roboto.experimental.topics#

Topics APIs in active refinement; see roboto.experimental for the stability contract.

Submodules#

Package Contents#

class roboto.experimental.topics.DatasetContext(/, **data)#

Bases: pydantic.BaseModel

The dataset a Topic is scoped to: limits topic operations to the topic’s data in the dataset’s files.

An omitted start_time or end_time defaults to the start or end of the topic’s data in those files, resolved by the service when the data is read.

Parameters:

data (Any)

dataset_id: str#
model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class roboto.experimental.topics.DeviceContext(/, **data)#

Bases: pydantic.BaseModel

The device a Topic is scoped to: limits topic operations to the topic’s data in the device’s files.

A file belongs to the device it names, or, when it names none, to the device its dataset names. An omitted start_time or end_time defaults to the start or end of the topic’s data in those files, resolved by the service when the data is read.

Parameters:

data (Any)

device_id: str#
model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class roboto.experimental.topics.FieldAddress(/, **data)#

Bases: pydantic.BaseModel

Addresses a schema field, and the subtree nested under it, by exactly one of two forms.

A path names the field by its path_in_schema components directly (no string delimiter, so a component may itself contain a .); a field_id names it opaquely and resolves server-side to the same path. Either form designates the field and every field nested under it.

Parameters:

data (Any)

field_id: str | None = None#

The field’s opaque id (sf_*).

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

path: tuple[str, ...] | None = None#

The field’s path_in_schema components; () addresses the schema root.

roboto.experimental.topics.FieldAddressLike#

A field-subtree address, as a FieldAddress or explicit path components (("pose", "position") for a nested field, ("angular_velocity",) for a top-level one).

Each component is one path_in_schema element; there is no string delimiter, so a component may itself contain a .. A bare string is rejected even though it is structurally a Sequence[str] — splitting it on . would guess at component boundaries, and iterating it would address one field per character; pass the components explicitly instead.

class roboto.experimental.topics.FileContext(/, **data)#

Bases: pydantic.BaseModel

The file a Topic is scoped to: limits topic operations to the topic’s data in that file.

An omitted start_time or end_time defaults to the start or end of the topic’s data in the file, resolved by the service when the data is read.

Parameters:

data (Any)

file_id: str#
model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

roboto.experimental.topics.PLAN_VERSION: int = 1#

Contract version stamped on every plan.

ReadPlan validation refuses a plan whose version it does not recognize, so a consumer on an older contract fails at parse time instead of misreading a newer plan.

class roboto.experimental.topics.ReadPlan(/, **data)#

Bases: pydantic.BaseModel

Resolves a read of one topic over a time window into the files to fetch and how to interpret them.

Parameters:

data (Any)

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

partitions: tuple[ReadPlanPartition, ...] = ()#

One entry per partition in the window, each its own fetch-and-interpret plan.

Ordered by when each file’s data begins. Partitions backed by the same file are contiguous in this tuple, ordered by where their data_range slice starts in that file, so they arrive in the order their data sits in it. This orders whole partitions, not rows.

plan_version: int = 1#

Contract version of this plan. Validation refuses a version this model does not recognize.

projection: ReadPlanProjection#

The output fields a consumer projects decoded rows to.

schema_: ReadPlanSchemaRef | None#

The schema the plan reads under. Serializes as schema.

None when no partition of the topic lies in the window (within the request’s restrictions), and the plan then has no partitions. A plan that names a schema may also have no partitions.

topic_id: str#

The topic this plan reads.

window: TimeWindow | None#

The time window the caller asked to read, which the plan was resolved over.

A consumer selects rows by each partition’s ReadPlanPartition.window alone, which can be narrower than this one.

None only when the request omitted a bound and either its restrictions select no timestamped data for the topic, or the bound it gave lies past the far end of that data (a start after the data ends, or an end before it begins); the plan then has no partitions.

class roboto.experimental.topics.ReadPlanFieldRef(/, **data)#

Bases: pydantic.BaseModel

A schema field named by both its id and its path components in the schema.

Parameters:

data (Any)

field_id: str#

Identifier of the schema field.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

path: roboto.domain.topics.record.FieldPath#

The field’s path components within the schema, from the root to the field.

class roboto.experimental.topics.ReadPlanObjectRef(/, **data)#

Bases: pydantic.BaseModel

Points to the file backing a scan task. A consumer fetches the file’s bytes from it.

Parameters:

data (Any)

fs_node_id: str#

Identifier of the backing file. This id is stable, so a consumer can cache on it.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

size_bytes: int | None = None#

The source object’s size in bytes.

class roboto.experimental.topics.ReadPlanPartition(/, **data)#

Bases: pydantic.BaseModel

Everything needed to fetch and interpret one in-window partition’s bytes.

