viam.app.data_client

Attributes

LOGGER

Classes

DataClient

gRPC client for uploading and retrieving data from app.

Module Contents

viam.app.data_client.LOGGER
class viam.app.data_client.DataClient(channel: grpclib.client.Channel, metadata: Mapping[str, str])[source]

gRPC client for uploading and retrieving data from app.

Constructor is used by ViamClient to instantiate relevant service stubs. Calls to DataClient methods should be made through ViamClient.

Establish a Connection:

import asyncio

from viam.rpc.dial import DialOptions, Credentials
from viam.app.viam_client import ViamClient


async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)


async def main():
    # Make a ViamClient
    viam_client = await connect()
    # Instantiate a DataClient to run data client API methods on
    data_client = viam_client.data_client

    viam_client.close()

if __name__ == '__main__':
    asyncio.run(main())

For more information, see Data Client API.

class TabularData[source]

Class representing a piece of tabular data and associated metadata.

data: Mapping[str, Any]

The requested data

metadata: viam.proto.app.data.CaptureMetadata

The metadata associated with the data

time_requested: datetime.datetime

The time the data were requested

time_received: datetime.datetime

The time the data were received

__str__() → str[source]

Return str(self).

__eq__(other: object) → bool[source]

Return self==value.

async tabular_data_by_filter(filter: viam.proto.app.data.Filter | None = None, limit: int | None = None, sort_order: viam.proto.app.data.Order.ValueType | None = None, last: str | None = None, count_only: bool = False, include_internal_data: bool = False, dest: str | None = None) → Tuple[List[TabularData], int, str][source]

Filter and download tabular data. The data will be paginated into pages of limit items, and the pagination ID will be included in the returned tuple. If a destination is provided, the data will be saved to that file. If the file is not empty, it will be overwritten.

from viam.utils import create_filter

my_data = []
last = None
my_filter = create_filter(component_name="left_motor")
while True:
    tabular_data, count, last = await data_client.tabular_data_by_filter(my_filter, last)
    if not tabular_data:
        break
    my_data.extend(tabular_data)
Parameters:

  • filter (viam.proto.app.data.Filter) – Optional Filter specifying tabular data to retrieve. No Filter implies all tabular data.

  • limit (int) – The maximum number of entries to include in a page. Defaults to 50 if unspecified.

  • sort_order (viam.proto.app.data.Order) – The desired sort order of the data.

  • last (str) – Optional string indicating the object identifier of the last-returned data. This object identifier is returned by calls to TabularDataByFilter as the last value. If provided, the server will return the next data entries after the last object identifier.

  • count_only (bool) – Whether to return only the total count of entries.

  • include_internal_data (bool) – Whether to return the internal data. Internal data is used for Viam-specific data ingestion, like cloud SLAM. Defaults to False.

  • dest (str) – Optional filepath for writing retrieved data.

Returns:

A tuple containing the following: List[TabularData]: The tabular data, int: The count (number of entries), str: The last-returned page ID.

Return type:

Tuple[List[TabularData], int, str]

For more information, see Data Client API.

async tabular_data_by_sql(organization_id: str, sql_query: str) → List[Dict[str, viam.utils.ValueTypes]][source]

Obtain unified tabular data and metadata, queried with SQL.

data = await data_client.tabular_data_by_sql(org_id="<your-org-id>", sql_query="SELECT * FROM readings LIMIT 5")
Parameters:

  • organization_id (str) – The ID of the organization that owns the data. You can obtain your organization ID from the Viam app’s organization settings page.

  • sql_query (str) – The SQL query to run.

Returns:

An array of data objects.

Return type:

List[Dict[str, ValueTypes]]

For more information, see Data Client API.

async tabular_data_by_mql(organization_id: str, mql_binary: List[bytes]) → List[Dict[str, viam.utils.ValueTypes]][source]

Obtain unified tabular data and metadata, queried with MQL.

