Quickstart

Authenticate

The preferred way to authenticating against the Cognite API is using OpenID Connect (OIDC). To enable this, the CogniteClient accepts a token provider function.

>>> from cognite.client import CogniteClient
>>> def token_provider():
>>>     ...
>>> c = CogniteClient(token=token_provider)

For details on different ways of implementing the token provider, take a look at this guide.

If OIDC has not been enabled for your CDF project, you will want to authenticate using an API key. You can do this by setting the following environment variable

$ export COGNITE_API_KEY = <your-api-key>

or by passing the API key directly to the CogniteClient.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient(api_key="<your-api-key>", client_name="<your-client-name>")

Instantiate a new client

Use this code to instantiate a client and get your login status. CDF returns an object with attributes that describe which project and service account your API key belongs to. The client_name is an user-defined string intended to give the client a unique identifier. You can provide the client_name through the COGNITE_CLIENT_NAME environment variable or by passing it directly to the CogniteClient constructor. All examples in this documentation assume that COGNITE_CLIENT_NAME has been set.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> status = c.login.status()

Read more about the CogniteClient and the functionality it exposes below.

Discover time series

For the next examples, you will need to supply ids for the time series that you want to retrieve. You can find some ids by listing the available time series. Limits for listing resources default to 25, so the following code will return the first 25 time series resources.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> ts_list = c.time_series.list(include_metadata=False)

Plot time series

There are several ways of plotting a time series you have fetched from the API. The easiest is to call .plot() on the returned TimeSeries or TimeSeriesList objects. By default, this plots the raw data points for the last 24 hours. If there are no data points for the last 24 hours, plot will throw an exception.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_time_series = c.time_series.retrieve(id=<time-series-id>)
>>> my_time_series.plot()

You can also pass arguments to the .plot() method to change the start, end, aggregates, and granularity of the request.

>>> my_time_series.plot(start="365d-ago", end="now", aggregates=["average"], granularity="1d")

The Datapoints and DatapointsList objects that are returned when you fetch data points, also have .plot() methods you can use to plot the data.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_datapoints = c.datapoints.retrieve(
...                     id=[<time-series-ids>],
...                     start="10d-ago",
...                     end="now",
...                     aggregates=["max"],
...                     granularity="1h"
...                 )
>>> my_datapoints.plot()

Note

To use the .plot() functionality you need to install matplotlib.

Create an asset hierarchy

CDF organizes digital information about the physical world. Assets are digital representations of physical objects or groups of objects, and assets are organized into an asset hierarchy. For example, an asset can represent a water pump which is part of a subsystem on an oil platform.

At the top of an asset hierarchy is a root asset (e.g., the oil platform). Each project can have multiple root assets. All assets have a name and a parent asset. No assets with the same parent can have the same name.

To create a root asset (an asset without a parent), omit the parent ID when you post the asset to the API. To make an asset a child of an existing asset, you must specify a parent ID.

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Asset
>>> c = CogniteClient()
>>> my_asset = Asset(name="my first asset", parent_id=123)
>>> c.assets.create(my_asset)

To post an entire asset hierarchy, you can describe the relations within your asset hierarchy using the external_id and parent_external_id attributes on the Asset object. You can post an arbitrary number of assets, and the SDK will split the request into multiple requests. To make sure that the assets are posted in the correct order, you can use the .create_hierarchy() function, which takes care of the sorting before splitting the request into smaller chunks. However, note that the .create_hierarchy() function requires the external_id property to be set for all assets.

This example shows how to post a three levels deep asset hierarchy consisting of three assets.

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Asset
>>> c = CogniteClient()
>>> root = Asset(name="root", external_id="1")
>>> child = Asset(name="child", external_id="2", parent_external_id="1")
>>> descendant = Asset(name="descendant", external_id="3", parent_external_id="2")
>>> c.assets.create_hierarchy([root, child, descendant])

Wrap the .create_hierarchy() call in a try-except to get information if posting the assets fails:

  • Which assets were posted. (The request yielded a 201.)
  • Which assets may have been posted. (The request yielded 5xx.)
  • Which assets were not posted. (The request yielded 4xx, or was a descendant of another asset which may or may not have been posted.)
>>> from cognite.client.exceptions import CogniteAPIError
>>> try:
...     c.assets.create_hierarchy([root, child, descendant])
>>> except CogniteAPIError as e:
...     assets_posted = e.successful
...     assets_may_have_been_posted = e.unknown
...     assets_not_posted = e.failed

Settings

Client configuration

You can pass configuration arguments directly to the CogniteClient constructor, for example to configure the base url of your requests and additional headers. For a list of all configuration arguments, see the CogniteClient class definition.

Environment configuration

You can set default configurations with these environment variables:

# Can be overrided by Client Configuration
$ export COGNITE_API_KEY = <your-api-key>
$ export COGNITE_PROJECT = <your-default-project>
$ export COGNITE_BASE_URL = http://<host>:<port>
$ export COGNITE_CLIENT_NAME = <user-defined-client-or-app-name>
$ export COGNITE_MAX_WORKERS = <number-of-workers>
$ export COGNITE_TIMEOUT = <num-of-seconds>
$ export COGNITE_FILE_TRANSFER_TIMEOUT = <num-of-seconds>

# Global Configuration
$ export COGNITE_DISABLE_PYPI_VERSION_CHECK = "0"
$ export COGNITE_DISABLE_GZIP = "0"
$ export COGNITE_DISABLE_SSL = "0"
$ export COGNITE_MAX_RETRIES = <number-of-retries>
$ export COGNITE_MAX_RETRY_BACKOFF = <number-of-seconds>
$ export COGNITE_MAX_CONNECTION_POOL_SIZE = <number-of-connections-in-pool>
$ export COGNITE_STATUS_FORCELIST = "429,502,503"

Concurrency and connection pooling

This library does not expose API limits to the user. If your request exceeds API limits, the SDK splits your request into chunks and performs the sub-requests in parallel. To control how many concurrent requests you send to the API, you can either pass the max_workers attribute when you instantiate the CogniteClient or set the COGNITE_MAX_WORKERS environment variable.

If you are working with multiple instances of CogniteClient, all instances will share the same connection pool. If you have several instances, you can increase the max connection pool size to reuse connections if you are performing a large amount of concurrent requests. You can increase the max connection pool size by setting the COGNITE_MAX_CONNECTION_POOL_SIZE environment variable.

Extensions and core library

Pandas integration

The SDK is tightly integrated with the pandas library. You can use the .to_pandas() method on pretty much any object and get a pandas data frame describing the data.

This is particularly useful when you are working with time series data and with tabular data from the Raw API.

Matplotlib integration

You can use the .plot() method on any time series or data points result that the SDK returns. The method takes keyword arguments which are passed on to the underlying matplotlib plot function, allowing you to configure for example the size and layout of your plots.

You need to install the matplotlib package manually:

$ pip install matplotlib

cognite-sdk vs. cognite-sdk-core

If your application doesn’t require the functionality from the pandas or numpy dependencies, you should install the cognite-sdk-core library.

The two libraries are exactly the same, except that cognite-sdk-core does not specify pandas or numpy as dependencies. This means that cognite-sdk-core only has a subset of the features available through the cognite-sdk package. If you attempt to use functionality that cognite-sdk-core does not support, a CogniteImportError is raised.

API

CogniteClient

class cognite.client.CogniteClient(api_key: Optional[str] = None, api_subversion: Optional[str] = None, project: Optional[str] = None, client_name: Optional[str] = None, base_url: Optional[str] = None, max_workers: Optional[int] = None, headers: Optional[Dict[str, str]] = None, timeout: Optional[int] = None, file_transfer_timeout: Optional[int] = None, proxies: Optional[Dict[str, str]] = None, token: Union[str, Callable[[], str], None] = None, token_url: Optional[str] = None, token_client_id: Optional[str] = None, token_client_secret: Optional[str] = None, token_scopes: Optional[List[str]] = None, token_custom_args: Optional[Dict[str, str]] = None, disable_pypi_version_check: Optional[bool] = None, debug: bool = False)

Main entrypoint into Cognite Python SDK.

All services are made available through this object. See examples below.

Parameters:
  • api_key (str) – API key
  • project (str) – Project. Defaults to project of given API key.
  • client_name (str) – A user-defined name for the client. Used to identify number of unique applications/scripts running on top of CDF.
  • base_url (str) – Base url to send requests to. Defaults to “https://api.cognitedata.com
  • max_workers (int) – Max number of workers to spawn when parallelizing data fetching. Defaults to 10.
  • headers (Dict) – Additional headers to add to all requests.
  • timeout (int) – Timeout on requests sent to the api. Defaults to 30 seconds.
  • file_transfer_timeout (int) – Timeout on file upload/download requests. Defaults to 600 seconds.
  • proxies (Dict[str, str]) – Dictionary mapping from protocol to url. e.g. {“https”: “http://10.10.1.10:1080”}
  • token (Union[str, Callable[[], str]]) – A jwt or method which takes no arguments and returns a jwt to use for authentication. This will override any api-key set.
  • token_url (str) – Optional url to use for token generation. This will override the COGNITE_TOKEN_URL environment variable and only be used if both api-key and token are not set.
  • token_client_id (str) – Optional client id to use for token generation. This will override the COGNITE_CLIENT_ID environment variable and only be used if both api-key and token are not set.
  • token_client_secret (str) – Optional client secret to use for token generation. This will override the COGNITE_CLIENT_SECRET environment variable and only be used if both api-key and token are not set.
  • token_scopes (list) – Optional list of scopes to use for token generation. This will override the COGNITE_TOKEN_SCOPES environment variable and only be used if both api-key and token are not set.
  • token_custom_args (Dict) – Optional additional arguments to use for token generation. This will be passed in as optional additional kwargs to OAuth2Session fetch_token and will only be used if both api-key and token are not set.
  • disable_pypi_version_check (bool) – Don’t check for newer versions of the SDK on client creation
  • debug (bool) – Configures logger to log extra request details to stderr.
get(url: str, params: Dict[str, Any] = None, headers: Dict[str, Any] = None)

Perform a GET request to an arbitrary path in the API.

post(url: str, json: Dict[str, Any], params: Dict[str, Any] = None, headers: Dict[str, Any] = None)

Perform a POST request to an arbitrary path in the API.

put(url: str, json: Dict[str, Any] = None, headers: Dict[str, Any] = None)

Perform a PUT request to an arbitrary path in the API.

delete(url: str, params: Dict[str, Any] = None, headers: Dict[str, Any] = None)

Perform a DELETE request to an arbitrary path in the API.

version

Returns the current SDK version.

Returns:The current SDK version
Return type:str
config

Returns a config object containing the configuration for the current client.

