roboto.domain.files.file#

Module Contents#

class roboto.domain.files.file.File(record, roboto_client=None, file_service=None)#

Represents a file within the Roboto platform.

Files are the fundamental data storage unit in Roboto. They can be uploaded to datasets, imported from external sources, or created as outputs from actions. Once in the platform, files can be tagged with metadata, post-processed by actions, added to collections, visualized in the web interface, and searched using the query system.

Files contain structured data that can be ingested into topics for analysis and visualization. Common file formats include ROS bags, MCAP files, ULOG files, CSV files, and many others. Each file has an associated ingestion status that tracks whether its data has been processed and made available for querying.

Files are versioned entities - each modification creates a new version while preserving the history. Files are associated with datasets and inherit access permissions from their parent dataset.

The File class provides methods for downloading, updating metadata, managing tags, accessing topics, and performing other file operations. It serves as the primary interface for file manipulation in the Roboto SDK.

Parameters:
add_topic(topic_name, df, timestamp_column=None, timestamp_unit=None)#

Create a Topic from a pandas DataFrame and associate it with this file.

If a topic with the same name already exists for this file, it will be updated with the new data and schema.

Parameters:

  • topic_name (str) – Name for the topic. Must be unique within this file.

  • df (pandas.DataFrame) – pandas DataFrame containing the data to ingest. Must include a timestamp column (either explicitly specified or automatically detectable).

  • timestamp_column (Optional[str]) – Name of the column to use as the timestamp. If not provided, the method will attempt to automatically detect a timestamp column by looking for the first column that is a timezone-aware timestamp type.

  • timestamp_unit (Optional[Union[str, roboto.time.TimeUnit]]) – Unit of the timestamp column values. Required when timestamp_column contains numeric values (int, float, decimal). Valid values include “s”, “ms”, “us”, “ns”. Not needed for datetime columns or when timestamp_column is not specified.

Returns:

The created or updated Topic instance.

Raises:

  • IngestionException – If the timestamp column cannot be determined, is not present in the DataFrame, has an invalid type, or if the timestamp unit is required but not provided.

  • ImportError – If pandas or pyarrow are not installed. Install with pip install roboto[ingestion] to use this feature.

  • RobotoIllegalArgumentException – This file is associated with a device or with the org itself, not with a dataset; only a dataset’s files hold topics.

  • RobotoUnauthorizedException – If the caller lacks permission to create topics or upload files to this file’s dataset.

Return type:

roboto.domain.topics.Topic

Notes

  • Requires installing this package using the roboto[ingestion] extra

  • Topic names are unique within a file

  • Schema and statistics are automatically inferred from the DataFrame

Examples

Create a topic with explicit timestamp column and unit:

>>> import pandas as pd
>>> from roboto import File
>>> file = File.from_id("file_abc123")
>>> df = pd.DataFrame(
...     {
...         "timestamp": [1763947309.4198897, 1763947316.7686195, 1763947335.0095527],
...         "temperature": [20.5, 21.0, 20.8],
...         "humidity": [45.2, 46.1, 45.8],
...     }
... )
>>> topic = file.add_topic(
...     topic_name="sensor_data", df=df, timestamp_column="timestamp", timestamp_unit="s"
... )
>>> print(f"Created topic: {topic.name}")
Created topic: sensor_data

Create a topic with automatic timestamp detection:

>>> import pandas as pd
>>> from roboto import File
>>> file = File.from_id("file_abc123")
>>> df = pd.DataFrame(
...     {
...         "ts": pd.date_range("2025-11-24", periods=3, freq="1s", tz="UTC"),
...         "velocity": [10.5, 11.2, 10.8],
...         "acceleration": [0.5, 0.3, -0.2],
...     }
... )
>>> topic = file.add_topic("motion_data", df)

Retrieve the data back

>>> retrieved_df = topic.get_data_as_df()
>>> print(f"Retrieved {len(retrieved_df)} rows")
Retrieved 3 rows

Add derived data as a new topic to the same file, using the original topic’s timestamp index:

>>> import pandas as pd
>>> from roboto import File
>>> file = File.from_id("file_abc123")
>>> # Get existing topic data as DataFrame
>>> original_topic = file.get_topic("sensor_data")
>>> original_df = original_topic.get_data_as_df()
>>> # Create derived data
>>> derived_df = pd.DataFrame(
...     {
...         "temp_category": original_df["temperature"].apply(lambda x: "hot" if x > 25 else "not_hot"),
...     },
...     index=original_df.index,
... )
>>> derived_topic = file.add_topic(
...     "temperature_categories",
...     derived_df,
... )
property association: roboto.association.Association#

The dataset, device, device type, or org this file is associated with.

Every file has exactly one association, inferred from the prefix of its association ID. Read file.association.association_type to branch on it.

Return type:

roboto.association.Association

property created: datetime.datetime#

Timestamp when this file was created.

Returns the UTC datetime when this file was first uploaded or created in the Roboto platform. This timestamp is immutable.

Return type:

datetime.datetime

property created_by: str#

Identifier of the user who created this file.

Returns the user ID or identifier of the person or service that originally uploaded or created this file in the Roboto platform.

Return type:

str

property dataset_id: str#

Identifier of the dataset that contains this file.

Valid only for a file associated with a dataset; files associated with a device or with the org itself have no dataset. Prefer association, which works for every file.