Parameters:

data (Any)

data_range: roboto.domain.topics.record.DataRange | None = None#

The slice of the file this partition owns, as (start, end), or None for the whole file.

start is the first owned position and end is one past the last, like a Python slice. Positions are in the addressing native to the file the slice was declared against: stored-row positions counted from 0, or nanoseconds of media time for video. Any slice a consumer decodes is a range of stored rows.

A slice appears when one file packs several partitions’ data (a LeRobot v3 data file, for example). Each of those partitions carries its own time_offset_ns and may cover the same instants as the partitions beside it in the file, so time alone cannot separate its rows from theirs. Restrict each scan task’s decode to this slice before filtering by time, or the read returns the neighboring partitions’ rows too.

A scan task may name a transformed representation of the file the slice was declared against (see ReadPlanScanTask.transformations) rather than that file itself. The slice still applies unchanged: a partition that owns a slice is served only by representations holding the same content at the same positions.

fs_node_id: str | None = None#

Identifier of the file this partition’s data was recorded in.

A scan task can read a transformed representation of that file (see ReadPlanScanTask.transformations), which is stored as a different file, so a scan task’s object does not always name the recording a partition’s rows came from; this id does. None when the plan does not name the file.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

scan_tasks: tuple[ReadPlanScanTask, ...] = ()#

The files to read for this partition; empty when the partition has no readable data.

A partition may have several scan tasks, each covering a subtree of the schema or the whole schema; the read takes each field from the highest-precedence scan task whose subtree contains it.

time_offset_ns: int#

Offset a consumer adds to each decoded row timestamp; the same for every row in the partition.

timestamp: ReadPlanTimestamp#

Where this partition’s row timestamps come from.

topic_part_id: str#

Identifier of the partition.

window: TimeWindow#

The window this partition’s rows are selected in, in absolute Unix-epoch nanoseconds.

A consumer keeps only the rows whose stored timestamp, once time_offset_ns is added, falls inside this window, inclusive on both ends. It is the only time filter a read applies.

It is the caller’s window intersected with the part of this partition’s backing file the read is scoped to, so it can be narrower than ReadPlan.window: a read scoped to a Session narrows it when the Session holds that file over only part of the file’s time span. The narrowing is stated per file, so every partition backed by one file carries the same window; ReadPlanPartition.data_range separates the partitions packed into one file.

class roboto.experimental.topics.ReadPlanProjection(/, **data)#

Bases: pydantic.BaseModel

The output fields the plan resolves rows to.

The projection takes exactly one of two forms: either every field in the schema (all is true, and the field list is left implicit so the plan need not enumerate a large schema) or an explicit fields list.

Parameters:

data (Any)

all: bool = False#

True when the projection covers every field in the schema.

classmethod all_fields()#

Return a projection covering every field in the schema.

Return type:

ReadPlanProjection

fields: tuple[ReadPlanFieldRef, ...] | None = None#

The resolved field set when the read is narrowed; None when all is true.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod narrowed(fields)#

Return a projection narrowed to an explicit field set.

Parameters:

fields (Iterable[ReadPlanFieldRef])

Return type:

ReadPlanProjection

class roboto.experimental.topics.ReadPlanRequest(/, **data)#

Bases: pydantic.BaseModel

The body of a read-plan request: the logical read question to resolve into a physical plan.

session_id, file_id, dataset_id and device_id are restrictions: each limits the read to the topic’s data in one session, file, dataset or device. Every restriction the request names narrows the read, and restrictions named together intersect.

Parameters:

data (Any)

dataset_id: str | None = None#

Limits the read to the topic’s data in this dataset’s files; None adds no dataset restriction.

device_id: str | None = None#

Limits the read to the topic’s data in this device’s files; None adds no device restriction.

A file belongs to the device it names, or, when it names none, to the device its dataset names.

end_time: int | None = None#

Inclusive window upper bound, absolute Unix-epoch nanoseconds.

May be None on the same terms as start_time; the bound then defaults to the latest time of the topic’s data within the request’s restrictions.

fields_exclude: tuple[FieldAddress, ...] | None = None#

Field subtrees to drop from the projection; None drops none.

fields_include: tuple[FieldAddress, ...] | None = None#

Field subtrees to project; None projects every field.

file_id: str | None = None#

Limits the read to the topic’s data in this file; None adds no file restriction.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

prefer: RepresentationPreference | None = None#

Per-subtree representation preference; None applies default selection everywhere.

schema_checksum: str | None = None#

Schema to use, by checksum, or None.

schema_id: str | None = None#

Schema to use, by id, or None to default to the sole in-window schema.

session_id: str | None = None#

Limits the read to the topic’s data in this Session’s files, each over the part of its time span the Session holds; None adds no session restriction.

start_time: int | None = None#

Inclusive window lower bound, absolute Unix-epoch nanoseconds.