Returns:The configuration object.
Return type:ClientConfig

Authentication

Get login status

LoginAPI.status() → cognite.client.data_classes.login.LoginStatus

Check login status

Returns:The login status of the current api key.
Return type:LoginStatus

Examples

Check the current login status and get the project:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> login_status = c.login.status()
>>> project = login_status.project

Data classes

class cognite.client.data_classes.login.LoginStatus(user: str, project: str, logged_in: bool, project_id: int, api_key_id: int)

Bases: cognite.client.data_classes._base.CogniteResponse

Current login status

Parameters:
  • user (str) – Current user.
  • logged_in (bool) – Is user logged in.
  • project (str) – Current project.
  • project_id (int) – Current project id.
  • api_key_id (int) – Current api key id.
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
to_pandas()

Assets

Retrieve an asset by id

AssetsAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.assets.Asset]

Retrieve a single asset by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested asset or None if it does not exist.

Return type:

Optional[Asset]

Examples

Get asset by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.retrieve(id=1)

Get asset by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.retrieve(external_id="1")

Retrieve multiple assets by id

AssetsAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.assets.AssetList

Retrieve multiple assets by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

The requested assets.

Return type:

AssetList

Examples

Get assets by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.retrieve_multiple(ids=[1, 2, 3])

Get assets by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.retrieve_multiple(external_ids=["abc", "def"], ignore_unknown_ids=True)

Retrieve an asset subtree

AssetsAPI.retrieve_subtree(id: int = None, external_id: str = None, depth: int = None) → cognite.client.data_classes.assets.AssetList

Retrieve the subtree for this asset up to a specified depth.

Parameters:
  • id (int) – Id of the root asset in the subtree.
  • external_id (str) – External id of the root asset in the subtree.
  • depth (int) – Retrieve assets up to this depth below the root asset in the subtree. Omit to get the entire subtree.
Returns:

The requested assets or empty AssetList if asset does not exist.

Return type:

AssetList

List assets

AssetsAPI.list(name: str = None, parent_ids: List[int] = None, parent_external_ids: List[str] = None, root_ids: List[int] = None, root_external_ids: List[str] = None, asset_subtree_ids: List[int] = None, asset_subtree_external_ids: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, metadata: Dict[str, str] = None, source: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, root: bool = None, external_id_prefix: str = None, aggregated_properties: List[str] = None, partitions: int = None, limit: int = 25) → cognite.client.data_classes.assets.AssetList

List assets

Parameters:
  • name (str) – Name of asset. Often referred to as tag.
  • parent_ids (List[int]) – Return only the direct descendants of the specified assets.
  • parent_external_ids (List[str]) – Return only the direct descendants of the specified assets.
  • root_ids (List[int], optional) – List of root ids ids to filter on.
  • root_external_ids (List[str], optional) – List of root external ids to filter on.
  • asset_subtree_ids (List[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (List[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (List[int]) – Return only assets in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only assets in the specified data sets with these external ids.
  • labels (LabelFilter) – Return only the assets matching the specified label filter.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value.
  • source (str) – The source of this asset.
  • created_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • last_updated_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • root (bool) – filtered assets are root assets or not.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • aggregated_properties (List[str]) – Set of aggregated properties to include.
  • partitions (int) – Retrieve assets in parallel using this number of workers. Also requires limit=None to be passed.
  • limit (int, optional) – Maximum number of assets to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested assets

Return type:

AssetList

Examples

List assets:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> asset_list = c.assets.list(limit=5)

Iterate over assets:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for asset in c.assets:
...     asset # do something with the asset

Iterate over chunks of assets to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for asset_list in c.assets(chunk_size=2500):
...     asset_list # do something with the assets

Filter assets based on labels:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import LabelFilter
>>> c = CogniteClient()
>>> my_label_filter = LabelFilter(contains_all=["PUMP", "VERIFIED"])
>>> asset_list = c.assets.list(labels=my_label_filter)

Aggregate assets

AssetsAPI.aggregate(filter: Union[cognite.client.data_classes.assets.AssetFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.assets.AssetAggregate]

Aggregate assets

Parameters:filter (Union[AssetFilter, Dict]) – Filter on assets filter with exact match
Returns:List of asset aggregates
Return type:List[AssetAggregate]

Examples

Aggregate assets:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> aggregate_by_prefix = c.assets.aggregate(filter={"external_id_prefix": "prefix"})

Search for assets

AssetsAPI.search(name: str = None, description: str = None, query: str = None, filter: Union[cognite.client.data_classes.assets.AssetFilter, Dict[KT, VT]] = None, limit: int = 100) → cognite.client.data_classes.assets.AssetList

Search for assets Primarily meant for human-centric use-cases and data exploration, not for programs, since matching and ordering may change over time. Use the list function if stable or exact matches are required.

Parameters:
  • name (str) – Fuzzy match on name.
  • description (str) – Fuzzy match on description.
  • query (str) – Whitespace-separated terms to search for in assets. Does a best-effort fuzzy search in relevant fields (currently name and description) for variations of any of the search terms, and orders results by relevance.
  • filter (Union[AssetFilter, Dict]) – Filter to apply. Performs exact match on these fields.
  • limit (int) – Maximum number of results to return.
Returns:

List of requested assets

Return type:

AssetList

Examples

Search for assets by fuzzy search on name:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.search(name="some name")

Search for assets by exact search on name:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.search(filter={"name": "some name"})

Search for assets by improved multi-field fuzzy search:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.search(query="TAG 30 XV")

Search for assets using multiple filters, finding all assets with name similar to xyz with parent asset 123 or 456 with source some source:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.assets.search(name="xyz",filter={"parent_ids": [123,456],"source": "some source"})

Search for an asset with an attached label:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_label_filter = LabelFilter(contains_all=["PUMP"])
>>> res = c.assets.search(name="xyz",filter=AssetFilter(labels=my_label_filter))

Create assets

AssetsAPI.create(asset: Union[cognite.client.data_classes.assets.Asset, List[cognite.client.data_classes.assets.Asset]]) → Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.assets.AssetList]

Create one or more assets.

You can create an arbitrary number of assets, and the SDK will split the request into multiple requests. When specifying parent-child relation between assets using parentExternalId the link will be resvoled into an internal ID and stored as parentId.

Parameters:asset (Union[Asset, List[Asset]]) – Asset or list of assets to create.
Returns:Created asset(s)
Return type:Union[Asset, AssetList]

Examples

Create new assets:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Asset
>>> c = CogniteClient()
>>> assets = [Asset(name="asset1"), Asset(name="asset2")]
>>> res = c.assets.create(assets)

Create asset with label:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Asset, Label
>>> c = CogniteClient()
>>> asset = Asset(name="my_pump", labels=[Label(external_id="PUMP")])
>>> res = c.assets.create(assets)

Create asset hierarchy

AssetsAPI.create_hierarchy(assets: List[cognite.client.data_classes.assets.Asset]) → cognite.client.data_classes.assets.AssetList

Create asset hierarchy. Like the create() method, when posting a large number of assets, the IDE will split the request into smaller requests. However, create_hierarchy() will additionally make sure that the assets are posted in correct order. The ordering is determined from the external_id and parent_external_id properties of the assets, and the external_id is therefore required for all assets. Before posting, it is checked that all assets have a unique external_id and that there are no circular dependencies.

Parameters:assets (List[Asset]]) – List of assets to create. Requires each asset to have a unique external id.
Returns:Created asset hierarchy
Return type:AssetList

Examples

Create asset hierarchy:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Asset
>>> c = CogniteClient()
>>> assets = [Asset(external_id="root", name="root"), Asset(external_id="child1", parent_external_id="root", name="child1"), Asset(external_id="child2", parent_external_id="root", name="child2")]
>>> res = c.assets.create_hierarchy(assets)

Delete assets

AssetsAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None, recursive: bool = False, ignore_unknown_ids: bool = False) → None

Delete one or more assets

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of exgernal ids
  • recursive (bool) – Recursively delete whole asset subtrees under given ids. Defaults to False.
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete assets by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.assets.delete(id=[1,2,3], external_id="3")

Update assets

AssetsAPI.update(item: Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.assets.AssetUpdate, List[Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.assets.AssetUpdate]]]) → Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.assets.AssetList]

Update one or more assets Labels can be added, removed or replaced (set). Note that set operation deletes all the existing labels and adds the new specified labels.

Parameters:item (Union[Asset, AssetUpdate, List[Union[Asset, AssetUpdate]]]) – Asset(s) to update
Returns:Updated asset(s)
Return type:Union[Asset, AssetList]

Examples

Perform a partial update on an asset, updating the description and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import AssetUpdate
>>> c = CogniteClient()
>>> my_update = AssetUpdate(id=1).description.set("New description").metadata.add({"key": "value"})
>>> res1 = c.assets.update(my_update)
>>> # Remove an already set field like so
>>> another_update = AssetUpdate(id=1).description.set(None)
>>> res2 = c.assets.update(another_update)

Remove the metadata on an asset:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import AssetUpdate
>>> c = CogniteClient()
>>> my_update = AssetUpdate(id=1).metadata.add({"key": "value"})
>>> res1 = c.assets.update(my_update)
>>> another_update = AssetUpdate(id=1).metadata.set(None)
>>> # The same result can be achieved with:
>>> another_update2 = AssetUpdate(id=1).metadata.set({})
>>> res2 = c.assets.update(another_update)

Attach labels to an asset:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import AssetUpdate
>>> c = CogniteClient()
>>> my_update = AssetUpdate(id=1).labels.add(["PUMP", "VERIFIED"])
>>> res = c.assets.update(my_update)

Detach a single label from an asset:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import AssetUpdate
>>> c = CogniteClient()
>>> my_update = AssetUpdate(id=1).labels.remove("PUMP")
>>> res = c.assets.update(my_update)

Rewrite all labels for an asset:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import AssetUpdate
>>> c = CogniteClient()
>>> my_update = AssetUpdate(id=1).labels.set("PUMP")
>>> res = c.assets.update(my_update)

Data classes

class cognite.client.data_classes.assets.AggregateResultItem(child_count: int = None, depth: int = None, path: List[Dict[str, Any]] = None, **kwargs)

Bases: dict

Aggregated metrics of the asset

Parameters:
  • child_count (int) – Number of direct descendants for the asset
  • depth (int) – Asset path depth (number of levels below root node).
  • path (List[Dict[str, Any]]) – IDs of assets on the path to the asset.
class cognite.client.data_classes.assets.Asset(external_id: str = None, name: str = None, parent_id: int = None, parent_external_id: str = None, description: str = None, data_set_id: int = None, metadata: Dict[str, str] = None, source: str = None, labels: List[Union[cognite.client.data_classes.labels.Label, str, cognite.client.data_classes.labels.LabelDefinition]] = None, id: int = None, created_time: int = None, last_updated_time: int = None, root_id: int = None, aggregates: Union[Dict[str, Any], cognite.client.data_classes.assets.AggregateResultItem] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a physical asset, for example a factory or a piece of equipment.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • name (str) – The name of the asset.
  • parent_id (int) – The parent of the node, null if it is the root node.
  • parent_external_id (str) – The external ID of the parent. The property is omitted if the asset doesn’t have a parent or if the parent doesn’t have externalId.
  • description (str) – The description of the asset.
  • data_set_id (int) – The id of the dataset this asset belongs to.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 10240 bytes, up to 256 key-value pairs, of total size at most 10240.
  • source (str) – The source of the asset.
  • labels (List[Label]) – A list of the labels associated with this resource item.
  • id (int) – A server-generated ID for the object.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • root_id (int) – ID of the root asset.
  • aggregates (Union[Dict[str, Any], AggregateResultItem]) – Aggregated metrics of the asset
  • cognite_client (CogniteClient) – The client to associate with this object.
children() → cognite.client.data_classes.assets.AssetList

Returns the children of this asset.