# using bson
import bson
tabular_data = await data_client.tabular_data_by_mql(org_id="<your-org-id>", mql_binary=[
    bson.dumps({ '$match': { 'location_id': '<location-id>' } }),
    bson.dumps({ "$limit": 5 })
])

# using pymongo
import bson
tabular_data = await data_client.tabular_data_by_mql(org_id="<your-org-id>", mql_binary=[
    bson.encode({ '$match': { 'location_id': '<location-id>' } }),
    bson.encode({ "$limit": 5 })
])
Parameters:

  • organization_id (str) – The ID of the organization that owns the data. You can obtain your organization ID from the Viam app’s organization settings page.

  • mql_binary (List[bytes]) – The MQL query to run as a list of BSON queries. You can encode your bson queries using a library like pymongo or bson.

Returns:

An array of data objects.

Return type:

List[Dict[str, ValueTypes]]

For more information, see Data Client API.

async binary_data_by_filter(filter: viam.proto.app.data.Filter | None = None, limit: int | None = None, sort_order: viam.proto.app.data.Order.ValueType | None = None, last: str | None = None, include_binary_data: bool = True, count_only: bool = False, include_internal_data: bool = False, dest: str | None = None) → Tuple[List[viam.proto.app.data.BinaryData], int, str][source]

Filter and download binary data. The data will be paginated into pages of limit items, and the pagination ID will be included in the returned tuple. If a destination is provided, the data will be saved to that file. If the file is not empty, it will be overwritten.

from viam.utils import create_filter


my_data = []
last = None
my_filter = create_filter(component_name="camera")
while True:
    data, count, last = await data_client.binary_data_by_filter(my_filter, last)
    if not data:
        break
    my_data.extend(data)
Parameters:

  • filter (viam.proto.app.data.Filter) – Optional Filter specifying tabular data to retrieve. No Filter implies all binary data.

  • limit (int) – The maximum number of entries to include in a page. Defaults to 50 if unspecified.

  • sort_order (viam.proto.app.data.Order) – The desired sort order of the data.

  • last (str) – Optional string indicating the object identifier of the last-returned data. This object identifier is returned by calls to BinaryDataByFilter as the last value. If provided, the server will return the next data entries after the last object identifier.

  • include_binary_data (bool) – Boolean specifying whether to actually include the binary file data with each retrieved file. Defaults to true (that is, both the files’ data and metadata are returned).

  • count_only (bool) – Whether to return only the total count of entries.

  • include_internal_data (bool) – Whether to return the internal data. Internal data is used for Viam-specific data ingestion, like cloud SLAM. Defaults to False.

  • dest (str) – Optional filepath for writing retrieved data.

Returns:

A tuple containing the following: List[viam.proto.app.data.BinaryData]: The binary data, int: The count (number of entries), str: The last-returned page ID.

Return type:

Tuple[List[viam.proto.app.data.BinaryData], int, str]

For more information, see Data Client API.

async binary_data_by_ids(binary_ids: List[viam.proto.app.data.BinaryID], dest: str | None = None) → List[viam.proto.app.data.BinaryData][source]

Filter and download binary data.

from viam.proto.app.data import BinaryID

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_ids = []

for obj in binary_metadata:
    my_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
        )
    )

binary_data = await data_client.binary_data_by_ids(my_ids)
Parameters:

  • binary_ids (List[viam.proto.app.data.BinaryID]) – BinaryID objects specifying the desired data. Must be non-empty.

  • dest (str) – Optional filepath for writing retrieved data.

Raises:

GRPCError – If no BinaryID objects are provided.

Returns:

The binary data.

