scaling
scale = rr.datatypes.Scale3D([1, 1, -1])
scale = rr.datatypes.Scale3D(rr.datatypes.Vec3D([1, 1, -1]))
inner: Union[datatypes.Vec3D, float] = field(converter=Scale3DExt.inner__field_converter_override) class-attribute instance-attribute

Must be one of:

  • ThreeD (datatypes.Vec3D): Individual scaling factors for each axis, distorting the original object.

  • Uniform (float): Uniform scaling factor along all axis.

class TensorBuffer

Bases: TensorBufferExt

Datatype: The underlying storage for a Tensor.

Tensor elements are stored in a contiguous buffer of a single type.

inner: Union[npt.NDArray[np.float16], npt.NDArray[np.float32], npt.NDArray[np.float64], npt.NDArray[np.int16], npt.NDArray[np.int32], npt.NDArray[np.int64], npt.NDArray[np.int8], npt.NDArray[np.uint16], npt.NDArray[np.uint32], npt.NDArray[np.uint64], npt.NDArray[np.uint8]] = field(converter=TensorBufferExt.inner__field_converter_override) class-attribute instance-attribute

Must be one of:

  • U8 (npt.NDArray[np.uint8]): 8bit unsigned integer.

  • U16 (npt.NDArray[np.uint16]): 16bit unsigned integer.

  • U32 (npt.NDArray[np.uint32]): 32bit unsigned integer.

  • U64 (npt.NDArray[np.uint64]): 64bit unsigned integer.

  • I8 (npt.NDArray[np.int8]): 8bit signed integer.

  • I16 (npt.NDArray[np.int16]): 16bit signed integer.

  • I32 (npt.NDArray[np.int32]): 32bit signed integer.

  • I64 (npt.NDArray[np.int64]): 64bit signed integer.

  • F16 (npt.NDArray[np.float16]): 16bit IEEE-754 floating point, also known as half.

  • F32 (npt.NDArray[np.float32]): 32bit IEEE-754 floating point, also known as float or single.

  • F64 (npt.NDArray[np.float64]): 64bit IEEE-754 floating point, also known as double.

  • JPEG (npt.NDArray[np.uint8]): Raw bytes of a JPEG file.

  • NV12 (npt.NDArray[np.uint8]): NV12 is a YUV 4:2:0 chroma downsamples format with 8 bits per channel.

    First comes entire image in Y, followed by interleaved lines ordered as U0, V0, U1, V1, etc.

  • YUY2 (npt.NDArray[np.uint8]): YUY2, also known as YUYV is a YUV 4:2:2 chroma downsampled format with 8 bits per channel.

    The order of the channels is Y0, U0, Y1, V0.

kind: Literal['u8', 'u16', 'u32', 'u64', 'i8', 'i16', 'i32', 'i64', 'f16', 'f32', 'f64', 'jpeg', 'nv12', 'yuy2'] = field(default='u8') class-attribute instance-attribute

Possible values:

  • "u8": 8bit unsigned integer.

  • "u16": 16bit unsigned integer.

  • "u32": 32bit unsigned integer.

  • "u64": 64bit unsigned integer.

  • "i8": 8bit signed integer.

  • "i16": 16bit signed integer.

  • "i32": 32bit signed integer.

  • "i64": 64bit signed integer.

  • "f16": 16bit IEEE-754 floating point, also known as half.

  • "f32": 32bit IEEE-754 floating point, also known as float or single.

  • "f64": 64bit IEEE-754 floating point, also known as double.

  • "jpeg": Raw bytes of a JPEG file.

  • "nv12": NV12 is a YUV 4:2:0 chroma downsamples format with 8 bits per channel.

    First comes entire image in Y, followed by interleaved lines ordered as U0, V0, U1, V1, etc.

  • "yuy2": YUY2, also known as YUYV is a YUV 4:2:2 chroma downsampled format with 8 bits per channel.

    The order of the channels is Y0, U0, Y1, V0.

class TensorData

Bases: TensorDataExt

Datatype: An N-dimensional array of numbers.

The number of dimensions and their respective lengths is specified by the shape field. The dimensions are ordered from outermost to innermost. For example, in the common case of a 2D RGB Image, the shape would be [height, width, channel].

These dimensions are combined with an index to look up values from the buffer field, which stores a contiguous array of typed values.

Note that the buffer may be encoded in a compressed format such as jpeg or in a format with downsampled chroma, such as NV12 or YUY2. For file formats, the shape is used as a hint, for chroma downsampled format the shape has to be the shape of the decoded image.

def __init__(*, shape=None, buffer=None, array=None, dim_names=None)

Construct a TensorData object.