Returns:The requested assets
Return type:AssetList
dump(camel_case: bool = False)

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
events(**kwargs) → EventList

Retrieve all events related to this asset.

Returns:All events related to this asset.
Return type:EventList
files(**kwargs) → FileMetadataList

Retrieve all files metadata related to this asset.

Returns:Metadata about all files related to this asset.
Return type:FileMetadataList
parent() → cognite.client.data_classes.assets.Asset

Returns this assets parent.

Returns:The parent asset.
Return type:Asset
sequences(**kwargs) → SequenceList

Retrieve all sequences related to this asset.

Returns:All sequences related to this asset.
Return type:SequenceList
subtree(depth: int = None) → cognite.client.data_classes.assets.AssetList

Returns the subtree of this asset up to a specified depth.

Parameters:depth (int, optional) – Retrieve assets up to this depth below the asset.
Returns:The requested assets sorted topologically.
Return type:AssetList
time_series(**kwargs) → TimeSeriesList

Retrieve all time series related to this asset.

Returns:All time series related to this asset.
Return type:TimeSeriesList
to_pandas(expand: List[str] = ('metadata', 'aggregates'), ignore: List[str] = None, camel_case: bool = True)

Convert the instance into a pandas DataFrame.

Parameters:
  • expand (List[str]) – List of row keys to expand, only works if the value is a Dict.
  • ignore (List[str]) – List of row keys to not include when converting to a data frame.
  • camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns:

The dataframe.

Return type:

pandas.DataFrame

class cognite.client.data_classes.assets.AssetAggregate(count: int = None, **kwargs)

Bases: dict

Aggregation group of assets

Parameters:count (int) – Size of the aggregation group
class cognite.client.data_classes.assets.AssetFilter(name: str = None, parent_ids: List[int] = None, parent_external_ids: List[str] = None, root_ids: List[Dict[str, Any]] = None, asset_subtree_ids: List[Dict[str, Any]] = None, data_set_ids: List[Dict[str, Any]] = None, metadata: Dict[str, str] = None, source: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, root: bool = None, external_id_prefix: str = None, labels: cognite.client.data_classes.labels.LabelFilter = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on assets with strict matching.

Parameters:
  • name (str) – The name of the asset.
  • parent_ids (List[int]) – Return only the direct descendants of the specified assets.
  • parent_external_ids (List[str]) – Return only the direct descendants of the specified assets.
  • root_ids (List[Dict[str, Any]]) – This parameter is deprecated. Use assetSubtreeIds instead. Only include these root assets and their descendants.
  • asset_subtree_ids (List[Dict[str, Any]]) – Only include assets in subtrees rooted at the specified assets (including the roots given). If the total size of the given subtrees exceeds 100,000 assets, an error will be returned.
  • data_set_ids (List[Dict[str, Any]]) – No description.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 10240 bytes, up to 256 key-value pairs, of total size at most 10240.
  • source (str) – The source of the asset.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • root (bool) – Whether the filtered assets are root assets, or not. Set to True to only list root assets.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • labels (LabelFilter) – Return only the resource matching the specified label constraints.
  • cognite_client (CogniteClient) – The client to associate with this object.
dump(camel_case: bool = False)

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
class cognite.client.data_classes.assets.AssetList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

events() → EventList

Retrieve all events related to these assets.

Returns:All events related to the assets in this AssetList.
Return type:EventList
files() → FileMetadataList

Retrieve all files metadata related to these assets.

Returns:Metadata about all files related to the assets in this AssetList.
Return type:FileMetadataList
sequences() → SequenceList

Retrieve all sequences related to these assets.

Returns:All sequences related to the assets in this AssetList.
Return type:SequenceList
time_series() → TimeSeriesList

Retrieve all time series related to these assets.

Returns:All time series related to the assets in this AssetList.
Return type:TimeSeriesList
class cognite.client.data_classes.assets.AssetUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes applied to asset

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.

Labels

List labels

LabelsAPI.list(name: str = None, external_id_prefix: str = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, limit: int = 25) → cognite.client.data_classes.labels.LabelDefinitionList

List Labels

Parameters:
  • name (str) – returns the label definitions matching that name
  • data_set_ids (List[int]) – return only labels in the data sets with these ids.
  • data_set_external_ids (List[str]) – return only labels in the data sets with these external ids.
  • external_id_prefix (str) – filter label definitions with external ids starting with the prefix specified
  • limit (int, optional) – Maximum number of label definitions to return.
Returns:

List of requested Labels

Return type:

LabelDefinitionList

Examples

List Labels and filter on name:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> label_list = c.labels.list(limit=5, name="Pump")

Iterate over label definitions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for label in c.labels:
...     label # do something with the label definition

Iterate over chunks of label definitions to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for label_list in c.labels(chunk_size=2500):
...     label_list # do something with the type definitions

Create a label

LabelsAPI.create(label: Union[cognite.client.data_classes.labels.LabelDefinition, List[cognite.client.data_classes.labels.LabelDefinition]]) → Union[cognite.client.data_classes.labels.LabelDefinition, cognite.client.data_classes.labels.LabelDefinitionList]

Create one or more label definitions.

Parameters:Label (Union[LabelDefinition, List[LabelDefinition]]) – label definition or a list of label definitions to create.
Returns:Created label definition(s)
Return type:Union[LabelDefinition, List[LabelDefinition]]

Examples

Create new label definitions:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import LabelDefinition
>>> c = CogniteClient()
>>> labels = [LabelDefinition(external_id="ROTATING_EQUIPMENT", name="Rotating equipment"), LabelDefinition(external_id="PUMP", name="pump")]
>>> res = c.labels.create(labels)

Delete labels

LabelsAPI.delete(external_id: Union[str, List[str]] = None) → None

Delete one or more label definitions

Parameters:external_id (Union[str, List[str]]) – One or more label external ids
Returns:None

Examples

Delete label definitions by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.labels.delete(external_id=["big_pump", "small_pump"])

Data classes

class cognite.client.data_classes.labels.Label(external_id: str = None, **kwargs)

Bases: dict

A label assigned to a resource.

Parameters:external_id (str) – The external id to the attached label.
class cognite.client.data_classes.labels.LabelDefinition(external_id: str = None, name: str = None, description: str = None, created_time: int = None, data_set_id: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A label definition is a globally defined label that can later be attached to resources (e.g., assets). For example, can you define a “Pump” label definition and attach that label to your pump assets.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • name (str) – Name of the label.
  • description (str) – Description of the label.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • data_set_id (int) – The id of the dataset this label belongs to.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.labels.LabelDefinitionFilter(name: str = None, external_id_prefix: str = None, data_set_ids: List[Dict[str, Any]] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on labels definitions with strict matching.

Parameters:
  • name (str) – Returns the label definitions matching that name.
  • external_id_prefix (str) – filter label definitions with external ids starting with the prefix specified
  • data_set_ids (List[Dict[str, Any]]) – Only include labels that belong to these datasets.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.labels.LabelDefinitionList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.labels.LabelFilter(contains_any: List[str] = None, contains_all: List[str] = None, cognite_client=None)

Bases: dict

Return only the resource matching the specified label constraints.

Parameters:
  • contains_any (List[Label]) – The resource item contains at least one of the listed labels. The labels are defined by a list of external ids.
  • contains_all (List[Label]) – The resource item contains at least all the listed labels. The labels are defined by a list of external ids.
  • cognite_client (CogniteClient) – The client to associate with this object.

Examples

List only resources marked as a PUMP and VERIFIED:

>>> from cognite.client.data_classes import LabelFilter
>>> my_label_filter = LabelFilter(contains_all=["PUMP", "VERIFIED"])

List only resources marked as a PUMP or as a VALVE:

>>> from cognite.client.data_classes import LabelFilter
>>> my_label_filter = LabelFilter(contains_any=["PUMP", "VALVE"])

Events

Retrieve an event by id

EventsAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.events.Event]

Retrieve a single event by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested event or None if it does not exist.

Return type:

Optional[Event]

Examples

Get event by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.events.retrieve(id=1)

Get event by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.events.retrieve(external_id="1")

Retrieve multiple events by id

EventsAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.events.EventList

Retrieve multiple events by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

The requested events.