May be None only when the request names a file_id, dataset_id or device_id; the bound then defaults to the earliest time of the topic’s data within the request’s restrictions, across every timeline source. An explicit bound narrows the read and never widens it.

timeline_source_id: str | None = None#

Timeline source to resolve partition extents with, by id, or None.

timeline_source_name: str | None = None#

Timeline source to resolve partition extents with, by name, or None.

class roboto.experimental.topics.ReadPlanScanTask(/, **data)#

Bases: pydantic.BaseModel

One file to open, with the topic to read from it and the format and transformations needed to interpret it.

Which of the representations satisfying the governing selector backs a scan task is service policy and may change between releases; only the selector’s hard-filter matching rule is contract.

Parameters:

data (Any)

format: roboto.domain.topics.RepresentationStorageFormat#

The format the bytes are stored in; selects the decoder a consumer applies.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

object: ReadPlanObjectRef#

The single file this scan task resolves to.

precedence: int#

Where two scan tasks’ subtrees contain the same field, the read takes it from the higher-precedence one.

subtree: ReadPlanFieldRef | None = None#

The field subtree this scan task covers; None covers the whole schema.

topic_name: str | None = None#

Name of the topic whose records this scan task reads from its file.

A file can hold several topics (an MCAP file does when its channels carry different topic names), so a reader selects the topic’s records by this name. None when the plan does not name the topic; a reader then reads the file as holding a single topic.

transformations: tuple[str, ...] = ()#

Transformations applied to produce this variant, in order; empty on the original.

class roboto.experimental.topics.ReadPlanSchemaRef(/, **data)#

Bases: pydantic.BaseModel

Identifies the single topic schema the plan uses.

Parameters:

data (Any)

checksum: str#

Checksum of the schema’s content. A consumer can cache the schema by this value.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

schema_id: str#

Identifier of the resolved topic schema.

class roboto.experimental.topics.ReadPlanTimestamp(/, **data)#

Bases: pydantic.BaseModel

Where a partition’s row timestamps come from.

Timestamps are either read out of a schema field (kind is "schema_field", and field names which one) or taken from the storage envelope (message log or publish time), in which case no schema field is involved and field is None.

Parameters:

data (Any)

field: ReadPlanFieldRef | None = None#

The schema field timestamps are read from; set exactly when kind is "schema_field".

kind: roboto.domain.topics.TimelineSourceKind#

from a schema field, or from the storage envelope.

Type:

How timestamps are sourced

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

unit: str | None = None#

Time unit of the designated field’s stored values (a TimeUnit value, e.g. "ms").

Only meaningful for a "schema_field" source, and only set when the schema declares the field’s unit. None when the schema does not record one; a consumer then treats non-self-describing values as nanoseconds, the unit the plan’s windows and offsets are recorded in. Envelope-derived timestamps (message log/publish time) are always assumed nanoseconds.

class roboto.experimental.topics.RepresentationOverride(/, **data)#

Bases: pydantic.BaseModel

Applies a representation selector to one field subtree, overriding the request default.

Parameters:

data (Any)

field: FieldAddress#

The subtree this override covers.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

selector: roboto.experimental.topics.record.RepresentationSelector#

The selector to apply within that subtree.

class roboto.experimental.topics.RepresentationPreference(/, **data)#

Bases: pydantic.BaseModel

Selects which stored variant of each field to read, per subtree.

A default selector applies to every field unless a more specific override covers it. Where several overrides cover a field, the one whose addressed subtree is the longest prefix of the field’s path wins; this rule is selector_for().

The governing selector and its matching rule are contract: a selector never substitutes a non-matching variant, and a read fails when a selector that sets any criterion is satisfied by no stored representation for a requested field — the plan never silently omits a field an explicit requirement covers. Which of the representations that satisfy the selector the service ultimately schedules is service policy and may change between releases.

Parameters:

data (Any)

default: roboto.experimental.topics.record.RepresentationSelector#

The selector applied to any field no override covers; matches anything when unset.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

overrides: tuple[RepresentationOverride, ...] = ()#

Per-subtree selector overrides, resolved longest-matching-prefix wins.

selector_for(field_path)#

Resolve the selector that governs the field at field_path, longest-matching-prefix wins.