The TensorData object is internally represented by three fields: shape and buffer.

This constructor provides additional arguments 'array', and 'dim_names'. When passing in a multi-dimensional array such as a np.ndarray, the shape and buffer fields will be populated automagically.

PARAMETER DESCRIPTION
self

The TensorData object to construct.

TYPE: Any

shape

The shape of the tensor. If None, and an array is provided, the shape will be inferred from the shape of the array.

TYPE: Sequence[TensorDimensionLike] | None DEFAULT: None

buffer

The buffer of the tensor. If None, and an array is provided, the buffer will be generated from the array.

TYPE: TensorBufferLike | None DEFAULT: None

array

A numpy array (or The array of the tensor. If None, the array will be inferred from the buffer.

TYPE: TensorLike | None DEFAULT: None

dim_names

The names of the tensor dimensions when generating the shape from an array.

TYPE: Sequence[str | None] | None DEFAULT: None

class TensorDimension

Datatype: A single dimension within a multi-dimensional tensor.

def __init__(size, name=None)

Create a new instance of the TensorDimension datatype.

PARAMETER DESCRIPTION
size

The length of this dimension.

TYPE: int

name

The name of this dimension, e.g. "width", "height", "channel", "batch', ….

TYPE: str | None DEFAULT: None

class TensorDimensionIndexSelection

Datatype: Indexing a specific tensor dimension.

Selecting dimension=2 and index=42 is similar to doing tensor[:, :, 42, :, :, …] in numpy.

def __init__(dimension, index)

Create a new instance of the TensorDimensionIndexSelection datatype.

PARAMETER DESCRIPTION
dimension

The dimension number to select.

TYPE: int

index

The index along the dimension to use.

TYPE: int

class TensorDimensionSelection

Bases: TensorDimensionSelectionExt

Datatype: Selection of a single tensor dimension.

def __init__(dimension, *, invert=False)

Create a new instance of the TensorDimensionSelection datatype.

PARAMETER DESCRIPTION
dimension

The dimension number to select.

TYPE: int

invert

Invert the direction of the dimension.

TYPE: bool DEFAULT: False

class TimeInt

Bases: TimeIntExt

Datatype: A 64-bit number describing either nanoseconds OR sequence numbers.

def __init__(*, seq=None, seconds=None, nanos=None)

Create a new instance of the TimeInt datatype.

PARAMETER DESCRIPTION
seq

Time as a sequence number. Mutually exclusive with seconds and nanos.

TYPE: int | None DEFAULT: None

seconds

Time in seconds. Mutually exclusive with seq and nanos.

TYPE: float | None DEFAULT: None

nanos

Time in nanoseconds. Mutually exclusive with seq and seconds.

TYPE: int | None DEFAULT: None

class TimeRange

Datatype: Visible time range bounds for a specific timeline.

def __init__(start, end)

Create a new instance of the TimeRange datatype.

PARAMETER DESCRIPTION
start

Low time boundary for sequence timeline.

TYPE: TimeRangeBoundaryLike

end

High time boundary for sequence timeline.

TYPE: TimeRangeBoundaryLike

class TimeRangeBoundary

Bases: TimeRangeBoundaryExt

Datatype: Left or right boundary of a time range.

inner: Union[None, datatypes.TimeInt] = field() class-attribute instance-attribute

Must be one of:

  • CursorRelative (datatypes.TimeInt): Boundary is a value relative to the time cursor.

  • Absolute (datatypes.TimeInt): Boundary is an absolute value.

  • Infinite (None): The boundary extends to infinity.

kind: Literal['cursor_relative', 'absolute', 'infinite'] = field(default='cursor_relative') class-attribute instance-attribute

Possible values:

  • "cursor_relative": Boundary is a value relative to the time cursor.

  • "absolute": Boundary is an absolute value.

  • "infinite": The boundary extends to infinity.

def absolute(time=None, *, seq=None, seconds=None, nanos=None) staticmethod

Boundary that is at an absolute time.

PARAMETER DESCRIPTION
time

Absolute time.

Mutually exclusive with seq, seconds and nanos.

TYPE: TimeInt | None DEFAULT: None

seq

Absolute time in sequence numbers.

Use this for sequence timelines. Mutually exclusive with time, seconds and nanos.

TYPE: int | None DEFAULT: None

seconds

Absolute time in seconds.

Use this for time based timelines. Mutually exclusive with time, seq and nanos.

TYPE: float | None DEFAULT: None

nanos

Absolute time in nanoseconds.

Use this for time based timelines. Mutually exclusive with time, seq and seconds.