Return type:

EventList

Examples

Get events by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.events.retrieve_multiple(ids=[1, 2, 3])

Get events by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.events.retrieve_multiple(external_ids=["abc", "def"])

List events

EventsAPI.list(start_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, end_time: Union[Dict[str, Any], cognite.client.data_classes.events.EndTimeFilter] = None, active_at_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, type: str = None, subtype: str = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[int] = None, root_asset_external_ids: List[str] = None, asset_subtree_ids: List[int] = None, asset_subtree_external_ids: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, source: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, external_id_prefix: str = None, sort: List[str] = None, partitions: int = None, limit: int = 25) → cognite.client.data_classes.events.EventList

List events

Parameters:
  • start_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • end_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • active_at_time (Union[Dict[str, Any], TimestampRange]) – Event is considered active from its startTime to endTime inclusive. If startTime is null, event is never active. If endTime is null, event is active from startTime onwards. activeAtTime filter will match all events that are active at some point from min to max, from min, or to max, depending on which of min and max parameters are specified.
  • type (str) – Type of the event, e.g ‘failure’.
  • subtype (str) – Subtype of the event, e.g ‘electrical’.
  • metadata (Dict[str, str]) – Customizable extra data about the event. String key -> String value.
  • asset_ids (List[int]) – Asset IDs of related equipments that this event relates to.
  • asset_external_ids (List[str]) – Asset External IDs of related equipment that this event relates to.
  • root_asset_ids (List[int]) – The IDs of the root assets that the related assets should be children of.
  • root_asset_external_ids (List[str]) – The external IDs of the root assets that the related assets should be children of.
  • asset_subtree_ids (List[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (List[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (List[int]) – Return only events in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only events in the specified data sets with these external ids.
  • source (str) – The source of this event.
  • created_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • last_updated_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • external_id_prefix (str) – External Id provided by client. Should be unique within the project.
  • sort (List[str]) – Sort by array of selected fields. Ex: [“startTime:desc’]. Default sort order is asc when ommitted. Filter accepts following field names: startTime, endTime, createdTime, lastUpdatedTime. We only support 1 field for now.
  • partitions (int) – Retrieve events in parallel using this number of workers. Also requires limit=None to be passed.
  • limit (int, optional) – Maximum number of events to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested events

Return type:

EventList

Examples

List events and filter on max start time:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> event_list = c.events.list(limit=5, start_time={"max": 1500000000})

Iterate over events:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for event in c.events:
...     event # do something with the event

Iterate over chunks of events to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for event_list in c.events(chunk_size=2500):
...     event_list # do something with the events

Aggregate events

EventsAPI.aggregate(filter: Union[cognite.client.data_classes.events.EventFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.shared.AggregateResult]

Aggregate events

Parameters:filter (Union[EventFilter, Dict]) – Filter on events filter with exact match
Returns:List of event aggregates
Return type:List[AggregateResult]

Examples

Aggregate events:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> aggregate_type = c.events.aggregate(filter={"type": "failure"})
EventsAPI.aggregate_unique_values(filter: Union[cognite.client.data_classes.events.EventFilter, Dict[KT, VT]] = None, fields: List[str] = None) → List[cognite.client.data_classes.shared.AggregateUniqueValuesResult]

Aggregate unique values for events

Parameters:
  • filter (Union[EventFilter, Dict]) – Filter on events filter with exact match
  • fields (List[str]) – The field name(s) to apply the aggregation on. Currently limited to one field.
Returns:

List of event aggregates

Return type:

List[AggregateUniqueValuesResult]

Examples

Aggregate events:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> aggregate_subtype = c.events.aggregate_unique_values(filter={"type": "failure"}, fields=["subtype"])

Search for events

EventsAPI.search(description: str = None, filter: Union[cognite.client.data_classes.events.EventFilter, Dict[KT, VT]] = None, limit: int = 100) → cognite.client.data_classes.events.EventList

Search for events Primarily meant for human-centric use-cases and data exploration, not for programs, since matching and ordering may change over time. Use the list function if stable or exact matches are required.

Parameters:
  • description (str) – Fuzzy match on description.
  • filter (Union[EventFilter, Dict]) – Filter to apply. Performs exact match on these fields.
  • limit (int) – Maximum number of results to return.
Returns:

List of requested events

Return type:

EventList

Examples

Search for events:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.events.search(description="some description")

Create events

EventsAPI.create(event: Union[cognite.client.data_classes.events.Event, List[cognite.client.data_classes.events.Event]]) → Union[cognite.client.data_classes.events.Event, cognite.client.data_classes.events.EventList]

Create one or more events.

Parameters:event (Union[Event, List[Event]]) – Event or list of events to create.
Returns:Created event(s)
Return type:Union[Event, EventList]

Examples

Create new events:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Event
>>> c = CogniteClient()
>>> events = [Event(start_time=0, end_time=1), Event(start_time=2, end_time=3)]
>>> res = c.events.create(events)

Delete events

EventsAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None, ignore_unknown_ids: bool = False) → None

Delete one or more events

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of external ids
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete events by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.events.delete(id=[1,2,3], external_id="3")

Update events

EventsAPI.update(item: Union[cognite.client.data_classes.events.Event, cognite.client.data_classes.events.EventUpdate, List[Union[cognite.client.data_classes.events.Event, cognite.client.data_classes.events.EventUpdate]]]) → Union[cognite.client.data_classes.events.Event, cognite.client.data_classes.events.EventList]

Update one or more events

Parameters:item (Union[Event, EventUpdate, List[Union[Event, EventUpdate]]]) – Event(s) to update
Returns:Updated event(s)
Return type:Union[Event, EventList]

Examples

Update an event that you have fetched. This will perform a full update of the event:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> event = c.events.retrieve(id=1)
>>> event.description = "New description"
>>> res = c.events.update(event)

Perform a partial update on a event, updating the description and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import EventUpdate
>>> c = CogniteClient()
>>> my_update = EventUpdate(id=1).description.set("New description").metadata.add({"key": "value"})
>>> res = c.events.update(my_update)

Data classes

class cognite.client.data_classes.events.EndTimeFilter(max: int = None, min: int = None, is_null: bool = None, **kwargs)

Bases: dict

Either range between two timestamps or isNull filter condition.

Parameters:
  • max (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • min (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • is_null (bool) – Set to true if you want to search for data with field value not set, false to search for cases where some value is present.
class cognite.client.data_classes.events.Event(external_id: str = None, data_set_id: int = None, start_time: int = None, end_time: int = None, type: str = None, subtype: str = None, description: str = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, source: str = None, id: int = None, last_updated_time: int = None, created_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

An event represents something that happened at a given interval in time, e.g a failure, a work order etc.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • data_set_id (int) – The id of the dataset this event belongs to.
  • start_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • end_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • type (str) – Type of the event, e.g ‘failure’.
  • subtype (str) – SubType of the event, e.g ‘electrical’.
  • description (str) – Textual description of the event.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 128000 bytes, up to 256 key-value pairs, of total size at most 200000.
  • asset_ids (List[int]) – Asset IDs of equipment that this event relates to.
  • source (str) – The source of this event.
  • id (int) – A server-generated ID for the object.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.events.EventFilter(start_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, end_time: Union[Dict[str, Any], cognite.client.data_classes.events.EndTimeFilter] = None, active_at_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[Dict[str, Any]] = None, asset_subtree_ids: List[Dict[str, Any]] = None, data_set_ids: List[Dict[str, Any]] = None, source: str = None, type: str = None, subtype: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, external_id_prefix: str = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on events filter with exact match

Parameters:
  • start_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • end_time (Union[Dict[str, Any], EndTimeFilter]) – Either range between two timestamps or isNull filter condition.
  • active_at_time (Union[Dict[str, Any], TimestampRange]) – Event is considered active from its startTime to endTime inclusive. If startTime is null, event is never active. If endTime is null, event is active from startTime onwards. activeAtTime filter will match all events that are active at some point from min to max, from min, or to max, depending on which of min and max parameters are specified.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 128000 bytes, up to 256 key-value pairs, of total size at most 200000.
  • asset_ids (List[int]) – Asset IDs of equipment that this event relates to.
  • asset_external_ids (List[str]) – Asset External IDs of equipment that this event relates to.
  • root_asset_ids (List[Dict[str, Any]]) – This parameter is deprecated. Use assetSubtreeIds instead. Only include events that have a related asset in a tree rooted at any of these root assetIds.
  • asset_subtree_ids (List[Dict[str, Any]]) – Only include events that have a related asset in a subtree rooted at any of these assetIds (including the roots given). If the total size of the given subtrees exceeds 100,000 assets, an error will be returned.
  • data_set_ids (List[Dict[str, Any]]) – Only include events that belong to these datasets.
  • source (str) – The source of this event.
  • type (str) – Type of the event, e.g ‘failure’.
  • subtype (str) – SubType of the event, e.g ‘electrical’.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.events.EventList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.events.EventUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes will be applied to event.

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.

Data sets

Retrieve an data set by id

DataSetsAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.data_sets.DataSet]

Retrieve a single data set by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested data set or None if it does not exist.

Return type:

Optional[DataSet]

Examples

Get data set by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.data_sets.retrieve(id=1)

Get data set by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.data_sets.retrieve(external_id="1")

Retrieve multiple data sets by id

DataSetsAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.data_sets.DataSetList

Retrieve multiple data sets by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

The requested data sets.

Return type:

DataSetList

Examples

Get data sets by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.data_sets.retrieve_multiple(ids=[1, 2, 3])

Get data sets by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.data_sets.retrieve_multiple(external_ids=["abc", "def"], ignore_unknown_ids=True)

List data sets

DataSetsAPI.list(metadata: Dict[str, str] = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, external_id_prefix: str = None, write_protected: bool = None, limit: int = 25) → cognite.client.data_classes.data_sets.DataSetList

List data sets

Parameters:
  • metadata (Dict[str, str]) – Custom, application-specific metadata. String key -> String value.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • write_protected (bool) – Specify whether the filtered data sets are write-protected, or not. Set to True to only list write-protected data sets.
  • limit (int, optional) – Maximum number of data sets to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested data sets

Return type:

DataSetList

Examples

List data sets and filter on write_protected:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data_sets_list = c.data_sets.list(limit=5, write_protected=False)

Iterate over data sets:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for data_set in c.data_sets:
...     data_set # do something with the data_set

Iterate over chunks of data sets to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for data_set_list in c.data_sets(chunk_size=2500):
...     data_set_list # do something with the list

Aggregate data sets

DataSetsAPI.aggregate(filter: Union[cognite.client.data_classes.data_sets.DataSetFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.data_sets.DataSetAggregate]

Aggregate data sets

Parameters:filter (Union[DataSetFilter, Dict]) – Filter on data set filter with exact match
Returns:List of data set aggregates
Return type:List[DataSetAggregate]

Examples

Aggregate data_sets:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> aggregate_protected = c.data_sets.aggregate(filter={"write_protected": True})

Create data sets

DataSetsAPI.create(data_set: Union[cognite.client.data_classes.data_sets.DataSet, List[cognite.client.data_classes.data_sets.DataSet]]) → Union[cognite.client.data_classes.data_sets.DataSet, cognite.client.data_classes.data_sets.DataSetList]

Create one or more data sets.

Parameters:data_set – Union[DataSet, List[DataSet]]: Data set or list of data sets to create.
Returns:Created data set(s)
Return type:Union[DataSet, DataSetList]

Examples

Create new data sets:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import DataSet
>>> c = CogniteClient()
>>> data_sets = [DataSet(name="1st level"), DataSet(name="2nd level")]
>>> res = c.data_sets.create(data_sets)

Delete data sets

This functionality is not yet available in the API.