An override applies when its addressed subtree path is a prefix of field_path; among applicable overrides the deepest subtree wins, and a field no override covers gets default.

Parameters:

field_path (tuple[str, ...]) – The path_in_schema components of the field whose selector is being resolved.

Returns:

The governing selector.

Raises:

ValueError – An override addresses its subtree by field_id. Resolving a field_id to a path takes the schema, which this value object does not hold; resolve every override address to its path form first.

Return type:

roboto.experimental.topics.record.RepresentationSelector

class roboto.experimental.topics.RepresentationRecord(/, **data)#

Bases: pydantic.BaseModel

One stored variant of a topic partition’s data, optionally narrowed to a subset of its fields.

A representation pairs a stored file with the data of a single topic partition. field_id narrows it to one field and the fields nested under it; None covers every field in the partition.

The same partition can have several representations that differ in storage_format, content_format, and transformations. A consumer picks the one whose attributes suit it: a viewer of image data, for example, may prefer a JPEG- or PNG-encoded variant over the untransformed original.

Parameters:

data (Any)

content_format: str | None = None#

The format of the data inside the stored file. For image data, this may be the image encoding (e.g. "jpeg", "png") on a transformed variant. None when unspecified.

created: datetime.datetime | None = None#
created_by: str#

Identity of the user who created this representation.

field_id: str | None = None#

The field this representation is narrowed to, covering that field and the fields nested under it. None when the representation covers every field in the partition.

fs_node_id: str#

Identifier of the file backing this representation.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

modified: datetime.datetime | None = None#
modified_by: str#
org_id: str#

Identifier of the org that owns this representation.

representation_id: str#

Unique identifier of this representation.

size_bytes: int | None = None#

Size in bytes of the file backing this representation, when known; None when the size is unavailable.

Populated on read-plan resolution so the plan can carry the backing file’s size onto its object refs. Write paths that upsert a representation leave it None.

storage_format: roboto.domain.topics.RepresentationStorageFormat#

Container the representation data is stored in (e.g. MCAP, Parquet).

topic_part_id: str#

Identifier of the topic partition this representation belongs to.

transformations: list[str]#

The transformations applied to the source data to produce this variant, in the order applied. Empty on the untransformed original.

Each entry is a "<kind>:<param>" string whose <kind> is a TransformationKind member, e.g. ["downsample:0.5", "encode:jpeg"].

class roboto.experimental.topics.RepresentationSelector(/, **data)#

Bases: pydantic.BaseModel

Selects which stored variant of a field to read when several are available.

A selector has three optional criteria — storage_format, content_format, and transformations — one for each attribute on which stored variants of the same field can differ (see RepresentationRecord). A criterion that is set is a requirement a variant must meet to be selected; a criterion left None places no requirement, and any value is acceptable.

A selector never falls back to a variant other than the one it describes. If any criterion is set and no stored variant of a requested field meets every requirement — whether the variants that exist all fall short, or the field has no stored variant at all — the read fails with an error rather than quietly leave out the field. Only under a selector with no criteria set is a field with no stored variant simply absent from the result; such a selector requires nothing, so nothing requested is missing.

Successor to roboto.domain.topics.RepresentationSelector, used by get_data().

Parameters:

data (Any)

content_format: str | None = None#

Required content encoding (e.g. "jpeg") by scalar equality; None does not constrain it.

There is no legacy carve-out: a representation whose content_format is None does not satisfy an explicit request.

matches(representation)#

Return whether representation satisfies every set axis of this selector.

Parameters:

representation (RepresentationRecord)

Return type:

bool

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod raw()#

Select the untransformed original (a representation with no transformations).

Return type:

RepresentationSelector

storage_format: roboto.domain.topics.RepresentationStorageFormat | None = None#

Required container (e.g. MCAP, Parquet) by scalar equality; None does not constrain it.

transformations: tuple[str, ...] | None = None#

Required transformations; None does not constrain, () requires the untransformed original.

A non-empty tuple is all-of: every token must be satisfied by some descriptor on the representation, which may carry additional transformations. A token is either a bare kind (e.g. "downsample"), satisfied by any descriptor whose kind prefix equals it, or a full "<kind>:<param>" descriptor (e.g. "encode:jpeg"), satisfied only by an exact match. The grammar is open string matching; the recognized vocabulary (TransformationKind) is enforced by the service at the request boundary, which rejects a token naming an unrecognized kind.

class roboto.experimental.topics.SessionContext(/, **data)#

Bases: pydantic.BaseModel

The Session a Topic is scoped to: limits topic operations to the Session’s associated files and supplies the Session’s aggregate time window as the default window for those operations.