Raises:

RobotoIllegalArgumentException – This file is not associated with a dataset.

Return type:

str

declare_topic(topic_name, topic_schema, timeline_sources, data_range=None, anchor=None, representations=())#

Register one topic this File contributes data to, without naming a Session.

The singular form of declare_topics(), taking the fields of one FileTopicDeclaration as separate arguments. That class documents what each field means; declare_topics() documents what the platform does with it.

Parameters:

  • topic_name (str) – Topic this File contributes data to. Topic names are unique within an org.

  • topic_schema (roboto.experimental.ingest.Schema) – Structure of the topic’s data.

  • timeline_sources (collections.abc.Sequence[roboto.experimental.ingest.DeclaredTimelineSource]) – Timeline sources this File’s topic data carries, each with the bounds it spans in this File, stated in the File’s own timestamps.

  • data_range (Optional[tuple[int, int]]) – The part of the File this topic’s data occupies, or None for the whole File.

  • anchor (Optional[roboto.time.Time]) – Optional wall-clock instant the data this declaration names was captured at: 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.

  • representations (collections.abc.Sequence[roboto.experimental.ingest.RepresentationDeclaration]) – The files a read of this topic’s data opens, each with how it holds that data: this File, when its own bytes are readable, and other files when the data is read from them, such as files converted out of it. Empty lists none, and reads of a topic with no representations return no rows.

Returns:

The topic this declaration registered against.

Raises:

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

  • ValueError – If anchor is a boolean, a negative number (an int, float, Decimal, or numeric string), or a string that is neither a number of seconds nor an ISO 8601 timestamp. Raised before anything is sent to the platform.

  • OverflowError – If anchor is an infinite float, Decimal, or string, such as "inf". Raised before anything is sent to the platform.

  • pydantic.ValidationError – If these arguments do not form a valid FileTopicDeclaration, for instance two representations of the whole topic, or of one field, sharing a storage format, content format and transformations, or any timeline source but SchemaFieldSource beside a PARQUET representation, or if representations names one file in two storage formats. Enforced before anything is sent to the platform.

  • RobotoDomainException – Whatever the platform refused this declaration with.

Return type:

roboto.experimental.topics.Topic

Examples

>>> from roboto.domain.files import File
>>> from roboto.domain.topics import CanonicalDataType, RepresentationStorageFormat
>>> from roboto.experimental.ingest import Field, RepresentationDeclaration, Schema, SchemaFieldSource
>>> timestamp = Field(
...     name="timestamp",
...     data_type="float64",
...     canonical_data_type=CanonicalDataType.Timestamp,
...     unit="s",
... )
>>> file = File.from_id("fl_0123456789ab")
>>> topic = file.declare_topic(
...     topic_name="observation.state",
...     topic_schema=Schema(
...         name="observation.state",
...         fields=[timestamp, Field(name="observation.state", data_type="float32")],
...     ),
...     timeline_sources=[
...         SchemaFieldSource(
...             field_path=["timestamp"],
...             min_file_timestamp_ns=0,
...             max_file_timestamp_ns=4_000,
...         )
...     ],
...     representations=[
...         RepresentationDeclaration(
...             file_id=file.file_id,
...             storage_format=RepresentationStorageFormat.PARQUET,
...         )
...     ],
... )
>>> print(topic.topic_id)
declare_topics(topics)#

Register the topic data this File carries, without naming a Session.

One call states everything the platform needs to serve this File’s topic data: for each topic, the structure of its rows, the timeline sources those rows carry with the bounds they span in this File, and, when the File packs its data into slices, which slice the topic occupies. The platform does not open the File when topics are declared on it, so this declaration is all it knows about the File’s contents.

The platform applies each declaration on its own: one it refuses leaves the others registered, and the response says what became of each. Nothing about the call involves a Session, so declarations on the Files of one recording can run concurrently, and a File’s topic data can be registered before the Session holding it exists. A Session takes that data on by attaching the File, through add_file() or a SessionFile carrying no topics. A Session already holding the File takes on what this call declares before the call returns, with its time bounds recomputed to cover the newly declared data.

Resending the same call is safe: the platform identifies the data a declaration registers by the topic plus the slice of the File that declaration names, so a resend converges on what the first attempt registered rather than duplicating it, and a corrected redeclaration replaces what it corrects.

A declaration states its bounds in the File’s own timestamps, read as nanoseconds since the Unix epoch; the platform never invents a wall-clock time. Data whose timestamps start at 0 therefore sits at the epoch until it is anchored. To place it at the wall-clock time it was captured, supply anchor_ns, which anchors the whole slice it names rather than the one topic declaring it, so the topics sharing a slice must agree on it.

The topic data belongs to this File: its bounds, anchors and slices are stated against it, and a Session holding this File holds the data. What makes the data readable is each topic’s representations: the files a read opens to get it, each decoded in the storage format its representation states. A file’s name and extension are not used. A topic lists this File when its own bytes are readable, and other files when the data is read from them, such as the per-topic MCAPs converted out of a PX4 ULog. A topic with no representations is still registered and still counts toward the bounds of the Sessions holding the File, but reads of it return no rows. RepresentationDeclaration states what a representation’s file must hold and when a read can decode an MCAP representation’s file.

Redeclaring a topic adds the representations listed to the ones it has, each taking the place of the stored ones it matches, as representations describes. To remove a representation, or to replace a topic’s representations outright, use set_representations().