Update data sets

DataSetsAPI.update(item: Union[cognite.client.data_classes.data_sets.DataSet, cognite.client.data_classes.data_sets.DataSetUpdate, List[Union[cognite.client.data_classes.data_sets.DataSet, cognite.client.data_classes.data_sets.DataSetUpdate]]]) → Union[cognite.client.data_classes.data_sets.DataSet, cognite.client.data_classes.data_sets.DataSetList]

Update one or more data sets

Parameters:item – Union[DataSet, DataSetUpdate, List[Union[DataSet, DataSetUpdate]]]: Data set(s) to update
Returns:Updated data set(s)
Return type:Union[DataSet, DataSetList]

Examples

Update a data set that you have fetched. This will perform a full update of the data set:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data_set = c.data_sets.retrieve(id=1)
>>> data_set.description = "New description"
>>> res = c.data_sets.update(data_set)

Perform a partial update on a data set, updating the description and removing a field from metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import DataSetUpdate
>>> c = CogniteClient()
>>> my_update = DataSetUpdate(id=1).description.set("New description").metadata.remove(["key"])
>>> res = c.data_sets.update(my_update)

Data classes

class cognite.client.data_classes.data_sets.DataSet(external_id: str = None, name: str = None, description: str = None, metadata: Dict[str, str] = None, write_protected: bool = None, id: int = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • name (str) – The name of the data set.
  • description (str) – The description of the data set.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 10240 bytes, up to 256 key-value pairs, of total size at most 10240.
  • write_protected (bool) – To write data to a write-protected data set, you need to be a member of a group that has the “datasets:owner” action for the data set. To learn more about write-protected data sets, follow this [guide](/cdf/data_governance/concepts/datasets/#write-protection)
  • id (int) – A server-generated ID for the object.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.data_sets.DataSetAggregate(count: int = None, **kwargs)

Bases: dict

Aggregation group of data sets

Parameters:count (int) – Size of the aggregation group
class cognite.client.data_classes.data_sets.DataSetFilter(metadata: Dict[str, str] = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, external_id_prefix: str = None, write_protected: bool = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on data sets with strict matching.

Parameters:
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 10240 bytes, up to 256 key-value pairs, of total size at most 10240.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • write_protected (bool) – No description.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.data_sets.DataSetList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.data_sets.DataSetUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Update applied to single data set

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.

Files

Retrieve file metadata by id

FilesAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.files.FileMetadata]

Retrieve a single file metadata by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested file metadata or None if it does not exist.

Return type:

Optional[FileMetadata]

Examples

Get file metadata by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.retrieve(id=1)

Get file metadata by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.retrieve(external_id="1")

Retrieve multiple files’ metadata by id

FilesAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None) → cognite.client.data_classes.files.FileMetadataList

Retrieve multiple file metadatas by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
Returns:

The requested file metadatas.

Return type:

FileMetadataList

Examples

Get file metadatas by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.retrieve_multiple(ids=[1, 2, 3])

Get file_metadatas by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.retrieve_multiple(external_ids=["abc", "def"])

List files metadata

FilesAPI.list(name: str = None, mime_type: str = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[int] = None, root_asset_external_ids: List[str] = None, asset_subtree_ids: List[int] = None, asset_subtree_external_ids: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, geo_location: cognite.client.data_classes.files.GeoLocationFilter = None, source: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, source_created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, source_modified_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, uploaded_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, external_id_prefix: str = None, directory_prefix: str = None, uploaded: bool = None, limit: int = 25) → cognite.client.data_classes.files.FileMetadataList

List files

Parameters:
  • name (str) – Name of the file.
  • mime_type (str) – File type. E.g. text/plain, application/pdf, ..
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value
  • asset_ids (List[int]) – Only include files that reference these specific asset IDs.
  • asset_subtree_external_ids (List[str]) – Only include files that reference these specific asset external IDs.
  • root_asset_ids (List[int]) – The IDs of the root assets that the related assets should be children of.
  • root_asset_external_ids (List[str]) – The external IDs of the root assets that the related assets should be children of.
  • asset_subtree_ids (List[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids – List of asset subtrees external ids to filter on.
  • data_set_ids (List[int]) – Return only files in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only files in the specified data sets with these external ids.
  • labels (LabelFilter) – Return only the files matching the specified label filter(s).
  • geo_location (GeoLocationFilter) – Only include files matching the specified geographic relation.
  • source (str) – The source of this event.
  • created_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • last_updated_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • uploaded_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps
  • source_created_time (Union[Dict[str, Any], TimestampRange]) – Filter for files where the sourceCreatedTime field has been set and is within the specified range.
  • source_modified_time (Union[Dict[str, Any], TimestampRange]) – Filter for files where the sourceModifiedTime field has been set and is within the specified range.
  • external_id_prefix (str) – External Id provided by client. Should be unique within the project.
  • directory_prefix (str) – Filter by this (case-sensitive) prefix for the directory provided by the client.
  • uploaded (bool) – Whether or not the actual file is uploaded. This field is returned only by the API, it has no effect in a post body.
  • limit (int, optional) – Max number of files to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The requested files.

Return type:

FileMetadataList

Examples

List files metadata and filter on external id prefix:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> file_list = c.files.list(limit=5, external_id_prefix="prefix")

Iterate over files metadata:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for file_metadata in c.files:
...     file_metadata # do something with the file metadata

Iterate over chunks of files metadata to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for file_list in c.files(chunk_size=2500):
...     file_list # do something with the files

Filter files based on labels:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import LabelFilter
>>> c = CogniteClient()
>>> my_label_filter = LabelFilter(contains_all=["WELL LOG", "VERIFIED"])
>>> file_list = c.files.list(labels=my_label_filter)

Filter files based on geoLocation:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import GeoLocationFilter, GeometryFilter
>>> c = CogniteClient()
>>> my_geo_location_filter = GeoLocationFilter(relation="intersects", shape=GeometryFilter(type="Point", coordinates=[35,10]))
>>> file_list = c.files.list(geo_location=my_geo_location_filter)

Aggregate files metadata

FilesAPI.aggregate(filter: Union[cognite.client.data_classes.files.FileMetadataFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.files.FileAggregate]

Aggregate files

Parameters:filter (Union[FileMetadataFilter, Dict]) – Filter on file metadata filter with exact match
Returns:List of file aggregates
Return type:List[FileAggregate]

Examples

List files metadata and filter on external id prefix:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> aggregate_uploaded = c.files.aggregate(filter={"uploaded": True})

Search for files

FilesAPI.search(name: str = None, filter: Union[cognite.client.data_classes.files.FileMetadataFilter, dict] = None, limit: int = 100) → cognite.client.data_classes.files.FileMetadataList

Search for files. Primarily meant for human-centric use-cases and data exploration, not for programs, since matching and ordering may change over time. Use the list function if stable or exact matches are required.

Parameters:
  • name (str, optional) – Prefix and fuzzy search on name.
  • filter (Union[FileMetadataFilter, dict], optional) – Filter to apply. Performs exact match on these fields.
  • limit (int, optional) – Max number of results to return.
Returns:

List of requested files metadata.

Return type:

FileMetadataList

Examples

Search for a file:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.search(name="some name")

Search for an asset with an attached label:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_label_filter = LabelFilter(contains_all=["WELL LOG"])
>>> res = c.assets.search(name="xyz",filter=FileMetadataFilter(labels=my_label_filter))

Create file metadata

FilesAPI.create(file_metadata: cognite.client.data_classes.files.FileMetadata, overwrite: bool = False) → Tuple[cognite.client.data_classes.files.FileMetadata, str]

Create file without uploading content.

Parameters:
  • file_metadata (FileMetaData) – File metadata for the file to create.
  • overwrite (bool) – If ‘overwrite’ is set to true, and the POST body content specifies a ‘externalId’ field, fields for the file found for externalId can be overwritten. The default setting is false. If metadata is included in the request body, all of the original metadata will be overwritten. File-Asset mappings only change if explicitly stated in the assetIds field of the POST json body. Do not set assetIds in request body if you want to keep the current file-asset mappings.
Returns:

Tuple containing the file metadata and upload url of the created file.

Return type:

Tuple[FileMetaData, str]

Examples

Create a file:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import FileMetadata
>>> c = CogniteClient()
>>> file_metadata = FileMetadata(name="MyFile")
>>> res = c.files.create(file_metadata)

Upload a file or directory

FilesAPI.upload(path: str, external_id: str = None, name: str = None, source: str = None, mime_type: str = None, metadata: Dict[str, str] = None, directory: str = None, asset_ids: List[int] = None, source_created_time: int = None, source_modified_time: int = None, data_set_id: int = None, labels: List[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.files.GeoLocation = None, security_categories: List[int] = None, recursive: bool = False, overwrite: bool = False) → Union[cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.files.FileMetadataList]

Upload a file

Parameters:
  • path (str) – Path to the file you wish to upload. If path is a directory, this method will upload all files in that directory.
  • external_id (str) – The external ID provided by the client. Must be unique within the project.
  • name (str) – Name of the file.
  • source (str) – The source of the file.
  • mime_type (str) – File type. E.g. text/plain, application/pdf, …
  • metadata (Dict[str, str]) – Customizable extra data about the file. String key -> String value.
  • directory (str) – The directory to be associated with this file. Must be an absolute, unix-style path.
  • asset_ids (List[int]) – No description.
  • data_set_id (int) – ID of the data set.
  • labels (List[Label]) – A list of the labels associated with this resource item.
  • geo_location (GeoLocation) – The geographic metadata of the file.
  • security_categories (List[int]) – Security categories to attach to this file.
  • source_created_time (int) – The timestamp for when the file was originally created in the source system.
  • source_modified_time (int) – The timestamp for when the file was last modified in the source system.
  • recursive (bool) – If path is a directory, upload all contained files recursively.
  • overwrite (bool) – If ‘overwrite’ is set to true, and the POST body content specifies a ‘externalId’ field, fields for the file found for externalId can be overwritten. The default setting is false. If metadata is included in the request body, all of the original metadata will be overwritten. The actual file will be overwritten after successful upload. If there is no successful upload, the current file contents will be kept. File-Asset mappings only change if explicitly stated in the assetIds field of the POST json body. Do not set assetIds in request body if you want to keep the current file-asset mappings.
Returns:

The file metadata of the uploaded file(s).

Return type:

Union[FileMetadata, FileMetadataList]

Examples

Upload a file in a given path:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.upload("/path/to/file", name="my_file")

If name is omitted, this method will use the name of the file

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.upload("/path/to/file")

You can also upload all files in a directory by setting path to the path of a directory:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.upload("/path/to/my/directory")

Upload a file with a label:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Label
>>> c = CogniteClient()
>>> res = c.files.upload("/path/to/file", name="my_file", labels=[Label(external_id="WELL LOG")])
Upload a file with a geo_location::
>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import GeoLocation, Geometry
>>> c = CogniteClient()
>>> geometry = Geometry(type="LineString", coordinates=[[30, 10], [10, 30], [40, 40]])
>>> res = c.files.upload("/path/to/file", geo_location=GeoLocation(type="Feature", geometry=geometry))

Upload a string or bytes

FilesAPI.upload_bytes(content: Union[str, bytes, TextIO, BinaryIO], name: str, external_id: str = None, source: str = None, mime_type: str = None, metadata: Dict[str, str] = None, directory: str = None, asset_ids: List[int] = None, data_set_id: int = None, labels: List[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.files.GeoLocation = None, source_created_time: int = None, source_modified_time: int = None, security_categories: List[int] = None, overwrite: bool = False)

Upload bytes or string.