TYPE: int | None DEFAULT: None

def cursor_relative(offset=None, *, seq=None, seconds=None, nanos=None) staticmethod

Boundary that is relative to the timeline cursor.

The offset can be positive or negative. An offset of zero (the default) means the cursor time itself.

PARAMETER DESCRIPTION
offset

Offset from the cursor time.

Mutually exclusive with seq, seconds and nanos.

TYPE: TimeInt | None DEFAULT: None

seq

Offset in sequence numbers.

Use this for sequence timelines. Mutually exclusive with time, seconds and nanos.

TYPE: int | None DEFAULT: None

seconds

Offset in seconds.

Use this for time based timelines. Mutually exclusive with time, seq and nanos.

TYPE: float | None DEFAULT: None

nanos

Offset in nanoseconds.

Use this for time based timelines. Mutually exclusive with time, seq and seconds.

TYPE: int | None DEFAULT: None

def infinite() staticmethod

Boundary that extends to infinity.

Depending on the context, this can mean the beginning or the end of the timeline.

class Transform3D

Bases: Transform3DExt

Datatype: Representation of a 3D affine transform.

inner: Union[datatypes.TranslationAndMat3x3, datatypes.TranslationRotationScale3D] = field() class-attribute instance-attribute

Must be one of:

  • TranslationAndMat3x3 (datatypes.TranslationAndMat3x3): Translation plus a 3x3 matrix for scale, rotation, skew, etc.

  • TranslationRotationScale (datatypes.TranslationRotationScale3D): Translation, rotation and scale, decomposed.

class TranslationAndMat3x3

Bases: TranslationAndMat3x3Ext

Datatype: Representation of an affine transform via a 3x3 affine matrix paired with a translation.

First applies the matrix, then the translation.

def __init__(translation=None, mat3x3=None, *, from_parent=False)

Create a new instance of the TranslationAndMat3x3 datatype.

PARAMETER DESCRIPTION
translation

3D translation, applied after the matrix.

TYPE: Vec3DLike | None DEFAULT: None

mat3x3

3x3 matrix for scale, rotation & shear.

TYPE: Mat3x3Like | None DEFAULT: None

from_parent

If true, the transform maps from the parent space to the space where the transform was logged. Otherwise, the transform maps from the space to its parent.

TYPE: bool DEFAULT: False

class TranslationRotationScale3D

Bases: TranslationRotationScale3DExt

Datatype: Representation of an affine transform via separate translation, rotation & scale.

class UInt32

Datatype: A 32bit unsigned integer.

def __init__(value)

Create a new instance of the UInt32 datatype.

class UInt64

Datatype: A 64bit unsigned integer.

def __init__(value)

Create a new instance of the UInt64 datatype.

class UVec2D

Datatype: A uint32 vector in 2D space.

def __init__(xy)

Create a new instance of the UVec2D datatype.

class UVec3D

Bases: UVec3DExt

Datatype: A uint32 vector in 3D space.

def __init__(xyz)

Create a new instance of the UVec3D datatype.

class UVec4D

Datatype: A uint vector in 4D space.

def __init__(xyzw)

Create a new instance of the UVec4D datatype.

class Utf8

Datatype: A string of text, encoded as UTF-8.

def __init__(value)

Create a new instance of the Utf8 datatype.

class Uuid

Bases: UuidExt

Datatype: A 16-byte UUID.

def __init__(bytes)

Create a new instance of the Uuid datatype.

PARAMETER DESCRIPTION
bytes

The raw bytes representing the UUID.

TYPE: UuidLike

class Vec2D

Bases: Vec2DExt

Datatype: A vector in 2D space.

def __init__(xy)

Create a new instance of the Vec2D datatype.

class Vec3D

Bases: Vec3DExt

Datatype: A vector in 3D space.

def __init__(xyz)

Create a new instance of the Vec3D datatype.

class Vec4D

Bases: Vec4DExt

Datatype: A vector in 4D space.

def __init__(xyzw)

Create a new instance of the Vec4D datatype.

class VisibleTimeRange

Bases: VisibleTimeRangeExt

Datatype: Visible time range bounds for a specific timeline.

def __init__(timeline, range=None, *, start=None, end=None)

Create a new instance of the VisibleTimeRange datatype.

PARAMETER DESCRIPTION
timeline

Name of the timeline this applies to.

TYPE: Utf8Like

range

Time range to use for this timeline.

TYPE: TimeRangeLike | None DEFAULT: None

start

Low time boundary for sequence timeline. Specify this instead of range.

TYPE: TimeRangeBoundary | None DEFAULT: None

end

High time boundary for sequence timeline. Specify this instead of range.

TYPE: TimeRangeBoundary | None DEFAULT: None