Parameters:

topics (collections.abc.Sequence[roboto.experimental.ingest.FileTopicDeclaration]) – One declaration per topic and slice of this File. An empty sequence returns an empty response without contacting the platform.

Returns:

One element per declaration, in request order, holding either the topic it registered against or why the platform refused it.

Raises:

  • pydantic.ValidationError – If more than MAX_FILES_AND_TOPICS_PER_REQUEST topics are given, one topic is declared twice over the same slice, two topics anchor one slice at different instants, or representations name one file in two storage formats. These are enforced when the request body is constructed, before anything is sent to the platform; each declaration’s own rules are enforced earlier, when the caller builds it.

  • RobotoNotFoundException – If this File no longer exists, or a representation names a file that does not exist in this File’s org or whose status is not Available. Nothing is registered.

  • RobotoUnauthorizedException – If the caller lacks edit access to this File or to a file a listed representation names, or lacks topic edit access in this File’s org while a declaration states is_default_for_reads on a timeline source.

Return type:

roboto.http.BatchResponse[roboto.experimental.topics.Topic]

Examples

Register the two topics a LeRobot episode file carries, each read from the file’s own bytes:

>>> from roboto.domain.files import File
>>> from roboto.domain.topics import CanonicalDataType, RepresentationStorageFormat
>>> from roboto.experimental.ingest import (
...     Field,
...     FileTopicDeclaration,
...     RepresentationDeclaration,
...     Schema,
...     SchemaFieldSource,
... )
>>> timestamp = Field(
...     name="timestamp",
...     data_type="float64",
...     canonical_data_type=CanonicalDataType.Timestamp,
...     unit="s",
... )
>>> file = File.from_id("fl_0123456789ab")
>>> from_file = RepresentationDeclaration(
...     file_id=file.file_id,
...     storage_format=RepresentationStorageFormat.PARQUET,
... )
>>> registered = file.declare_topics(
...     [
...         FileTopicDeclaration(
...             topic_name="observation.state",
...             topic_schema=Schema(
...                 name="observation.state",
...                 fields=[timestamp, Field(name="observation.state", data_type="float32")],
...             ),
...             timeline_sources=[
...                 SchemaFieldSource(
...                     field_path=["timestamp"],
...                     min_file_timestamp_ns=0,
...                     max_file_timestamp_ns=4_000,
...                 )
...             ],
...             representations=[from_file],
...         ),
...         FileTopicDeclaration(
...             topic_name="action",
...             topic_schema=Schema(
...                 name="action",
...                 fields=[timestamp, Field(name="action", data_type="float32")],
...             ),
...             timeline_sources=[
...                 SchemaFieldSource(
...                     field_path=["timestamp"],
...                     min_file_timestamp_ns=0,
...                     max_file_timestamp_ns=4_000,
...                 )
...             ],
...             representations=[from_file],
...         ),
...     ],
... )
>>> print([topic.topic_id for topic in registered.succeeded])

Register a topic over the slice of a shared file that holds one episode, anchored at the instant that episode was recorded:

>>> registered = file.declare_topics(
...     [
...         FileTopicDeclaration(
...             topic_name="observation.state",
...             topic_schema=Schema(
...                 name="observation.state",
...                 fields=[timestamp, Field(name="observation.state", data_type="float32")],
...             ),
...             timeline_sources=[
...                 SchemaFieldSource(
...                     field_path=["timestamp"],
...                     min_file_timestamp_ns=0,
...                     max_file_timestamp_ns=4_000,
...                 )
...             ],
...             data_range=(0, 80),
...             anchor_ns=1_785_974_400_000_000_000,
...             representations=[from_file],
...         ),
...     ],
... )
delete()#

Delete this file from the Roboto platform.

Permanently removes the file and all its associated data, including topics and metadata. This operation cannot be undone.

For files that were imported from customer S3 buckets (read-only BYOB integrations), this method does not delete the file content from S3. It only removes the metadata and references within the Roboto platform.

Raises:
Return type:

None

Examples

>>> file = File.from_id("file_abc123")
>>> file.delete()
# File is now permanently deleted
property description: str | None#

Human-readable description of this file.

Returns the optional description text that provides details about the file’s contents, purpose, or context. Can be None if no description was provided.

Return type:

Optional[str]

property device_id: str | None#

Identifier of the device that generated this data.

Returns the optional identifier of the device that generated the data contained within this file. Can be None if the file was not generated by a device.

Return type:

Optional[str]

download(local_path, print_progress=True)#

Download this file to a local path.

Downloads the file content from cloud storage to the specified local path. The parent directories are created automatically if they don’t exist.

For a link, downloads the version of the target file that the link pins.

Parameters:

  • local_path (pathlib.Path) – Local filesystem path where the file should be saved.

  • print_progress (bool) – Whether to show a progress bar during download.

Raises:

  • RobotoNotFoundException – This file is a link whose target, at the pinned version, no longer exists.

  • RobotoUnauthorizedException – Caller lacks permission to download the file, or a link’s target.

  • FileNotFoundError – File content is not available in storage.

Examples

>>> import pathlib
>>> file = File.from_id("file_abc123")
>>> local_path = pathlib.Path("/tmp/downloaded_file.bag")
>>> file.download(local_path)
>>> print(f"Downloaded to {local_path}")
property file_id: str#

Unique identifier for this file.