You can also pass a file handle to content.

Parameters:
  • content (Union[str, bytes, TextIO, BinaryIO]) – The content to upload.
  • name (str) – Name of the file.
  • external_id (str) – The external ID provided by the client. Must be unique within the project.
  • source (str) – The source of the file.
  • mime_type (str) – File type. E.g. text/plain, application/pdf,…
  • metadata (Dict[str, str]) – Customizable extra data about the file. String key -> String value.
  • directory (str) – The directory to be associated with this file. Must be an absolute, unix-style path.
  • asset_ids (List[int]) – No description.
  • data_set_id (int) – Id of the data set.
  • labels (List[Label]) – A list of the labels associated with this resource item.
  • geo_location (GeoLocation) – The geographic metadata of the file.
  • source_created_time (int) – The timestamp for when the file was originally created in the source system.
  • source_modified_time (int) – The timestamp for when the file was last modified in the source system.
  • security_categories (List[int]) – Security categories to attach to this file.
  • overwrite (bool) – If ‘overwrite’ is set to true, and the POST body content specifies a ‘externalId’ field, fields for the file found for externalId can be overwritten. The default setting is false. If metadata is included in the request body, all of the original metadata will be overwritten. The actual file will be overwritten after successful upload. If there is no successful upload, the current file contents will be kept. File-Asset mappings only change if explicitly stated in the assetIds field of the POST json body. Do not set assetIds in request body if you want to keep the current file-asset mappings.

Examples

Upload a file from memory:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.files.upload_bytes(b"some content", name="my_file", asset_ids=[1,2,3])

Download files to disk

FilesAPI.download(directory: str, id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None) → None

Download files by id or external id.

This method will stream all files to disk, never keeping more than 2MB of a given file in memory.

Parameters:
  • directory (str) – Directory to download the file(s) to.
  • id (Union[int, List[int]], optional) – Id or list of ids
  • external_id (Union[str, List[str]), optional) – External ID or list of external ids.
Returns:

None

Examples

Download files by id and external id into directory ‘my_directory’:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.files.download(directory="my_directory", id=[1,2,3], external_id=["abc", "def"])

Download a single file to a specific path

FilesAPI.download_to_path(path: str, id: int = None, external_id: str = None)

Download a file to a specific target.

Parameters:
  • path (str) – The path in which to place the file.
  • id (int) – Id of of the file to download.
  • external_id (str) – External id of the file to download.
Returns:

None

Examples

Download a file by id:
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.files.download_to_path("~/mydir/my_downloaded_file.txt", id=123)

Download a file as bytes

FilesAPI.download_bytes(id: int = None, external_id: str = None) → bytes

Download a file as bytes.

Parameters:
  • id (int, optional) – Id of the file
  • external_id (str, optional) – External id of the file

Examples

Download a file’s content into memory:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> file_content = c.files.download_bytes(id=1)

Delete files

FilesAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None) → None

Delete files

Parameters:
  • id (Union[int, List[int]]) – Id or list of ids
  • external_id (Union[str, List[str]]) – str or list of str
Returns:

None

Examples

Delete files by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.files.delete(id=[1,2,3], external_id="3")

Update files metadata

FilesAPI.update(item: Union[cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.files.FileMetadataUpdate, List[Union[cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.files.FileMetadataUpdate]]]) → Union[cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.files.FileMetadataList]

Update files Currently, a full replacement of labels on a file is not supported (only partial add/remove updates). See the example below on how to perform partial labels update.

Parameters:item (Union[FileMetadata, FileMetadataUpdate, List[Union[FileMetadata, FileMetadataUpdate]]]) – file(s) to update.
Returns:The updated files.
Return type:Union[FileMetadata, FileMetadataList]

Examples

Update file metadata that you have fetched. This will perform a full update of the file metadata:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> file_metadata = c.files.retrieve(id=1)
>>> file_metadata.description = "New description"
>>> res = c.files.update(file_metadata)

Perform a partial update on file metadata, updating the source and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import FileMetadataUpdate
>>> c = CogniteClient()
>>> my_update = FileMetadataUpdate(id=1).source.set("new source").metadata.add({"key": "value"})
>>> res = c.files.update(my_update)

Attach labels to a files:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import FileMetadataUpdate
>>> c = CogniteClient()
>>> my_update = FileMetadataUpdate(id=1).labels.add(["PUMP", "VERIFIED"])
>>> res = c.files.update(my_update)

Detach a single label from a file:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import FileMetadataUpdate
>>> c = CogniteClient()
>>> my_update = FileMetadataUpdate(id=1).labels.remove("PUMP")
>>> res = c.files.update(my_update)

Data classes

class cognite.client.data_classes.files.FileAggregate(count: int = None, **kwargs)

Bases: dict

Aggregation results for files

Parameters:count (int) – Number of filtered items included in aggregation
class cognite.client.data_classes.files.FileMetadata(external_id: str = None, name: str = None, source: str = None, mime_type: str = None, metadata: Dict[str, str] = None, directory: str = None, asset_ids: List[int] = None, data_set_id: int = None, labels: List[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.files.GeoLocation = None, source_created_time: int = None, source_modified_time: int = None, security_categories: List[int] = None, id: int = None, uploaded: bool = None, uploaded_time: int = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • name (str) – Name of the file.
  • source (str) – The source of the file.
  • mime_type (str) – File type. E.g. text/plain, application/pdf, ..
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • directory (str) – Directory associated with the file. Must be an absolute, unix-style path.
  • asset_ids (List[int]) – No description.
  • data_set_id (int) – The dataSet Id for the item.
  • labels (List[Label]) – A list of the labels associated with this resource item.
  • geo_location (GeoLocation) – The geographic metadata of the file.
  • source_created_time (int) – The timestamp for when the file was originally created in the source system.
  • source_modified_time (int) – The timestamp for when the file was last modified in the source system.
  • security_categories (List[int]) – The security category IDs required to access this file.
  • id (int) – A server-generated ID for the object.
  • uploaded (bool) – Whether or not the actual file is uploaded. This field is returned only by the API, it has no effect in a post body.
  • uploaded_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.files.FileMetadataFilter(name: str = None, mime_type: str = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[Dict[str, Any]] = None, data_set_ids: List[Dict[str, Any]] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, geo_location: cognite.client.data_classes.files.GeoLocationFilter = None, asset_subtree_ids: List[Dict[str, Any]] = None, source: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, uploaded_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, source_created_time: Dict[str, Any] = None, source_modified_time: Dict[str, Any] = None, external_id_prefix: str = None, directory_prefix: str = None, uploaded: bool = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

No description.

Parameters:
  • name (str) – Name of the file.
  • mime_type (str) – File type. E.g. text/plain, application/pdf, ..
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • asset_ids (List[int]) – Only include files that reference these specific asset IDs.
  • asset_external_ids (List[str]) – Only include files that reference these specific asset external IDs.
  • root_asset_ids (List[Dict[str, Any]]) – Only include files that have a related asset in a tree rooted at any of these root assetIds.
  • data_set_ids (List[Dict[str, Any]]) – Only include files that belong to these datasets.
  • labels (LabelFilter) – Return only the files matching the specified label(s).
  • geo_location (GeoLocationFilter) – Only include files matching the specified geographic relation.
  • asset_subtree_ids (List[Dict[str, Any]]) – Only include files that have a related asset in a subtree rooted at any of these assetIds (including the roots given). If the total size of the given subtrees exceeds 100,000 assets, an error will be returned.
  • source (str) – The source of this event.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • uploaded_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • source_created_time (Dict[str, Any]) – Filter for files where the sourceCreatedTime field has been set and is within the specified range.
  • source_modified_time (Dict[str, Any]) – Filter for files where the sourceModifiedTime field has been set and is within the specified range.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • directory_prefix (str) – Filter by this (case-sensitive) prefix for the directory provided by the client.
  • uploaded (bool) – Whether or not the actual file is uploaded. This field is returned only by the API, it has no effect in a post body.
  • cognite_client (CogniteClient) – The client to associate with this object.
dump(camel_case: bool = False)

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
class cognite.client.data_classes.files.FileMetadataList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.files.FileMetadataUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes will be applied to file.

Args:

class cognite.client.data_classes.files.GeoLocation(type: str, geometry: cognite.client.data_classes.files.Geometry, properties: dict = None)

Bases: dict

A GeoLocation object conforming to the GeoJSON spec.

Args: type (str): The GeoJSON type. Currently only ‘Feature’ is supported.
geometry (object): The geometry type. One of ‘Point’, ‘MultiPoint, ‘LineString’, ‘MultiLineString’, ‘Polygon’, or ‘MultiPolygon’. properties (object): Optional additional properties in a String key -> Object value format.
class cognite.client.data_classes.files.GeoLocationFilter(relation: str, shape: cognite.client.data_classes.files.GeometryFilter)

Bases: dict

Return only the file matching the specified geographic relation.

Args: relation (str): One of the following supported queries: INTERSECTS, DISJOINT, WITHIN.
shape (GeometryFilter): Represents the points, curves and surfaces in the coordinate space.
class cognite.client.data_classes.files.Geometry(type: str, coordinates: List[T])

Bases: dict

Represents the points, curves and surfaces in the coordinate space.

Parameters:
  • type (str) – The geometry type. One of ‘Point’, ‘MultiPoint’, ‘LineString’, ‘MultiLineString’, ‘Polygon’, or ‘MultiPolygon’.
  • coordinates (List) –

    An array of the coordinates of the geometry. The structure of the elements in this array is determined by the type of geometry.

    Point:
    Coordinates of a point in 2D space, described as an array of 2 numbers.

    Example: [4.306640625, 60.205710352530346]

    LineString:
    Coordinates of a line described by a list of two or more points. Each point is defined as a pair of two numbers in an array, representing coordinates of a point in 2D space.

    Example: [[30, 10], [10, 30], [40, 40]]

    Polygon:
    List of one or more linear rings representing a shape. A linear ring is the boundary of a surface or the boundary of a hole in a surface. It is defined as a list consisting of 4 or more Points, where the first and last Point is equivalent. Each Point is defined as an array of 2 numbers, representing coordinates of a point in 2D space.