Return type:

List[viam.proto.app.data.BinaryData]

For more information, see Data Client API.

async delete_tabular_data(organization_id: str, delete_older_than_days: int) → int[source]

Delete tabular data older than a specified number of days.

from viam.utils import create_filter

my_filter = create_filter(component_name="left_motor")
days_of_data_to_delete = 10
tabular_data = await data_client.delete_tabular_data(
    org_id="a12b3c4e-1234-1abc-ab1c-ab1c2d345abc", days_of_data_to_delete)
Parameters:

  • organization_id (str) – ID of organization to delete data from. You can obtain your organization ID from the Viam app’s organization settings page.

  • delete_older_than_days (int) – Delete data that was captured up to this many days ago. For example if delete_older_than_days is 10, this deletes any data that was captured up to 10 days ago. If it is 0, all existing data is deleted.

Returns:

The number of items deleted.

Return type:

int

For more information, see Data Client API.

abstract delete_tabular_data_by_filter(filter: viam.proto.app.data.Filter | None) → int[source]
Async:

Deprecated: use delete_tabular_data instead.

async delete_binary_data_by_filter(filter: viam.proto.app.data.Filter | None) → int[source]

Filter and delete binary data.

from viam.utils import create_filter

my_filter = create_filter(component_name="left_motor")
res = await data_client.delete_binary_data_by_filter(my_filter)
Parameters:

filter (viam.proto.app.data.Filter) – Optional Filter specifying binary data to delete. Passing an empty Filter will lead to all data being deleted. Exercise caution when using this option.

Returns:

The number of items deleted.

Return type:

int

For more information, see Data Client API.

async delete_binary_data_by_ids(binary_ids: List[viam.proto.app.data.BinaryID]) → int[source]

Filter and delete binary data.

from viam.proto.app.data import BinaryID

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_ids = []

for obj in binary_metadata:
    my_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
        )
    )

binary_data = await data_client.delete_binary_data_by_ids(my_ids)
Parameters:

binary_ids (List[viam.proto.app.data.BinaryID]) – BinaryID objects specifying the data to be deleted. Must be non-empty.

Raises:

GRPCError – If no BinaryID objects are provided.

Returns:

The number of items deleted.

Return type:

int

For more information, see Data Client API.

async add_tags_to_binary_data_by_ids(tags: List[str], binary_ids: List[viam.proto.app.data.BinaryID]) → None[source]

Add tags to binary data.

from viam.proto.app.data import BinaryID

tags = ["tag1", "tag2"]

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_ids = []

for obj in binary_metadata:
    my_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
        )
    )

binary_data = await data_client.add_tags_to_binary_data_by_ids(tags, my_ids)
Parameters:

  • tags (List[str]) – List of tags to add to specified binary data. Must be non-empty.

  • binary_ids (List[viam.app.proto.BinaryID]) – List of BinaryID objects specifying binary data to tag. Must be non-empty.

Raises:

GRPCError – If no BinaryID objects or tags are provided.

For more information, see Data Client API.

async add_tags_to_binary_data_by_filter(tags: List[str], filter: viam.proto.app.data.Filter | None = None) → None[source]

Add tags to binary data.

from viam.utils import create_filter

my_filter = create_filter(component_name="my_camera")
tags = ["tag1", "tag2"]
res = await data_client.add_tags_to_binary_data_by_filter(tags, my_filter)
Parameters:

  • tags (List[str]) – List of tags to add to specified binary data. Must be non-empty.

  • filter (viam.proto.app.data.Filter) – Filter specifying binary data to tag. If no Filter is provided, all data will be tagged.

Raises:

GRPCError – If no tags are provided.

For more information, see Data Client API.

async remove_tags_from_binary_data_by_ids(tags: List[str], binary_ids: List[viam.proto.app.data.BinaryID]) → int[source]

Remove tags from binary data by IDs.

from viam.proto.app.data import BinaryID

tags = ["tag1", "tag2"]

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_ids = []

for obj in binary_metadata:
    my_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
        )
    )

binary_data = await data_client.remove_tags_from_binary_data_by_ids(
    tags, my_ids)
Parameters:

  • tags (List[str]) – List of tags to remove from specified binary data. Must be non-empty.