Returns the globally unique identifier assigned to this file when it was created. This ID is immutable and used to reference the file across the Roboto platform.

Return type:

str

classmethod from_id(file_id, version_id=None, roboto_client=None)#

Create a File instance from a file ID.

Retrieves file information from the Roboto platform using the provided file ID and optionally a specific version.

Parameters:

  • file_id (str) – Unique identifier for the file.

  • version_id (Optional[int]) – Specific version of the file to retrieve. If None, gets the latest version.

  • roboto_client (Optional[roboto.http.RobotoClient]) – HTTP client for API communication. If None, uses the default client.

Returns:

File instance representing the requested file.

Raises:
Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> print(file.relative_path)
'data/sensor_logs.bag'

>>> old_version = File.from_id("file_abc123", version_id=1)
>>> print(old_version.version)
1
classmethod from_path_and_dataset_id(file_path, dataset_id, version_id=None, roboto_client=None)#

Create a File instance from a file path and dataset ID.

Retrieves file information using the file’s relative path within a specific dataset. This is useful when you know the file’s location within a dataset but not its file ID.

Parameters:

  • file_path (Union[str, pathlib.Path]) – Relative path of the file within the dataset.

  • dataset_id (str) – ID of the dataset containing the file.

  • version_id (Optional[int]) – Specific version of the file to retrieve. If None, gets the latest version.

  • roboto_client (Optional[roboto.http.RobotoClient]) – HTTP client for API communication. If None, uses the default client.

Returns:

File instance representing the requested file.

Raises:
Return type:

File

Examples

>>> file = File.from_path_and_dataset_id("logs/session1.bag", "ds_abc123")
>>> print(file.file_id)
'file_xyz789'

>>> file = File.from_path_and_dataset_id(pathlib.Path("data/sensors.csv"), "ds_abc123")
>>> print(file.relative_path)
'data/sensors.csv'
get_signed_url(override_content_type=None, override_content_disposition=None)#

Generate a signed URL for direct access to this file.

Creates a time-limited URL that allows direct access to the file content without requiring Roboto authentication. Useful for sharing files or integrating with external systems.

Parameters:

  • override_content_type (Optional[str]) – Custom MIME type to set in the response headers.

  • override_content_disposition (Optional[str]) – Custom content disposition header value (e.g., “attachment; filename=myfile.bag”).

Return type:

str

For a link, the URL is for the version of the target file that the link pins.

Returns:

Signed URL string that provides temporary access to the file.

Raises:
Parameters:

  • override_content_type (Optional[str])

  • override_content_disposition (Optional[str])

Return type:

str

Examples

>>> file = File.from_id("file_abc123")
>>> url = file.get_signed_url()
>>> print(f"Direct access URL: {url}")

>>> # Force download with custom filename
>>> download_url = file.get_signed_url(override_content_disposition="attachment; filename=data.bag")
get_topic(topic_name)#

Get a specific topic from this file by name.

Retrieves a topic with the specified name that is associated with this file. Topics contain the structured data extracted from the file during ingestion.

Parameters:

topic_name (str) – Name of the topic to retrieve (e.g., “/camera/image”, “/imu/data”).

Returns:

Topic instance for the specified topic name.

Raises:
Return type:

roboto.domain.topics.Topic

Examples

>>> file = File.from_id("file_abc123")
>>> camera_topic = file.get_topic("/camera/image")
>>> print(f"Topic schema: {camera_topic.schema}")

>>> # Access topic data
>>> for record in camera_topic.get_data():
...     print(f"Timestamp: {record['timestamp']}")
get_topics(include=None, exclude=None)#

Get all topics associated with this file, with optional filtering.

Retrieves all topics that were extracted from this file during ingestion. Topics can be filtered by name using include/exclude patterns.

Parameters:

  • include (Optional[collections.abc.Sequence[str]]) – If provided, only topics with names in this sequence are yielded.

  • exclude (Optional[collections.abc.Sequence[str]]) – If provided, topics with names in this sequence are skipped.

Yields:

Topic instances associated with this file, filtered according to the parameters.

Return type:

collections.abc.Generator[roboto.domain.topics.Topic, None, None]

Examples

>>> file = File.from_id("file_abc123")
>>> for topic in file.get_topics():
...     print(f"Topic: {topic.name}")
Topic: /camera/image
Topic: /imu/data
Topic: /gps/fix

>>> # Only get camera topics
>>> camera_topics = list(file.get_topics(include=["/camera/image", "/camera/info"]))
>>> print(f"Found {len(camera_topics)} camera topics")

>>> # Exclude diagnostic topics
>>> data_topics = list(file.get_topics(exclude=["/diagnostics"]))
classmethod import_batch(requests, roboto_client=None, caller_org_id=None)#

Import files from customer S3 bring-your-own buckets into Roboto datasets.

This is the ingress point for importing data stored in customer-owned S3 buckets that have been registered as read-only bring-your-own bucket (BYOB) integrations with Roboto. Files remain in their original S3 locations while metadata is registered with Roboto for discovery, processing, and analysis.

This method only works with S3 URIs from buckets that have been properly registered as BYOB integrations for your organization. It performs batch operations to efficiently import multiple files in a single API call, reducing overhead and improving performance.

Parameters:

  • requests (collections.abc.Sequence[roboto.domain.files.operations.ImportFileRequest]) – Sequence of import requests, each specifying file details and metadata.