Parameters:

data (Any)

end_time: int | None = None#

Latest time covered by the Session (Unix-epoch ns); the default end_time for get_data*. None when the Session includes no files.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

session_id: str#
start_time: int | None = None#

Earliest time covered by the Session (Unix-epoch ns); the default start_time for get_data*. None when the Session includes no files.

class roboto.experimental.topics.SetTopicUnixOffsetRequest(/, **data)#

Bases: pydantic.BaseModel

Request body for POST /v2/topics/id/<topic_id>/unix-offset.

Anchors one Session’s data on one topic to wall-clock time. See set_unix_offset() for the write’s reach.

Parameters:

data (Any)

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

session_id: str#

Session whose data is anchored. Its files decide which of the topic’s stored data the write reaches; the topic’s data in files outside the Session keeps the anchor it already has.

unix_epoch_offset_ns: roboto.time._EpochNanosecondsFromTime#

Wall-clock instant of stored time 0 for the Session’s data on this topic, in nanoseconds since the Unix epoch; each stored timestamp then reads as stored_time_ns + unix_epoch_offset_ns. Must fall after the Unix epoch, and must fit in the signed 64-bit integer the platform stores it in. Also accepts any roboto.time.Time at runtime, read as roboto.time.to_epoch_nanoseconds() reads it; convert with that function first to satisfy a type checker.

roboto.experimental.topics.TIMESTAMP_FIELD_METADATA_KEY = b'roboto.topic_data.timestamp'#

Arrow field-metadata key marking the per-row timestamp column of a topic-data batch.

The timestamp column holds this key with the value b"true" (MARKER_FIELD_METADATA_VALUE); a column holding the key with any other value is a value column.

class roboto.experimental.topics.TimeWindow(/, **data)#

Bases: pydantic.BaseModel

A closed time window in absolute nanoseconds since the Unix epoch; both bounds inclusive.

The same shape serves the window a plan resolves over and the window each partition’s rows are selected in.

Parameters:

data (Any)

end: int#

Inclusive upper bound, in nanoseconds.

model_config#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start: int#

Inclusive lower bound, in nanoseconds.

class roboto.experimental.topics.Topic(record, roboto_client=None, context=None)#

A logical stream of robotics data, identified durably across the files that carry it.

Within an organization, topic names are unique; contributions from different files with the same topic name share a single topic identity. By default a Topic reads org-wide and its data-returning methods require an explicit time window.

A Topic carrying a TopicContext instead scopes topic operations like get_data* to the topic’s data in that context, and never reads the topic’s data elsewhere.

A Topic’s context is fixed when it is built.

Parameters:
clear_unix_offset()#

Return this Topic’s data in its Session to an offset of 0.

Its stored timestamps are then read as nanoseconds since the Unix epoch with nothing added. This call reaches the same data as set_unix_offset(), and a file’s declared time range in the Session moves back under the same condition: only when everything that range covers moved by the same distance.

Returns:

One TimelineExtentRecord per timeline extent whose offset changed. An extent already at an offset of 0 is left untouched and absent from the list, so an empty list means there was no anchor to clear.

Raises:

  • ValueError – This Topic carries no Session context; see set_unix_offset().

  • RobotoInvalidRequestException – Some of this Topic’s own timestamps are negative, so an offset of 0 would place that data before the Unix epoch; or a time range declared over the data would start before the epoch once moved back with it.

  • RobotoConflictException – A concurrent writer added files to the Session while the offset was being cleared; retry the call.

  • RobotoNotFoundException – The Session does not exist, or holds no data for this Topic.

  • RobotoUnauthorizedException – The caller cannot view the Session, or cannot edit some file behind the data being written.

Return type:

list[roboto.domain.topics.TimelineExtentRecord]

Examples

>>> from roboto.experimental.sessions import Session
>>> session = Session.from_id("se_abc123")  
>>> topic = session.get_topic("/camera/image_raw")  
>>> topic.clear_unix_offset()
classmethod from_id(topic_id, roboto_client=None, context=None)#

Load an existing topic by its id.

Parameters:

  • topic_id (str) – Identifier of the topic (ti_*).

  • roboto_client (Optional[roboto.http.RobotoClient]) – Roboto client instance. Uses the default if omitted.

  • context (Optional[TopicContext]) – Optional. When provided, limits topic operations to the topic’s data in one Session, file, dataset or device, and supplies the default read window, as described on Topic. None reads org-wide.

Returns:

The loaded topic.