  • binary_ids (List[BinaryID]) – List of BinaryID objects specifying binary data to untag. Must be non-empty.

Raises:

GRPCError – If no binary_ids or tags are provided.

Returns:

The number of tags removed.

Return type:

int

For more information, see Data Client API.

async remove_tags_from_binary_data_by_filter(tags: List[str], filter: viam.proto.app.data.Filter | None = None) → int[source]

Remove tags from binary data.

from viam.utils import create_filter

my_filter = create_filter(component_name="my_camera")
tags = ["tag1", "tag2"]
res = await data_client.remove_tags_from_binary_data_by_filter(tags, my_filter)
Parameters:

  • tags (List[str]) – List of tags to remove from specified binary data.

  • filter (viam.proto.app.data.Filter) – Filter specifying binary data to untag. If no Filter is provided, all data will be untagged.

Raises:

GRPCError – If no tags are provided.

Returns:

The number of tags removed.

Return type:

int

For more information, see Data Client API.

async tags_by_filter(filter: viam.proto.app.data.Filter | None = None) → List[str][source]

Get a list of tags using a filter.

from viam.utils import create_filter

my_filter = create_filter(component_name="my_camera")
tags = await data_client.tags_by_filter(my_filter)
Parameters:

filter (viam.proto.app.data.Filter) – Filter specifying data to retrieve from. If no Filter is provided, all data tags will return.

Returns:

The list of tags.

Return type:

List[str]

For more information, see Data Client API.

async add_bounding_box_to_image_by_id(binary_id: viam.proto.app.data.BinaryID, label: str, x_min_normalized: float, y_min_normalized: float, x_max_normalized: float, y_max_normalized: float) → str[source]

Add a bounding box to an image.

from viam.proto.app.data import BinaryID

MY_BINARY_ID = BinaryID(
    file_id=your-file_id,
    organization_id=your-org-id,
    location_id=your-location-id
)

bbox_label = await data_client.add_bounding_box_to_image_by_id(
    binary_id=MY_BINARY_ID,
    label="label",
    x_min_normalized=0,
    y_min_normalized=.1,
    x_max_normalized=.2,
    y_max_normalized=.3
)

print(bbox_label)
Parameters:

  • binary_id (viam.proto.app.data.BinaryID) – The ID of the image to add the bounding box to.

  • label (str) – A label for the bounding box.

  • x_min_normalized (float) – Min X value of the bounding box normalized from 0 to 1.

  • y_min_normalized (float) – Min Y value of the bounding box normalized from 0 to 1.

  • x_max_normalized (float) – Max X value of the bounding box normalized from 0 to 1.

  • y_max_normalized (float) – Max Y value of the bounding box normalized from 0 to 1.

Raises:

GRPCError – If the X or Y values are outside of the [0, 1] range.

Returns:

The bounding box ID.

Return type:

str

For more information, see Data Client API.

async remove_bounding_box_from_image_by_id(bbox_id: str, binary_id: viam.proto.app.data.BinaryID) → None[source]

Removes a bounding box from an image.

from viam.proto.app.data import BinaryID

MY_BINARY_ID = BinaryID(
    file_id=your-file_id,
    organization_id=your-org-id,
    location_id=your-location-id
)

await data_client.remove_bounding_box_from_image_by_id(
binary_id=MY_BINARY_ID,
bbox_id="your-bounding-box-id-to-delete"
)
Parameters:

  • bbox_id (str) – The ID of the bounding box to remove.

  • binary_id (viam.proto.arr.data.BinaryID) – Binary ID of the image to to remove the bounding box from.

For more information, see Data Client API.

async bounding_box_labels_by_filter(filter: viam.proto.app.data.Filter | None = None) → List[str][source]

Get a list of bounding box labels using a Filter.

from viam.utils import create_filter

my_filter = create_filter(component_name="my_camera")
bounding_box_labels = await data_client.bounding_box_labels_by_filter(
    my_filter)
Parameters:

filter (viam.proto.app.data.Filter) – Filter specifying data to retrieve from. If no Filter is provided, all labels will return.