  • roboto_client (Optional[roboto.http.RobotoClient]) – HTTP client for API communication. If None, uses the default client.

  • caller_org_id (Optional[str]) – Organization ID of the caller. Required for multi-org users.

Returns:

Sequence of File objects representing the imported files.

Raises:

  • RobotoInvalidRequestException – If any URI is not a valid S3 URI, if the batch exceeds 500 items, or if bucket integrations are not properly configured.

  • RobotoUnauthorizedException – If the caller lacks upload permissions for target datasets or if buckets don’t belong to the caller’s organization.

Return type:

collections.abc.Sequence[File]

Notes

  • Only works with S3 URIs from registered read-only BYOB integrations

  • Files are not copied; only metadata is imported into Roboto

  • Batch size is limited to 500 items per request

  • All S3 buckets must be registered to the caller’s organization

Examples

>>> from roboto.domain.files import ImportFileRequest
>>> requests = [
...     ImportFileRequest(
...         dataset_id="ds_abc123",
...         relative_path="logs/session1.bag",
...         uri="s3://my-bucket/data/session1.bag",
...         size=1024000,
...     ),
...     ImportFileRequest(
...         dataset_id="ds_abc123",
...         relative_path="logs/session2.bag",
...         uri="s3://my-bucket/data/session2.bag",
...         size=2048000,
...     ),
... ]
>>> files = File.import_batch(requests)
>>> print(f"Imported {len(files)} files")
Imported 2 files
classmethod import_one(dataset_id, relative_path, uri, description=None, tags=None, metadata=None, device_id=None, roboto_client=None)#

Import a single file from an external bucket into a Roboto dataset. This currently only supports AWS S3.

This is a convenience method for importing a single file from customer-owned buckets that have been registered as bring-your-own bucket (BYOB) integrations with Roboto. Unlike import_batch(), this method automatically determines the file size by querying the object store and verifies that the object actually exists before importing, providing additional validation and convenience for single-file operations.

The file remains in its original location while metadata is registered with Roboto for discovery, processing, and analysis. This method currently only works with S3 URIs from buckets that have been properly registered as BYOB integrations for your organization.

Parameters:

  • dataset_id (str) – ID of the dataset to import the file into.

  • relative_path (str) – Path of the file relative to the dataset root (e.g., logs/session1.bag).

  • uri (str) – URI where the file is located (e.g., s3://my-bucket/path/to/file.bag). Must be from a registered BYOB integration.

  • description (Optional[str]) – Optional human-readable description of the file.

  • tags (Optional[list[str]]) – Optional list of tags for file discovery and organization.

  • metadata (Optional[dict[str, Any]]) – Optional key-value metadata pairs to associate with the file.

  • device_id (Optional[str]) – Optional identifier of the device that generated this data.

  • roboto_client (Optional[roboto.http.RobotoClient]) – HTTP client for API communication. If None, uses the default client.

Returns:

File object representing the imported file.

Raises:
Return type:

File

Notes

  • Only works with S3 URIs from registered BYOB integrations

  • File size is automatically determined from the object metadata

  • The file is not copied; only metadata is imported into Roboto

  • For importing multiple files efficiently, use import_batch() instead

Examples

Import a single ROS bag file:

>>> from roboto.domain.files import File
>>> file = File.import_one(
...     dataset_id="ds_abc123", relative_path="logs/session1.bag", uri="s3://my-bucket/data/session1.bag"
... )
>>> print(f"Imported file: {file.relative_path}")
Imported file: logs/session1.bag

Import a file with metadata and tags:

>>> file = File.import_one(
...     dataset_id="ds_abc123",
...     relative_path="sensors/lidar_data.pcd",
...     uri="s3://my-bucket/sensors/lidar_data.pcd",
...     description="LiDAR point cloud from highway test",
...     tags=["lidar", "highway", "test"],
...     metadata={"sensor_type": "Velodyne", "resolution": "high"},
... )
>>> print(f"File size: {file.size} bytes")
property ingestion_status: roboto.domain.files.record.IngestionStatus#

Current ingestion status of this file.

Returns the status indicating whether this file has been processed and its data extracted into topics. Used to track ingestion pipeline progress.

Return type:

roboto.domain.files.record.IngestionStatus

Whether this file is a link to one version of another file.

A link sits at its own path under its own device or org, and stores no object. download() and get_signed_url() fetch the target at the version the link pins.

Return type:

bool

mark_ingested()#

Mark this file as fully ingested and ready for post-processing.

Updates the file’s ingestion status to indicate that all data has been successfully processed and extracted into topics. This enables triggers and other automated workflows that depend on complete ingestion.

Returns:

Updated File instance with ingestion status set to Ingested.

Raises:

RobotoUnauthorizedException – Caller lacks permission to update the file.

Return type:

File

Notes

This method is typically called by ingestion actions after they have successfully processed all data in the file. Once marked as ingested, the file becomes eligible for additional post-processing actions.

Examples

>>> file = File.from_id("file_abc123")
>>> print(file.ingestion_status)
IngestionStatus.NotIngested
>>> updated_file = file.mark_ingested()
>>> print(updated_file.ingestion_status)
IngestionStatus.Ingested
property metadata: dict[str, Any]#

Custom metadata associated with this file.