Raises:
Return type:

Topic

Examples

>>> from roboto.experimental.topics import Topic
>>> topic = Topic.from_id("ti_abc123")
>>> topic.name
'/camera/image_raw'

Read all of the topic’s data in one file:

>>> from roboto.experimental.topics import FileContext
>>> file_topic = Topic.from_id("ti_abc123", context=FileContext(file_id="fl_abc123"))
>>> rows = list(file_topic.get_data())
classmethod from_record(record, roboto_client=None, context=None)#

Wrap an already-loaded topic identity record.

Parameters:
Returns:

A topic backed by record, with no further service calls.

Return type:

Topic

Examples

>>> from roboto.experimental.topics import Topic
>>> topic = Topic.from_record(record)
>>> topic.topic_id  
'ti_abc123'
get_data(start_time=None, end_time=None, fields_include=None, fields_exclude=None, prefer=None, schema_id=None, schema_checksum=None, timeline_source_id=None, timeline_source_name=None, cache_policy=CachePolicy.ADAPTIVE, cache_dir=None)#

Yield this topic’s data within a time window, as (timestamp, record) pairs.

Convenience over get_data_as_record_batches() that unpacks each Arrow RecordBatch into one (timestamp, record) tuple per row. timestamp is the row’s absolute Unix-epoch nanosecond timestamp (an int); record is a dict of the projected fields, with struct fields as nested dicts and list fields as lists. A field the data omits for a row is absent from (or null within) that row’s dict.

Time windowing, field projection, representation selection, sort order, and error behavior are all as documented on get_data_as_record_batches().

Requires the roboto[analytics] extra.

Parameters:
Yields:

(timestamp, record) tuples for the in-window rows, filtered and projected per the arguments.

Raises:
Return type:

collections.abc.Generator[tuple[roboto.domain.topics.Timestamp, dict[str, Any]], None, None]

Examples

>>> from roboto.experimental.topics import Topic
>>> topic = Topic.from_id("ti_abc123")
>>> for timestamp, record in topic.get_data(start_time=t0, end_time=t1):
...     print(timestamp, record)
get_data_as_df(start_time=None, end_time=None, fields_include=None, fields_exclude=None, prefer=None, schema_id=None, schema_checksum=None, timeline_source_id=None, timeline_source_name=None, flatten=False, cache_policy=CachePolicy.ADAPTIVE, cache_dir=None)#

Return this topic’s data within a time window as a pandas DataFrame.

Same pipeline as get_data_as_record_batches(), with the batches packed into a DataFrame whose index is a timezone-aware DatetimeIndex.

Rows return ordered by partition (each file’s data in start order), not interleaved across partitions; within a partition rows keep their stored order. Call df.sort_index() for a strict row-level time-ordered view.

A struct field is returned as a single schema-shaped column of dicts unless flatten is set, which expands every struct level into dot-delimited leaf columns (e.g. pose.position.x). List-typed fields are unaffected by flatten.

Read parameters and error behavior are as documented on get_data_as_record_batches().

Requires the roboto[analytics] extra.

Parameters:
Returns:

DataFrame of the in-window rows indexed by a timezone-aware DatetimeIndex.

Raises:
Return type:

pandas.DataFrame

Examples

>>> from roboto.experimental.topics import Topic
>>> topic = Topic.from_id("ti_abc123")
>>> df = topic.get_data_as_df(start_time=t0, end_time=t1)
get_data_as_record_batches(start_time=None, end_time=None, fields_include=None, fields_exclude=None, prefer=None, schema_id=None, schema_checksum=None, timeline_source_id=None, timeline_source_name=None, cache_policy=CachePolicy.ADAPTIVE, cache_dir=None)#

Yield this topic’s data within a time window, as Arrow RecordBatches.

Each batch carries one column per top-level projected field, with nested struct and list types mirroring the topic’s schema, pruned to the projection, plus a dedicated int64 column of Unix-epoch nanosecond timestamps; locate that column with timestamp_column_index(). A field the data omits for a row surfaces as null at the deepest level that represents the omission (a whole absent subtree is a single null).

Batch sizes and boundaries carry no meaning, and a window matching no rows yields no batches, as does a context holding no data for the topic. A topic’s data can span several files (“topic partitions”); rows from different partitions are never mixed within a batch. Partitions arrive ordered by when each file’s data begins. Within a partition, rows keep their stored order, and rows from different partitions are never interleaved. So batches arrive as whole partitions in start order, not as a globally time-sorted row stream. Sort downstream if a strict row-level time order is needed.

Requires the roboto[analytics] extra.