Returns:

The list of bounding box labels.

Return type:

List[str]

For more information, see Data Client API.

async get_database_connection(organization_id: str) → str[source]

Get a connection to access a MongoDB Atlas Data federation instance.

data_client.get_database_connection(org_id="a12b3c4e-1234-1abc-ab1c-ab1c2d345abc")
Parameters:

organization_id (str) – Organization to retrieve the connection for. You can obtain your organization ID from the Viam app’s organization settings page.

Returns:

The hostname of the federated database.

Return type:

str

For more information, see Data Client API.

async configure_database_user(organization_id: str, password: str) → None[source]

Configure a database user for the Viam organization’s MongoDB Atlas Data Federation instance. It can also be used to reset the password of the existing database user.

await data_client.configure_database_user(
    organization_id="<your-org-id>",
    password="your_password"
)
Parameters:

  • organization_id (str) – The ID of the organization. You can obtain your organization ID from the Viam app’s organization settings page.

  • password (str) – The password of the user.

For more information, see Data Client API.

async create_dataset(name: str, organization_id: str) → str[source]

Create a new dataset.

name = await data_client.create_dataset(
    name="<dataset-name>",
    organization_id="<your-org-id>"
)
print(name)
Parameters:

  • name (str) – The name of the dataset being created.

  • organization_id (str) – The ID of the organization where the dataset is being created. You can obtain your organization ID from the Viam app’s organization settings page.

Returns:

The dataset ID of the created dataset.

Return type:

str

For more information, see Data Client API.

async list_dataset_by_ids(ids: List[str]) → Sequence[viam.proto.app.dataset.Dataset][source]

Get a list of datasets using their IDs.

datasets = await data_client.list_dataset_by_ids(
    ids=["abcd-1234xyz-8765z-123abc"]
)
print(datasets)
Parameters:

ids (List[str]) – The IDs of the datasets being called for. To retrieve these IDs, navigate to your dataset’s page in the Viam app, click … in the left-hand menu, and click Copy dataset ID.

Returns:

The list of datasets.

Return type:

Sequence[Dataset]

For more information, see Data Client API.

async list_datasets_by_organization_id(organization_id: str) → Sequence[viam.proto.app.dataset.Dataset][source]

Get the datasets in an organization.

datasets = await data_client.list_dataset_by_organization_id(
    organization_id=[""a12b3c4e-1234-1abc-ab1c-ab1c2d345abc""]
)
print(datasets)
Parameters:

organization_id (str) – The ID of the organization. You can obtain your organization ID from the Viam app’s organization settings page.

Returns:

The list of datasets in the organization.

Return type:

Sequence[Dataset]

For more information, see Data Client API.

async rename_dataset(id: str, name: str) → None[source]

Rename a dataset specified by the dataset ID.

await data_client.rename_dataset(
    id="abcd-1234xyz-8765z-123abc",
    name="<dataset-name>"
)
Parameters:

  • id (str) – The ID of the dataset.

  • name (str) – The new name of the dataset.

For more information, see Data Client API.

async delete_dataset(id: str) → None[source]

Delete a dataset.

await data_client.delete_dataset(
    id="abcd-1234xyz-8765z-123abc"
)
Parameters:

id (str) – The ID of the dataset.

For more information, see Data Client API.

async add_binary_data_to_dataset_by_ids(binary_ids: List[viam.proto.app.data.BinaryID], dataset_id: str) → None[source]

Add the BinaryData to the provided dataset.