    Example: [[[35, 10], [45, 45], [15, 40], [10, 20], [35, 10]], [[20, 30], [35, 35], [30, 20], [20, 30]]] type: array

    MultiPoint:
    List of Points. Each Point is defined as an array of 2 numbers, representing coordinates of a point in 2D space.

    Example: [[35, 10], [45, 45]]

    MultiLineString:
    List of lines where each line (LineString) is defined as a list of two or more points. Each point is defined as a pair of two numbers in an array, representing coordinates of a point in 2D space.

    Example: [[[30, 10], [10, 30]], [[35, 10], [10, 30], [40, 40]]]

    MultiPolygon:
    List of multiple polygons.

    Each polygon is defined as a list of one or more linear rings representing a shape.

    A linear ring is the boundary of a surface or the boundary of a hole in a surface. It is defined as a list consisting of 4 or more Points, where the first and last Point is equivalent.

    Each Point is defined as an array of 2 numbers, representing coordinates of a point in 2D space.

    Example: [[[[30, 20], [45, 40], [10, 40], [30, 20]]], [[[15, 5], [40, 10], [10, 20], [5, 10], [15, 5]]]]

class cognite.client.data_classes.files.GeometryFilter(type: str, coordinates: List[T])

Bases: dict

Represents the points, curves and surfaces in the coordinate space.

Args: type (str): The geometry type. One of ‘Point’, ‘LineString’, ‘MultiLineString’, ‘Polygon’, or ‘MultiPolygon’.
coordinates (List): An array of the coordinates of the geometry. The structure of the elements in this array is determined by the type of geometry.

Time series

Retrieve a time series by id

TimeSeriesAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.time_series.TimeSeries]

Retrieve a single time series by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested time series or None if it does not exist.

Return type:

Optional[TimeSeries]

Examples

Get time series by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.retrieve(id=1)

Get time series by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.retrieve(external_id="1")

Retrieve multiple time series by id

TimeSeriesAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.time_series.TimeSeriesList

Retrieve multiple time series by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

The requested time series.

Return type:

TimeSeriesList

Examples

Get time series by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.retrieve_multiple(ids=[1, 2, 3])

Get time series by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.retrieve_multiple(external_ids=["abc", "def"])

List time series

TimeSeriesAPI.list(name: str = None, unit: str = None, is_string: bool = None, is_step: bool = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[int] = None, asset_subtree_ids: List[int] = None, asset_subtree_external_ids: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, metadata: Dict[str, Any] = None, external_id_prefix: str = None, created_time: Dict[str, Any] = None, last_updated_time: Dict[str, Any] = None, partitions: int = None, limit: int = 25, include_metadata=True) → cognite.client.data_classes.time_series.TimeSeriesList

List over time series

Fetches time series as they are iterated over, so you keep a limited number of objects in memory.

Parameters:
  • name (str) – Name of the time series. Often referred to as tag.
  • unit (str) – Unit of the time series.
  • is_string (bool) – Whether the time series is an string time series.
  • is_step (bool) – Whether the time series is a step (piecewise constant) time series.
  • asset_ids (List[int], optional) – List time series related to these assets.
  • asset_external_ids (List[str], optional) – List time series related to these assets.
  • root_asset_ids (List[int], optional) – List time series related to assets under these root assets.
  • asset_subtree_ids (List[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (List[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (List[int]) – Return only assets in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only assets in the specified data sets with these external ids.
  • metadata (Dict[str, Any]) – Custom, application specific metadata. String key -> String value
  • created_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • last_updated_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • limit (int, optional) – Maximum number of time series to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
  • partitions (int) – Retrieve time series in parallel using this number of workers. Also requires limit=None to be passed.
  • include_metadata (bool, optional) – Ignored. Only present in parameter list for backward compatibility.
Returns:

The requested time series.

Return type:

TimeSeriesList

Examples

List time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.list(limit=5)

Iterate over time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for ts in c.time_series:
...     ts # do something with the time_series

Iterate over chunks of time series to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for ts_list in c.time_series(chunk_size=2500):
...     ts_list # do something with the time_series

Aggregate time series

TimeSeriesAPI.aggregate(filter: Union[cognite.client.data_classes.time_series.TimeSeriesFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.time_series.TimeSeriesAggregate]

Aggregate time series

Parameters:filter (Union[TimeSeriesFilter, Dict]) – Filter on time series filter with exact match
Returns:List of sequence aggregates
Return type:List[TimeSeriesAggregate]

Examples

List time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.aggregate(filter={"unit": "kpa"})

Search for time series

TimeSeriesAPI.search(name: str = None, description: str = None, query: str = None, filter: Union[cognite.client.data_classes.time_series.TimeSeriesFilter, Dict[KT, VT]] = None, limit: int = 100) → cognite.client.data_classes.time_series.TimeSeriesList

Search for time series. Primarily meant for human-centric use-cases and data exploration, not for programs, since matching and ordering may change over time. Use the list function if stable or exact matches are required.

Parameters:
  • name (str, optional) – Prefix and fuzzy search on name.
  • description (str, optional) – Prefix and fuzzy search on description.
  • query (str, optional) – Search on name and description using wildcard search on each of the words (separated by spaces). Retrieves results where at least one word must match. Example: ‘some other’
  • filter (Union[TimeSeriesFilter, Dict], optional) – Filter to apply. Performs exact match on these fields.
  • limit (int, optional) – Max number of results to return.
Returns:

List of requested time series.

Return type:

TimeSeriesList

Examples

Search for a time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.search(name="some name")

Search for all time series connected to asset with id 123:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.search(filter={"asset_ids":[123]})

Create time series

TimeSeriesAPI.create(time_series: Union[cognite.client.data_classes.time_series.TimeSeries, List[cognite.client.data_classes.time_series.TimeSeries]]) → Union[cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.time_series.TimeSeriesList]

Create one or more time series.

Parameters:time_series (Union[TimeSeries, List[TimeSeries]]) – TimeSeries or list of TimeSeries to create.
Returns:The created time series.
Return type:Union[TimeSeries, TimeSeriesList]

Examples

Create a new time series:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TimeSeries
>>> c = CogniteClient()
>>> ts = c.time_series.create(TimeSeries(name="my ts"))

Delete time series

TimeSeriesAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None, ignore_unknown_ids: bool = False) → None

Delete one or more time series.

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of external ids
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete time series by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.time_series.delete(id=[1,2,3], external_id="3")

Update time series

TimeSeriesAPI.update(item: Union[cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.time_series.TimeSeriesUpdate, List[Union[cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.time_series.TimeSeriesUpdate]]]) → Union[cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.time_series.TimeSeriesList]

Update one or more time series.

Parameters:item (Union[TimeSeries, TimeSeriesUpdate, List[Union[TimeSeries, TimeSeriesUpdate]]]) – Time series to update
Returns:Updated time series.
Return type:Union[TimeSeries, TimeSeriesList]

Examples

Update a time series that you have fetched. This will perform a full update of the time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.retrieve(id=1)
>>> res.description = "New description"
>>> res = c.time_series.update(res)

Perform a partial update on a time series, updating the description and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TimeSeriesUpdate
>>> c = CogniteClient()
>>> my_update = TimeSeriesUpdate(id=1).description.set("New description").metadata.add({"key": "value"})
>>> res = c.time_series.update(my_update)

Data classes

class cognite.client.data_classes.time_series.TimeSeries(id: int = None, external_id: str = None, name: str = None, is_string: bool = None, metadata: Dict[str, str] = None, unit: str = None, asset_id: int = None, is_step: bool = None, description: str = None, security_categories: List[int] = None, data_set_id: int = None, created_time: int = None, last_updated_time: int = None, legacy_name: str = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The externally supplied ID for the time series.
  • name (str) – The display short name of the time series. Note: Value of this field can differ from name presented by older versions of API 0.3-0.6.
  • is_string (bool) – Whether the time series is string valued or not.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • unit (str) – The physical unit of the time series.
  • asset_id (int) – Asset ID of equipment linked to this time series.
  • is_step (bool) – Whether the time series is a step series or not.
  • description (str) – Description of the time series.
  • security_categories (List[int]) – The required security categories to access this time series.
  • data_set_id (int) – The dataSet Id for the item.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • legacy_name (str) – Set a value for legacyName to allow applications using API v0.3, v04, v05, and v0.6 to access this time series. The legacy name is the human-readable name for the time series and is mapped to the name field used in API versions 0.3-0.6. The legacyName field value must be unique, and setting this value to an already existing value will return an error. We recommend that you set this field to the same value as externalId.
  • cognite_client (CogniteClient) – The client to associate with this object.
asset() → Asset

Returns the asset this time series belongs to.

Returns:The asset given by its asset_id.
Return type:Asset
count() → int

Returns the number of datapoints in this time series.

This result may not be completely accurate, as it is based on aggregates which may be occasionally out of date.

Returns:The number of datapoints in this time series.
Return type:int
first() → Optional[Datapoint]

Returns the first datapoint in this time series.

Returns:A datapoint object containing the value and timestamp of the first datapoint.
Return type:Datapoint
latest() → Optional[Datapoint]

Returns the latest datapoint in this time series

Returns:A datapoint object containing the value and timestamp of the latest datapoint.
Return type:Datapoint
class cognite.client.data_classes.time_series.TimeSeriesAggregate(count: int = None, **kwargs)

Bases: dict

No description.

Parameters:count (int) – No description.
class cognite.client.data_classes.time_series.TimeSeriesFilter(name: str = None, unit: str = None, is_string: bool = None, is_step: bool = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, asset_external_ids: List[str] = None, root_asset_ids: List[int] = None, asset_subtree_ids: List[Dict[str, Any]] = None, data_set_ids: List[Dict[str, Any]] = None, external_id_prefix: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

No description.

Parameters:
  • name (str) – Filter on name.
  • unit (str) – Filter on unit.
  • is_string (bool) – Filter on isString.
  • is_step (bool) – Filter on isStep.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • asset_ids (List[int]) – Only include time series that reference these specific asset IDs.
  • asset_external_ids (List[str]) – Asset External IDs of related equipment that this time series relates to.
  • root_asset_ids (List[int]) – Only include time series that have a related asset in a tree rooted at any of these root assetIds.
  • asset_subtree_ids (List[Dict[str, Any]]) – Only include time series that are related to an asset in a subtree rooted at any of these assetIds (including the roots given). If the total size of the given subtrees exceeds 100,000 assets, an error will be returned.
  • data_set_ids (List[Dict[str, Any]]) – No description.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.time_series.TimeSeriesList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.time_series.TimeSeriesUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes will be applied to time series.

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.

Synthetic time series

Calculate the result of a function on time series

SyntheticDatapointsAPI.query(expressions: Union[str, sympy.Expr, List[Union[str, sympy.Expr]]], start: Union[int, str, datetime.datetime], end: Union[int, str, datetime.datetime], limit: int = None, variables: Dict[str, Union[str, cognite.client.data_classes.time_series.TimeSeries]] = None, aggregate: str = None, granularity: str = None) → Union[cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsList]

Calculate the result of a function on time series.