Parameters:

  • start_time (Optional[roboto.time.Time]) – Inclusive window lower bound, as nanoseconds since the Unix epoch or anything convertible via to_epoch_nanoseconds(). None defaults to the start of the topic’s data in this topic’s file, dataset or device context, or to the earliest time the Session covers in a SessionContext (such as on a topic obtained from list_topics() or get_topic()); otherwise required (a ValueError is raised when it cannot be resolved). An explicit bound narrows the read within the context and never widens it.

  • end_time (Optional[roboto.time.Time]) – Inclusive window upper bound, same forms as start_time; defaults to the end of the topic’s data, or to the latest time the Session covers, on the same terms.

  • fields_include (Optional[collections.abc.Iterable[FieldAddressLike]]) – Field subtrees to project. None projects every field.

  • fields_exclude (Optional[collections.abc.Iterable[FieldAddressLike]]) – Field subtrees to drop from the projection. None drops none.

  • prefer (Optional[roboto.experimental.topics.operations.RepresentationPreference]) – Preferred representation per field subtree, selecting which stored variant of a field to read. None applies the default selection everywhere.

  • schema_id (Optional[str]) – Schema to read under, by id. Required only when the window spans data with more than one schema.

  • schema_checksum (Optional[str]) – Schema to read under, by checksum. Mutually exclusive with schema_id.

  • timeline_source_id (Optional[str]) – Timeline source to resolve the window with, by id. None uses each schema’s default source.

  • timeline_source_name (Optional[str]) – Timeline source by name. Mutually exclusive with timeline_source_id.

  • cache_policy (roboto.storage.CachePolicy) – Whether fetched Parquet files are cached to local disk. MCAP data always streams.

  • cache_dir (Union[str, pathlib.Path, None]) – Directory topic data files are cached under. Defaults to a topic-data subdirectory of ROBOTO_CACHE_DIR, or the platform-conventional per-user cache directory when that is unset.

Yields:

pyarrow.RecordBatch instances holding the in-window rows, filtered and projected per the arguments.

Raises:

  • RobotoNotFoundException – This topic’s file or dataset context names a file or dataset that does not exist.

  • RobotoInvalidRequestException – The window spans multiple schemas and none was chosen with schema_id or schema_checksum, a named schema or timeline source does not match the window’s data, no stored representation satisfies a representation preference, or fields_include and fields_exclude together select no field. The error carries an actionable message.

  • RobotoReadPlanExecutionException –

    The topic’s data cannot be read as the service’s read plan describes it; a RobotoInternalException whose kind says why:

    • field-not-in-file: a file backing this topic lacks a field the read takes from it, such as a projected field or the field holding each row’s timestamp. field_path runs from that field’s top-level field down to the first component the file lacks.

    • unsupported-timestamp: the read cannot take timestamps from where the data keeps them, such as a message time on a Parquet file, or a Parquet timestamp field that is not a number.

    • invalid-timestamp: a stored timestamp is NaN or infinite, is a DECIMAL holding a fraction of a nanosecond, or leaves the signed 64-bit range once shifted to absolute time. row_number names the row by its 0-based position among the topic’s rows in its file.

    • scan-task-row-mismatch: files that store different fields of the same rows hold different rows in the window.

    • field-split-inside-non-struct: files that store different fields of the same rows split a field that one of them stores as other than a struct, such as a list or a map.

    • projected-field-in-no-scan-task: no scan task of a topic partition reads the whole schema or a subtree containing a projected field.

    • inconsistent-scan-tasks-on-file: the plan reads one file two ways, with a different format, transformations or topic name.

    • partition-schema-mismatch: a topic partition’s files give the read a different schema than the first topic partition’s files. It is raised when that partition’s files open, before any of its rows, so even when it has no rows in the window.

    • plan-without-schema: the plan reads every field of its schema and has a topic partition with a scan task, but names no schema.

    • data-range-not-in-file: a topic partition’s declared slice of its file (data_range) ends past the file’s stored row count, so the slice does not match the file.

  • RobotoInternalException – An MCAP file backing this topic cannot be decoded, such as one with no chunk or message index, or one whose messages are protobuf-encoded.

  • RobotoUnauthorizedException – The caller lacks read access to at least one in-window file backing this topic, or cannot view the file or dataset this topic’s context names.