Returns the file’s metadata dictionary containing arbitrary key-value pairs for storing custom information. Supports nested structures and dot notation for accessing nested fields.

Return type:

dict[str, Any]

property modified: datetime.datetime#

Timestamp when this file was last modified.

Returns the UTC datetime when this file’s metadata, tags, or other properties were most recently updated. The file content itself is immutable, but metadata can be modified.

Return type:

datetime.datetime

property modified_by: str#

Identifier of the user who last modified this file.

Returns the user ID or identifier of the person who most recently updated this file’s metadata, tags, or other mutable properties.

Return type:

str

property org_id: str#

Organization identifier that owns this file.

Returns the unique identifier of the organization that owns and has primary access control over this file.

Return type:

str

put_metadata(metadata)#

Add or update metadata fields for this file.

Adds new metadata fields or updates existing ones. Existing fields not specified in the metadata dict are preserved.

Parameters:

metadata (dict[str, Any]) – Dictionary of metadata key-value pairs to add or update.

Returns:

Updated File instance with the new metadata.

Raises:

RobotoUnauthorizedException – Caller lacks permission to update the file.

Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> updated_file = file.put_metadata(
...     {"vehicle_id": "vehicle_001", "session_type": "highway_driving", "weather": "sunny"}
... )
>>> print(updated_file.metadata["vehicle_id"])
'vehicle_001'
put_tags(tags)#

Add or update tags for this file.

Replaces the file’s current tags with the provided list. To add tags while preserving existing ones, retrieve current tags first and combine them.

Parameters:

tags (list[str]) – List of tag strings to set on the file.

Returns:

Updated File instance with the new tags.

Raises:

RobotoUnauthorizedException – Caller lacks permission to update the file.

Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> updated_file = file.put_tags(["sensor-data", "highway", "sunny"])
>>> print(updated_file.tags)
['sensor-data', 'highway', 'sunny']
classmethod query(spec=None, roboto_client=None, owner_org_id=None)#

Query files using a specification with filters and pagination.

Searches for files matching the provided query specification. Results are returned as a generator that automatically handles pagination, yielding File instances as they are retrieved from the API.

Parameters:

  • spec (Optional[roboto.query.QuerySpecification]) – Query specification with filters, sorting, and pagination options. If None, returns all accessible files.

  • roboto_client (Optional[roboto.http.RobotoClient]) – HTTP client for API communication. If None, uses the default client.

  • owner_org_id (Optional[str]) – Organization ID to scope the query. If None, uses caller’s org.

Yields:

File instances matching the query specification.

Raises:

  • ValueError – Query specification references unknown file attributes.

  • RobotoUnauthorizedException – Caller lacks permission to query files.

Return type:

collections.abc.Generator[File, None, None]

Examples

>>> from roboto.query import Comparator, Condition, QuerySpecification
>>> spec = QuerySpecification(
...     condition=Condition(field="tags", comparator=Comparator.Contains, value="sensor-data")
... )
>>> for file in File.query(spec):
...     print(f"Found file: {file.relative_path}")
Found file: logs/sensors_2024_01_01.bag
Found file: logs/sensors_2024_01_02.bag

>>> # Query with metadata filter
>>> spec = QuerySpecification(
...     condition=Condition(field="metadata.vehicle_id", comparator=Comparator.Equals, value="vehicle_001")
... )
>>> files = list(File.query(spec))
>>> print(f"Found {len(files)} files for vehicle_001")
property record: roboto.domain.files.record.FileRecord#

Underlying data record for this file.

Returns the raw FileRecord that contains all the file’s data fields. This provides access to the complete file state as stored in the platform.

Return type:

roboto.domain.files.record.FileRecord

refresh()#

Refresh this file instance with the latest data from the platform.

Fetches the current state of the file from the Roboto platform and updates this instance’s data. Useful when the file may have been modified by other processes or users.

Returns:

This File instance with refreshed data.

Raises:
Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> # File may have been updated by another process
>>> refreshed_file = file.refresh()
>>> print(f"Current version: {refreshed_file.version}")
property relative_path: str#

Path of this file relative to the root of its association’s files.

Uses forward slashes as separators regardless of the operating system. This path uniquely identifies the file among the files of its dataset, device, or org.

Return type:

str

rename_file(file_id, new_path)#

Rename this file to a new path within its dataset, device, or org.

Changes the relative path of the file among the files of its association. This updates the file’s location identifier but does not move the actual file content.

Parameters:

  • file_id (str) – File ID (currently unused, kept for API compatibility).

  • new_path (str) – New relative path for the file, relative to the root of its association’s files.

Returns:

Updated FileRecord with the new path.

Raises:
Return type:

roboto.domain.files.record.FileRecord

Examples

>>> file = File.from_id("file_abc123")
>>> print(file.relative_path)
'old_logs/session1.bag'
>>> updated_record = file.rename_file("file_abc123", "logs/session1.bag")
>>> print(updated_record.relative_path)
'logs/session1.bag'
set_device_id(device_id)#

Set the device ID for this file.

Parameters:

device_id (str) – The device ID to set for this file.

Returns:

Updated File instance with the new device ID.

Raises:
Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> updated_file = file.set_device_id("device_xyz789")
set_representations(topics)#

Replace the representations the named topics’ data on this File is read from.