This BinaryData will be tagged with the VIAM_DATASET_{id} label.

from viam.proto.app.data import BinaryID

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_binary_ids = []

for obj in binary_metadata:
    my_binary_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
            )
        )

await data_client.add_binary_data_to_dataset_by_ids(
    binary_ids=my_binary_ids,
    dataset_id="abcd-1234xyz-8765z-123abc"
)
Parameters:

  • binary_ids (List[BinaryID]) – The IDs of binary data to add to dataset. To retrieve these IDs, navigate to your dataset’s page in the Viam app, click … in the left-hand menu, and click Copy dataset ID.

  • dataset_id (str) – The ID of the dataset to be added to.

For more information, see Data Client API.

async remove_binary_data_from_dataset_by_ids(binary_ids: List[viam.proto.app.data.BinaryID], dataset_id: str) → None[source]

Remove the BinaryData from the provided dataset.

This BinaryData will lose the VIAM_DATASET_{id} tag.

from viam.proto.app.data import BinaryID

binary_metadata = await data_client.binary_data_by_filter(
    include_file_data=False
)

my_binary_ids = []

for obj in binary_metadata:
    my_binary_ids.append(
        BinaryID(
            file_id=obj.metadata.id,
            organization_id=obj.metadata.capture_metadata.organization_id,
            location_id=obj.metadata.capture_metadata.location_id
        )
    )

await data_client.remove_binary_data_from_dataset_by_ids(
    binary_ids=my_binary_ids,
    dataset_id="abcd-1234xyz-8765z-123abc"
)
Parameters:

  • binary_ids (List[BinaryID]) – The IDs of binary data to remove from dataset. To retrieve these IDs, navigate to your dataset’s page in the Viam app, click … in the left-hand menu, and click Copy dataset ID.

  • dataset_id (str) – The ID of the dataset to be removed from.

For more information, see Data Client API.

async binary_data_capture_upload(binary_data: bytes, part_id: str, component_type: str, component_name: str, method_name: str, file_extension: str, method_parameters: Mapping[str, Any] | None = None, tags: List[str] | None = None, data_request_times: Tuple[datetime.datetime, datetime.datetime] | None = None) → str[source]

Upload binary sensor data.

Upload binary data collected on a robot through a specific component (for example, a motor) along with the relevant metadata to app.viam.com. Binary data can be found under the “Files” subtab of the Data tab on app.viam.com.

time_requested = datetime(2023, 6, 5, 11)
time_received = datetime(2023, 6, 5, 11, 0, 3)

file_id = await data_client.binary_data_capture_upload(
    part_id="INSERT YOUR PART ID",
    component_type='camera',
    component_name='my_camera',
    method_name='GetImages',
    method_parameters=None,
    tags=["tag_1", "tag_2"],
    data_request_times=[time_requested, time_received],
    file_extension=".jpg",
    binary_data=b"Encoded image bytes"
)
Parameters:

  • binary_data (bytes) – The data to be uploaded, represented in bytes.

  • part_id (str) – Part ID of the component used to capture the data.

  • component_type (str) – Type of the component used to capture the data (for example, “movement_sensor”).

  • component_name (str) – Name of the component used to capture the data.

  • method_name (str) – Name of the method used to capture the data.

  • file_extension (str) – The file extension of binary data including the period, for example .jpg, .png, .pcd. The backend will route the binary to its corresponding mime type based on this extension. Files with a .jpeg, .jpg, or .png extension will be saved to the images tab.

  • method_parameters (Optional[Mapping[str, Any]]) – Optional dictionary of method parameters. No longer in active use.

  • tags (Optional[List[str]]) – Optional list of tags to allow for tag-based data filtering when retrieving data.

  • data_request_times (Optional[Tuple[datetime.datetime, datetime.datetime]]) – Optional tuple containing datetime objects denoting the times this data was requested[0] by the robot and received[1] from the appropriate sensor.

Raises:

GRPCError – If an invalid part ID is passed.

Returns:

The file_id of the uploaded data.