Return type:

collections.abc.Generator[pyarrow.RecordBatch, None, None]

Examples

Print every record in a window:

>>> from roboto.experimental.topics import Topic
>>> topic = Topic.from_id("ti_abc123")
>>> for batch in topic.get_data_as_record_batches(start_time=t0, end_time=t1):
...     print(batch.num_rows, batch.schema.names)

Project to one field subtree, dropping one of its children:

>>> for batch in topic.get_data_as_record_batches(
...     start_time=t0,
...     end_time=t1,
...     fields_include=[("angular_velocity",)],
...     fields_exclude=[("angular_velocity", "y")],
... ):
...     print(batch.to_pylist())
property name: str#

Human-readable topic name (e.g. "/camera/image_raw"). Unique within an organization.

Return type:

str

property org_id: str#

Identifier of the organization that owns this topic.

Return type:

str

property record: roboto.domain.topics.TopicIdentityRecord#

The underlying topic identity record.

Return type:

roboto.domain.topics.TopicIdentityRecord

set_unix_offset(anchor)#

Anchor this Topic’s data in its Session to wall-clock time.

anchor is the wall-clock instant at which stored time 0 of that data occurred. Converted to nanoseconds since the Unix epoch, it is added to the stored timestamps of every part of this Topic the Session holds, so each timestamp reads as wall-clock time. Use this call for a Session whose data all starts from one time 0 but is stored apart: a topic chunked across several files, or several Sessions packed into slices of one shared file where this Session holds one of the slices. Every part moves in one transaction, so a failure leaves every one of them at the offset it already had.

This call covers one Topic within one Session. To anchor everything in a Session, use set_unix_offset(); to anchor a whole file regardless of Session, use set_timeline_offset(); to anchor exactly the slice a declaration names, supply that declaration’s anchor_ns at ingest.

Two consequences:

  1. The data written is shared, not copied. Any other Session holding the same data reads the same anchor.

  2. A file’s declared time range in the Session moves only when everything that range covers moved by the same distance. A file whose range also covers another topic’s data, which this call leaves alone, keeps the range it has.

Parameters:

anchor (roboto.time.Time) – Wall-clock instant of stored time 0: an int of nanoseconds since the Unix epoch, or any other Time, read as to_epoch_nanoseconds() reads it (a datetime or ISO 8601 string is that instant; a float, Decimal, or numeric string is seconds since the epoch). Must fall after the Unix epoch: zero is not an anchor (use clear_unix_offset() to return this Topic’s data in the Session to an offset of 0), and earlier instants are rejected.

Returns:

One TimelineExtentRecord per timeline extent whose offset changed. An extent already anchored at anchor is left untouched and absent from the list, so an empty list means the anchor was already in place.

Raises:

  • ValueError – This Topic carries no Session context, so there is no way to tell which of the Topic’s data is meant; reach it through get_topic() or list_topics().

  • TypeError – anchor is not one of the Time types.

  • ValueError – anchor is a boolean, a string that is neither seconds nor ISO 8601, zero, before the Unix epoch, or too large for a signed 64-bit integer of nanoseconds; rejected client-side, before any request is made. A range refusal is raised as pydantic.ValidationError, a subclass of ValueError.

  • RobotoInvalidRequestException – The anchor would move this Topic’s data, or a time range declared over it, before the Unix epoch or past the largest storable Unix-epoch nanosecond value; anchor the data at the instant it was recorded.

  • RobotoConflictException – A concurrent writer added files to the Session while the anchor was being applied; retry the call.

  • RobotoNotFoundException – The Session does not exist, or holds no data for this Topic.

  • RobotoUnauthorizedException – The caller cannot view the Session, or cannot edit some file behind the data being written.

Return type:

list[roboto.domain.topics.TimelineExtentRecord]

Examples

>>> from roboto.experimental.sessions import Session
>>> session = Session.from_id("se_abc123")  
>>> topic = session.get_topic("/camera/image_raw")  
>>> topic.set_unix_offset(1_700_000_000_000_000_000)

The same anchor given as a datetime:

>>> import datetime
>>> topic.set_unix_offset(
...     datetime.datetime(2023, 11, 14, 22, 13, 20, tzinfo=datetime.timezone.utc)
... )
property topic_id: str#

Durable identifier of this topic (ti_*).

Return type:

str

roboto.experimental.topics.TopicContext#

one Session, file, dataset or device.

Type:

The scope a Topic reads within

roboto.experimental.topics.timestamp_column_index(schema)#

Locate the timestamp column: the field whose TIMESTAMP_FIELD_METADATA_KEY metadata is b"true".

The column is identified by metadata, never by name: a projected root field can legitimately carry any name, including the timestamp column’s conventional one.

Raises:

RobotoInternalException – The schema does not contain exactly one marked column.

Parameters:

schema (pyarrow.Schema)

Return type:

int

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