This File is the one the topics were declared on, through declare_topics() or a Session. Each topic listed ends up with exactly the representations listed, over the part of this File its entry’s data_range names; its other representations there are removed, whether they cover the whole topic or one field of it. Topics and slices not listed keep theirs. Nothing else about the topics changes: their schemas, timeline sources, bounds, anchors and slices stay as declared, and so do the time bounds of every Session holding this File, which do not depend on which files the data is read from.

Use it for what redeclaring a topic cannot do:

  1. Remove a representation.

  2. Replace a representation with one that names another file and differs from it in what it covers, its storage format, its content format or its transformations. Those four identify a representation, as RepresentationDeclaration describes, so declaring the new one adds it beside the first.

  3. Stop a topic being read at all, by listing no representations for it.

The platform checks every entry before writing any, and one refused entry refuses the whole call.

Parameters:

topics (collections.abc.Sequence[roboto.experimental.ingest.TopicRepresentations]) – One entry per topic and slice, at most MAX_FILES_AND_TOPICS_PER_REQUEST; split a larger set across several calls. An empty sequence returns without contacting the platform.

Raises:

  • pydantic.ValidationError – topics is longer than the cap, lists one topic and slice twice, or lists representations naming one file in two storage formats. Raised before any request is made. The rules for one topic’s own representations are enforced earlier, when the caller builds its TopicRepresentations.

  • RobotoNotFoundException – This File does not exist, a topic is not declared on it, an entry’s data_range is not one the topic is declared over on it, or a representation names a file that does not exist in this File’s org or whose status is not Available. Nothing is written.

  • RobotoInvalidRequestException – A topic declared with a timeline source other than SchemaFieldSource (MCAP log or publish time, MP4 presentation time) would get a PARQUET representation, a topic declared over a data_range would be left with a representation that cannot be read by row position and none that can covering the same fields, as transformations describes, or a representation’s field_path names no field of the schema the topic is declared under on this File. Nothing is written.

  • RobotoUnauthorizedException – The caller cannot edit this File or a file a listed representation names.

Return type:

None

Examples

Replace a camera topic’s representation re-encoded as JPEG with one downsampled and re-encoded as PNG, keeping the untransformed one that names the recording, and stop reading a debug topic at all:

>>> from roboto.domain.files import File
>>> from roboto.domain.topics import RepresentationStorageFormat
>>> from roboto.experimental.ingest import RepresentationDeclaration, TopicRepresentations
>>> recording = File.from_id("fl_recording_0412_mcap")
>>> recording.set_representations(
...     [
...         TopicRepresentations(
...             topic_name="/camera/front/image_raw",
...             representations=[
...                 RepresentationDeclaration(
...                     file_id=recording.file_id,
...                     storage_format=RepresentationStorageFormat.MCAP,
...                 ),
...                 RepresentationDeclaration(
...                     file_id="fl_front_png",
...                     storage_format=RepresentationStorageFormat.MCAP,
...                     content_format="png",
...                     transformations=["downsample:0.5", "encode:png"],
...                 ),
...             ],
...         ),
...         TopicRepresentations(topic_name="/debug/raw_dump", representations=[]),
...     ]
... )
set_timeline_offset(offset, *, topic=None, topic_name=None, timeline_source=None, timeline_source_name=None)#

Calibrate this file’s timeline to Unix-epoch wall-clock, optionally scoped to a topic and/or source.

Contract:

  1. The offset, in nanoseconds, is added to stored partition time to produce session wall-clock: session_time_ns = stored_time_ns + offset_ns. An offset given as an instant, such as a datetime, is the nanoseconds since the Unix epoch at which stored time 0 occurred.

  2. topic / topic_name scopes the update to a single topic in this file; timeline_source / timeline_source_name scopes it to a single source. With no selectors, the offset applies to every timeline on the file.

Use set_timeline_offsets() to send several offsets in one atomic request.

Parameters:

  • offset (roboto.time.Time) – Offset to apply: an int of nanoseconds, 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). Must not be negative, and must fit in a signed 64-bit integer of nanoseconds.

  • topic (Optional[roboto.domain.topics.Topic]) – Topic to scope the update to. Mutually exclusive with topic_name.

  • topic_name (Optional[str]) – Topic name to scope the update to (e.g. "/imu/raw"). Mutually exclusive with topic.

  • timeline_source (Optional[roboto.domain.topics.TimelineSourceRecord]) – Source record to scope the update to. Mutually exclusive with timeline_source_name.

  • timeline_source_name (Optional[str]) – Source name to scope the update to (e.g. "header.stamp"). Mutually exclusive with timeline_source.

Returns:

The updated TimelineExtentRecord objects returned by the server.

Raises:

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

  • ValueError – offset is a boolean, a negative number (an int, float, Decimal, or numeric string), or a string that is neither seconds nor ISO 8601; or both of a mutually exclusive pair of selectors are given. Raised before any request is made.

  • OverflowError – offset is an infinite float, Decimal, or string, such as "inf". Raised before any request is made.

  • pydantic.ValidationError – offset converts to a negative number of nanoseconds, or to more than a signed 64-bit integer holds. A subclass of ValueError, raised before any request is made.

  • RobotoUnauthorizedException – The caller cannot edit this file.

  • RobotoNotFoundException – The file carries no timeline data, or the selectors match none of it.

  • RobotoInvalidRequestException – The offset would place the data it reaches, or a session time range declared over that data, before the Unix epoch or past the largest storable Unix-epoch nanosecond value. Nothing is written.