Return type:

str

For more information, see Data Client API.

async tabular_data_capture_upload(tabular_data: List[Mapping[str, Any]], part_id: str, component_type: str, component_name: str, method_name: str, method_parameters: Mapping[str, Any] | None = None, tags: List[str] | None = None, data_request_times: List[Tuple[datetime.datetime, datetime.datetime]] | None = None) → str[source]

Upload tabular sensor data.

Upload tabular data collected on a robot through a specific component (for example, a motor) along with the relevant metadata to app.viam.com. Tabular data can be found under the “Sensors” subtab of the Data tab on app.viam.com.

time_requested = datetime(2023, 6, 5, 11)
time_received = datetime(2023, 6, 5, 11, 0, 3)

file_id = await data_client.tabular_data_capture_upload(
    part_id="INSERT YOUR PART ID",
    component_type='motor',
    component_name='left_motor',
    method_name='IsPowered',
    tags=["tag_1", "tag_2"],
    data_request_times=[(time_requested, time_received)],
    tabular_data=[{'PowerPCT': 0, 'IsPowered': False}]
)
Parameters:

  • tabular_data (List[Mapping[str, Any]]) – List of the data to be uploaded, represented tabularly as a collection of dictionaries.

  • part_id (str) – Part ID of the component used to capture the data.

  • component_type (str) – Type of the component used to capture the data (for example, “movement_sensor”).

  • component_name (str) – Name of the component used to capture the data.

  • method_name (str) – Name of the method used to capture the data.

  • method_parameters (Optional[Mapping[str, Any]]) – Optional dictionary of method parameters. No longer in active use.

  • tags (Optional[List[str]]) – Optional list of tags to allow for tag-based data filtering when retrieving data.

  • data_request_times (Optional[List[Tuple[datetime.datetime, datetime.datetime]]]) – Optional list of tuples, each containing datetime objects denoting the times this data was requested[0] by the robot and received[1] from the appropriate sensor. Passing a list of tabular data and Timestamps with length n > 1 will result in n datapoints being uploaded, all tied to the same metadata.

Raises:

  • GRPCError – If an invalid part ID is passed.

  • ValueError – If a list of Timestamp objects is provided and its length does not match the length of the list of tabular data.

Returns:

The file_id of the uploaded data.

Return type:

str

For more information, see Data Client API.

async streaming_data_capture_upload(data: bytes, part_id: str, file_ext: str, component_type: str | None = None, component_name: str | None = None, method_name: str | None = None, method_parameters: Mapping[str, Any] | None = None, data_request_times: Tuple[datetime.datetime, datetime.datetime] | None = None, tags: List[str] | None = None) → str[source]

Uploads the metadata and contents of streaming binary data.

time_requested = datetime(2023, 6, 5, 11)
time_received = datetime(2023, 6, 5, 11, 0, 3)

file_id = await data_client.streaming_data_capture_upload(
    data="byte-data-to-upload",
    part_id="INSERT YOUR PART ID",
    file_ext="png",
    component_type='motor',
    component_name='left_motor',
    method_name='IsPowered',
    data_request_times=[(time_requested, time_received)],
    tags=["tag_1", "tag_2"]
)
Parameters:

  • data (bytes) – the data to be uploaded.

  • part_id (str) – Part ID of the resource associated with the file.

  • file_ext (str) – file extension type for the data. required for determining MIME type.

  • component_type (Optional[str]) – Optional type of the component associated with the file (for example, “movement_sensor”).

  • component_name (Optional[str]) – Optional name of the component associated with the file.

  • method_name (Optional[str]) – Optional name of the method associated with the file.

  • method_parameters (Optional[str]) – Optional dictionary of the method parameters. No longer in active use.

  • data_request_times (Optional[Tuple[datetime.datetime, datetime.datetime]]) – Optional tuple containing datetime objects denoting the times this data was requested[0] by the robot and received[1] from the appropriate sensor.

