roboto.domain.topics.record

Module Contents

class roboto.domain.topics.record.CanonicalDataType

Bases: enum.Enum

Normalized data types used across different robotics frameworks.

Well-known and simplified data types that provide a common vocabulary for describing message path data types across different frameworks and technologies. These canonical types are primarily used for UI rendering decisions and cross-platform compatibility.

The canonical types abstract away framework-specific details while preserving the essential characteristics needed for data processing and visualization.

References

• ROS 1 field types: http://wiki.ros.org/msg

• ROS 2 field types: https://docs.ros.org/en/iron/Concepts/Basic/About-Interfaces.html#field-types

• uORB: https://docs.px4.io/main/en/middleware/uorb.html#adding-a-new-topic

Example mappings:

• float32 -> CanonicalDataType.Number

• uint8[] -> CanonicalDataType.Array

• sensor_msgs/Image -> CanonicalDataType.Image

• geometry_msgs/Pose -> CanonicalDataType.Object

• std_msgs/Header -> CanonicalDataType.Object

• string -> CanonicalDataType.String

• char -> CanonicalDataType.String

• bool -> CanonicalDataType.Boolean

• byte -> CanonicalDataType.Byte

Array = 'array'

A sequence of values.

Boolean = 'boolean'

Byte = 'byte'

Image = 'image'

Special purpose type for data that can be rendered as an image.

LatDegFloat = 'latdegfloat'

Geographic point in degrees. E.g. 47.6749387 (used in ULog ver_data_format >= 2)

LatDegInt = 'latdegint'

Geographic point in degrees, expressed as an integer. E.g. 317534036 (used in ULog ver_data_format < 2)

LonDegFloat = 'londegfloat'

Geographic point in degrees. E.g. 9.1445274 (used in ULog ver_data_format >= 2)

LonDegInt = 'londegint'

Geographic point in degrees, expressed as an integer. E.g. 1199146398 (used in ULog ver_data_format < 2)

Number = 'number'

NumberArray = 'number_array'

Object = 'object'

A struct with attributes.

String = 'string'

Timestamp = 'timestamp'

Time elapsed since the Unix epoch, identifying a single instant on the time-line. Roboto clients will look for a "unit" metadata key on the MessagePath record, and will assume “ns” if none is found. If the timestamp is in a different unit, add the following metadata to the MessagePath record: { "unit": "s"|"ms"|"us"|"ns" }

Unknown = 'unknown'

This is a fallback and should be used sparingly.

class roboto.domain.topics.record.MessagePathRecord(/, **data)

Bases: pydantic.BaseModel

Record representing a message path within a topic.

Defines a specific field or signal within a topic’s data schema, including its data type, metadata, and statistical information. Message paths use dot notation to specify nested attributes within complex message structures.

Message paths are the fundamental units for accessing individual data elements within time-series robotics data, enabling fine-grained analysis and visualization of specific signals or measurements.

Parameters:

data (Any)

canonical_data_type: CanonicalDataType

Normalized data type, used primarily internally by the Roboto Platform.

created: datetime.datetime

created_by: str

data_type: str

‘Native’/framework-specific data type of the attribute at this path. E.g. “float32”, “uint8[]”, “geometry_msgs/Pose”, “string”.

message_path: str

Dot-delimited path to the attribute within the datum record.

message_path_id: str

metadata: collections.abc.Mapping[str, Any] = None

Key-value pairs to associate with this metadata for discovery and search, e.g. { ‘min’: ‘0.71’, ‘max’: ‘1.77 }

modified: datetime.datetime

modified_by: str

org_id: str

This message path’s organization ID, which is the organization ID of the containing topic.

representations: collections.abc.MutableSequence[RepresentationRecord] = None

Zero to many Representations of this MessagePath.

topic_id: str

class roboto.domain.topics.record.MessagePathStatistic

Bases: enum.Enum

Statistics computed by Roboto in our standard ingestion actions.

Count = 'count'

Max = 'max'

Mean = 'mean'

Median = 'median'

Min = 'min'

class roboto.domain.topics.record.RepresentationRecord(/, **data)

Bases: pydantic.BaseModel

Record representing a data representation for topic content.

A representation is a pointer to processed topic data stored in a specific format and location. Representations enable efficient access to topic data by providing multiple storage formats optimized for different use cases.

Most message paths within a topic point to the same representation (e.g., an MCAP or Parquet file containing all topic data). However, some message paths may have multiple representations for analytics or preview formats.

Representations are versioned and associated with specific files or storage locations through the association field.

Parameters:

data (Any)

association: roboto.association.Association

Identifier and entity type with which this Representation is associated. E.g., a file, a database.

created: datetime.datetime

modified: datetime.datetime

representation_id: str

storage_format: RepresentationStorageFormat

topic_id: str

version: int

class roboto.domain.topics.record.RepresentationStorageFormat

Bases: enum.Enum

Supported storage formats for topic data representations.

Defines the available formats for storing and accessing topic data within the Roboto platform. Each format has different characteristics and use cases.

MCAP = 'mcap'

MCAP format - optimized for robotics time-series data with efficient random access.

PARQUET = 'parquet'

Parquet format - columnar storage optimized for analytics and large-scale data processing.

class roboto.domain.topics.record.TopicRecord(/, **data)

Bases: pydantic.BaseModel

Record representing a topic in the Roboto platform.

A topic is a collection of timestamped data records that share a common name and association (typically a file). Topics represent logical data streams from robotics systems, such as sensor readings, robot state information, or other time-series data.

Data from the same file with the same topic name are considered part of the same topic. Data from different files or with different topic names belong to separate topics, even if they have similar schemas.

When source files are chunked by time or size but represent the same logical data collection, they will produce multiple topic records for the same “logical topic” (same name and schema) across those chunks.

Parameters:

data (Any)

association: roboto.association.Association

Identifier and entity type with which this Topic is associated. E.g., a file, a dataset.

created: datetime.datetime

created_by: str

default_representation: RepresentationRecord | None = None

Default Representation for this Topic. Assume that if a MessagePath is not more specifically associated with a Representation, it should use this one.

end_time: int | None = None

Timestamp of oldest message in topic, in nanoseconds since epoch (assumed Unix epoch).

message_count: int | None = None

message_paths: collections.abc.MutableSequence[MessagePathRecord] = None

Zero to many MessagePathRecords associated with this TopicSource.

metadata: collections.abc.Mapping[str, Any] = None

Arbitrary metadata.

modified: datetime.datetime

modified_by: str

org_id: str

schema_checksum: str | None = None

Checksum of topic schema. May be None if topic does not have a known/named schema.

schema_name: str | None = None

Type of messages in topic. E.g., “sensor_msgs/PointCloud2”. May be None if topic does not have a known/named schema.

start_time: int | None = None

Timestamp of earliest message in topic, in nanoseconds since epoch (assumed Unix epoch).

topic_id: str

topic_name: str