Return type:

list[roboto.domain.topics.TimelineExtentRecord]

Examples

Apply a file-wide offset:

>>> file = File.from_id("file_abc123")
>>> file.set_timeline_offset(1_700_000_000_000_000_000)

Apply the same offset as a datetime, the instant stored time 0 occurred:

>>> import datetime
>>> file.set_timeline_offset(datetime.datetime(2023, 11, 14, 22, 13, 20, tzinfo=datetime.timezone.utc))

Apply an offset to a single topic by name:

>>> file.set_timeline_offset(1_700_000_000_000_000_000, topic_name="/imu/raw")

Apply an offset to a specific source on a topic:

>>> file.set_timeline_offset(
...     500_000_000,
...     topic_name="data",
...     timeline_source_name="ts",
... )
set_timeline_offsets(offsets)#

Apply multiple timeline offsets to this file in one atomic request.

Each entry carries a unix_epoch_offset_ns and optional selectors (topic_name, timeline_source_id, timeline_source_name) that narrow where the offset is applied. An entry with no selectors targets every timeline on the file.

Use set_timeline_offset() for the single-offset convenience form.

Parameters:

offsets (list[roboto.domain.topics.TimelineOffsetEntry]) – Offset entries to apply, each with its own selectors.

Returns:

The updated TimelineExtentRecord objects returned by the server.

Raises:

  • pydantic.ValidationError – offsets is empty. Raised before any request is made.

  • RobotoUnauthorizedException – The caller cannot edit this file.

  • RobotoNotFoundException – The file carries no timeline data, or the selectors of every entry together match none of it.

  • RobotoInvalidRequestException – An entry’s offset would place the data it reaches, or a session time range declared over that data, before the Unix epoch or past the largest storable Unix-epoch nanosecond value. The whole request is refused and nothing is written.

Return type:

list[roboto.domain.topics.TimelineExtentRecord]

Examples

Apply per-topic offsets in a single request:

>>> from roboto.domain.topics import TimelineOffsetEntry
>>> file = File.from_id("file_abc123")
>>> file.set_timeline_offsets(
...     [
...         TimelineOffsetEntry(unix_epoch_offset_ns=1_700_000_000_000_000_000, topic_name="/imu/raw"),
...         TimelineOffsetEntry(unix_epoch_offset_ns=1_700_000_000_000_000_000, topic_name="/camera/image"),
...     ]
... )
property tags: list[str]#

List of tags associated with this file.

Returns the list of string tags that have been applied to this file for categorization and filtering purposes.

Return type:

list[str]

to_association()#

Convert this file to an Association reference.

Creates an Association object that can be used to reference this file in other contexts, such as when creating collections or specifying action inputs.

Returns:

Association object referencing this file and its current version.

Return type:

roboto.association.Association

Examples

>>> file = File.from_id("file_abc123")
>>> association = file.to_association()
>>> print(f"Association: {association.association_type}:{association.association_id}")
Association: file:file_abc123
to_dict()#

Convert this file to a dictionary representation.

Returns the file’s data as a JSON-serializable dictionary containing all file attributes and metadata.

Returns:

Dictionary representation of the file data.

Return type:

dict[str, Any]

Examples

>>> file = File.from_id("file_abc123")
>>> file_dict = file.to_dict()
>>> print(file_dict["relative_path"])
'logs/session1.bag'
>>> print(file_dict["metadata"])
{'vehicle_id': 'vehicle_001', 'session_type': 'highway'}
update(description=NotSet, metadata_changeset=NotSet, ingestion_complete=NotSet, device_id=NotSet)#

Update this file’s properties.

Updates various properties of the file including description, metadata, and ingestion status. Only specified parameters are updated; others remain unchanged.

Parameters:

  • description (Optional[Union[str, roboto.sentinels.NotSetType]]) – New description for the file. Use NotSet to leave unchanged.

  • metadata_changeset (Union[roboto.updates.MetadataChangeset, roboto.sentinels.NotSetType]) – Metadata changes to apply (add, update, or remove fields/tags). Use NotSet to leave metadata unchanged.

  • ingestion_complete (Union[Literal[True], roboto.sentinels.NotSetType]) – Set to True to mark the file as fully ingested. Use NotSet to leave ingestion status unchanged.

  • device_id (Optional[Union[str, roboto.sentinels.NotSetType]]) – New device ID for the file. Use NotSet to leave unchanged.

Returns:

Updated File instance with the new properties.

Raises:

RobotoUnauthorizedException – Caller lacks permission to update the file.

Return type:

File

Examples

>>> file = File.from_id("file_abc123")
>>> updated_file = file.update(description="Updated sensor data from highway test")
>>> print(updated_file.description)
'Updated sensor data from highway test'

>>> # Update metadata and mark as ingested
>>> from roboto.updates import MetadataChangeset
>>> changeset = MetadataChangeset(put_fields={"processed": True})
>>> updated_file = file.update(metadata_changeset=changeset, ingestion_complete=True)
property uri: str#

Storage URI for this file’s content.

Returns the storage location URI where the file’s actual content is stored. This is typically an S3 URI or similar cloud storage reference.

Return type:

str

property version: int#

Version number of this file.

Returns the version number that increments each time the file’s metadata or properties are updated. The file content itself is immutable, but metadata changes create new versions.

Return type:

int

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