  • tags (Optional[List[str]]) – Optional list of tags to allow for tag-based filtering when retrieving data.

Raises:

GRPCError – If an invalid part ID is passed.

Returns:

The file_id of the uploaded data.

Return type:

str

For more information, see Data Client API.

async file_upload(part_id: str, data: bytes, component_type: str | None = None, component_name: str | None = None, method_name: str | None = None, file_name: str | None = None, method_parameters: Mapping[str, Any] | None = None, file_extension: str | None = None, tags: List[str] | None = None) → str[source]

Upload arbitrary file data.

Upload file data that may be stored on a robot along with the relevant metadata to app.viam.com. File data can be found under the “Files” subtab of the Data tab on app.viam.com.

file_id = await data_client.file_upload(
    data=b"Encoded image bytes",
    part_id="INSERT YOUR PART ID",
    tags=["tag_1", "tag_2"],
    file_name="your-file",
    file_extension=".txt"
)
Parameters:

  • part_id (str) – Part ID of the resource associated with the file.

  • data (bytes) – Bytes representing file data to upload.

  • component_type (Optional[str]) – Optional type of the component associated with the file (for example, “movement_sensor”).

  • component_name (Optional[str]) – Optional name of the component associated with the file.

  • method_name (Optional[str]) – Optional name of the method associated with the file.

  • file_name (Optional[str]) – Optional name of the file. The empty string “” will be assigned as the file name if one isn’t provided.

  • method_parameters (Optional[str]) – Optional dictionary of the method parameters. No longer in active use.

  • file_extension (Optional[str]) – Optional file extension. The empty string “” will be assigned as the file extension if one isn’t provided. Files with a .jpeg, .jpg, or .png extension will be saved to the images tab.

  • tags (Optional[List[str]]) – Optional list of tags to allow for tag-based filtering when retrieving data.

Raises:

GRPCError – If an invalid part ID is passed.

Returns:

ID of the new file.

Return type:

str

For more information, see Data Client API.

async file_upload_from_path(filepath: str, part_id: str, component_type: str | None = None, component_name: str | None = None, method_name: str | None = None, method_parameters: Mapping[str, Any] | None = None, tags: List[str] | None = None) → str[source]

Upload arbitrary file data.

Upload file data that may be stored on a robot along with the relevant metadata to app.viam.com. File data can be found under the “Files” subtab of the Data tab on app.viam.com.

file_id = await data_client.file_upload_from_path(
    part_id="INSERT YOUR PART ID",
    tags=["tag_1", "tag_2"],
    filepath="/Users/<your-username>/<your-directory>/<your-file.txt>"
)
Parameters:

  • filepath (str) – Absolute filepath of file to be uploaded.

  • part_id (str) – Part ID of the component associated with the file.

  • component_type (Optional[str]) – Optional type of the component associated with the file (for example, “movement_sensor”).

  • component_name (Optional[str]) – Optional name of the component associated with the file.

  • method_name (Optional[str]) – Optional name of the method associated with the file.

  • method_parameters (Optional[str]) – Optional dictionary of the method parameters. No longer in active use.

  • tags (Optional[List[str]]) – Optional list of tags to allow for tag-based filtering when retrieving data.

Raises:

  • GRPCError – If an invalid part ID is passed.

  • FileNotFoundError – If the provided filepath is not found.

Returns:

ID of the new file.

Return type:

str

For more information, see Data Client API.

static create_filter(component_name: str | None = None, component_type: str | None = None, method: str | None = None, robot_name: str | None = None, robot_id: str | None = None, part_name: str | None = None, part_id: str | None = None, location_ids: List[str] | None = None, organization_ids: List[str] | None = None, mime_type: List[str] | None = None, start_time: datetime.datetime | None = None, end_time: datetime.datetime | None = None, tags: List[str] | None = None, bbox_labels: List[str] | None = None, dataset_id: str | None = None) → viam.proto.app.data.Filter[source]