Parameters:
  • expressions (Union[str, "sympy.Expr", List[Union[str, "sympy.Expr"]]]) – Functions to be calculated. Supports both strings and sympy expressions. Strings can have either the API ts{} syntax, or contain variable names to be replaced using the variables parameter.
  • start (Union[int, str, datetime]) – Inclusive start.
  • end (Union[int, str, datetime]) – Exclusive end
  • limit (int) – Number of datapoints per expression to retrieve.
  • variables (Dict[str,Union[str,TimeSeries]]) – An optional map of symbol replacements.
  • aggregate (str) – use this aggregate when replacing entries from variables, does not affect time series given in the ts{} syntax.
  • granularity (str) – use this granularity with the aggregate.
Returns:

A DatapointsList object containing the calculated data.

Return type:

Union[Datapoints, DatapointsList]

Examples

Request a synthetic time series query with direct syntax

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> dps = c.datapoints.synthetic.query(expressions="TS{id:123} + TS{externalId:'abc'}", start="2w-ago", end="now")

Use variables to re-use an expression:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> vars = {"A": "my_ts_external_id", "B": client.time_series.retrieve(id=1)}
>>> dps = c.datapoints.synthetic.query(expressions="A+B", start="2w-ago", end="now", variables=vars)

Use sympy to build complex expressions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> from sympy import symbols, cos, sin
>>> a = symbols('a')
>>> dps = c.datapoints.synthetic.query([sin(a), cos(a)], start="2w-ago", end="now", variables={"a": "my_ts_external_id"}, aggregate='interpolation', granularity='1m')

Data points

Retrieve datapoints

DatapointsAPI.retrieve(start: Union[int, str, datetime.datetime], end: Union[int, str, datetime.datetime], id: Union[int, List[int], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, external_id: Union[str, List[str], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, aggregates: List[str] = None, granularity: str = None, include_outside_points: bool = None, limit: int = None, ignore_unknown_ids: bool = False) → Union[None, cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsList]

Get datapoints for one or more time series.

Note that you cannot specify the same ids/external_ids multiple times.

Parameters:
  • start (Union[int, str, datetime]) – Inclusive start.
  • end (Union[int, str, datetime]) – Exclusive end.
  • id (Union[int, List[int], Dict[str, Any], List[Dict[str, Any]]]) – Id or list of ids. Can also be object specifying aggregates. See example below.
  • external_id (Union[str, List[str], Dict[str, Any], List[Dict[str, Any]]]) – External id or list of external ids. Can also be object specifying aggregates. See example below.
  • aggregates (List[str]) – List of aggregate functions to apply.
  • granularity (str) – The granularity to fetch aggregates at. e.g. ‘1s’, ‘2h’, ‘10d’.
  • include_outside_points (bool) – Whether or not to include outside points.
  • limit (int) – Maximum number of datapoints to return for each time series.
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

A Datapoints object containing the requested data, or a list of such objects. If ignore_unknown_id is True, single id is requested and it is not found, the function will return None.

Return type:

Union[None, Datapoints, DatapointsList]

Examples

You can get specify the ids of the datapoints you wish to retrieve in a number of ways. In this example we are using the time-ago format to get raw data for the time series with id 1:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> dps = c.datapoints.retrieve(id=1, start="2w-ago", end="now")

We can also get aggregated values, such as average. Here we are getting daily averages for all of 2018 for two different time series. Note that we are fetching them using their external ids:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> dps = c.datapoints.retrieve(external_id=["abc", "def"],
...                         start=datetime(2018,1,1),
...                         end=datetime(2019,1,1),
...                         aggregates=["average"],
...                         granularity="1d")

If you want different aggregates for different time series specify your ids like this:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> dps = c.datapoints.retrieve(id=[{"id": 1, "aggregates": ["average"]},
...                             {"id": 1, "aggregates": ["min"]}],
...                         external_id={"externalId": "1", "aggregates": ["max"]},
...                         start="1d-ago", end="now", granularity="1h")

Retrieve pandas dataframe

DatapointsAPI.retrieve_dataframe(start: Union[int, str, datetime.datetime], end: Union[int, str, datetime.datetime], aggregates: List[str], granularity: str, id: Union[int, List[int], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, external_id: Union[str, List[str], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, limit: int = None, include_aggregate_name=True, complete: str = None, ignore_unknown_ids: bool = False) → pandas.DataFrame

Get a pandas dataframe describing the requested data.

Note that you cannot specify the same ids/external_ids multiple times.

Parameters:
  • start (Union[int, str, datetime]) – Inclusive start.
  • end (Union[int, str, datetime]) – Exclusive end.
  • aggregates (List[str]) – List of aggregate functions to apply.
  • granularity (str) – The granularity to fetch aggregates at. e.g. ‘1s’, ‘2h’, ‘10d’.
  • id (Union[int, List[int], Dict[str, Any], List[Dict[str, Any]]]) – Id or list of ids. Can also be object specifying aggregates. See example below.
  • external_id (Union[str, List[str], Dict[str, Any], List[Dict[str, Any]]]) – External id or list of external ids. Can also be object specifying aggregates. See example below.
  • limit (int) – Maximum number of datapoints to return for each time series.
  • include_aggregate_name (bool) – Include ‘aggregate’ in the column name. Defaults to True and should only be set to False when only a single aggregate is requested per id/external id.
  • complete (str) – Post-processing of the dataframe.
  • ignore_unknown_ids (bool) –

    Ignore IDs and external IDs that are not found rather than throw an exception.

    Pass ‘fill’ to insert missing entries into the index, and complete data where possible (supports interpolation, stepInterpolation, count, sum, totalVariation).

    Pass ‘fill,dropna’ to additionally drop rows in which any aggregate for any time series has missing values (typically rows at the start and end for interpolation aggregates). This option guarantees that all returned dataframes have the exact same shape and no missing values anywhere, and is only supported for aggregates sum, count, totalVariation, interpolation and stepInterpolation.

Returns:

The requested dataframe

Return type:

pandas.DataFrame

Examples

Get a pandas dataframe:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> df = c.datapoints.retrieve_dataframe(id=[1,2,3], start="2w-ago", end="now",
...         aggregates=["average","sum"], granularity="1h")

Get a pandas dataframe with the index regularly spaced at 1 minute intervals, missing values completed and without the aggregate name in the columns:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> df = c.datapoints.retrieve_dataframe(id=[1,2,3], start="2w-ago", end="now",
...         aggregates=["interpolation"], granularity="1m", include_aggregate_name=False, complete="fill,dropna")

Retrieve pandas dataframes indexed by aggregate

DatapointsAPI.retrieve_dataframe_dict(start: Union[int, str, datetime.datetime], end: Union[int, str, datetime.datetime], aggregates: List[str], granularity: str, id: Union[int, List[int], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, external_id: Union[str, List[str], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, limit: int = None, ignore_unknown_ids: bool = False, complete: bool = None) → Dict[str, pandas.DataFrame]

Get a dictionary of aggregate: pandas dataframe describing the requested data.

Parameters:
  • start (Union[int, str, datetime]) – Inclusive start.
  • end (Union[int, str, datetime]) – Exclusive end.
  • aggregates (List[str]) – List of aggregate functions to apply.
  • granularity (str) – The granularity to fetch aggregates at. e.g. ‘1s’, ‘2h’, ‘10d’.
  • (Union[int, List[int], Dict[str, Any], List[Dict[str, Any]]] (id) – Id or list of ids. Can also be object specifying aggregates.
  • external_id (Union[str, List[str], Dict[str, Any], List[Dict[str, Any]]]) – External id or list of external ids. Can also be object specifying aggregates.
  • limit (int) – Maximum number of datapoints to return for each time series.
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
  • complete (str) –

    Post-processing of the dataframe.

    Pass ‘fill’ to insert missing entries into the index, and complete data where possible (supports interpolation, stepInterpolation, count, sum, totalVariation).

    Pass ‘fill,dropna’ to additionally drop rows in which any aggregate for any time series has missing values (typically rows at the start and end for interpolation aggregates). This option guarantees that all returned dataframes have the exact same shape and no missing values anywhere, and is only supported for aggregates sum, count, totalVariation, interpolation and stepInterpolation.

Returns:

A dictionary of aggregate: dataframe.

Return type:

Dict[str,pandas.DataFrame]

Examples

Get a dictionary of pandas dataframes, with the index evenly spaced at 1h intervals, missing values completed in the middle and incomplete entries dropped at the start and end:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> dfs = c.datapoints.retrieve_dataframe_dict(id=[1,2,3], start="2w-ago", end="now",
...          aggregates=["interpolation","count"], granularity="1h", complete="fill,dropna")

Perform data points queries

DatapointsAPI.query(query: Union[cognite.client.data_classes.datapoints.DatapointsQuery, List[cognite.client.data_classes.datapoints.DatapointsQuery]]) → Union[cognite.client.data_classes.datapoints.DatapointsList, List[cognite.client.data_classes.datapoints.DatapointsList]]

Get datapoints for one or more time series

This method is different from get() in that you can specify different start times, end times, and granularities for each requested time series.

Parameters:query (Union[DatapointsQuery, List[DatapointsQuery]) – List of datapoint queries.
Returns:The requested DatapointsList(s).
Return type:Union[DatapointsList, List[DatapointsList]]

Examples

This method is useful if you want to get multiple time series, but you want to specify different starts, ends, or granularities for each. e.g.:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import DatapointsQuery
>>> c = CogniteClient()
>>> queries = [DatapointsQuery(id=1, start="2d-ago", end="now"),
...             DatapointsQuery(external_id="abc",
...                             start="10d-ago",
...                             end="now",
...                             aggregates=["average"],
...                             granularity="1m")]
>>> res = c.datapoints.query(queries)

Retrieve latest datapoint

DatapointsAPI.retrieve_latest(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None, before: Union[int, str, datetime.datetime] = None, ignore_unknown_ids: bool = False) → Union[cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsList]

Get the latest datapoint for one or more time series

Parameters:
  • (Union[int, List[int]] (id) – Id or list of ids.
  • external_id (Union[str, List[str]) – External id or list of external ids.
  • before – Union[int, str, datetime]: Get latest datapoint before this time.
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

A Datapoints object containing the requested data, or a list of such objects.

Return type:

Union[Datapoints, DatapointsList]

Examples

Getting the latest datapoint in a time series. This method returns a Datapoints object, so the datapoint will be the first element:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.datapoints.retrieve_latest(id=1)[0]

You can also get the first datapoint before a specific time:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.datapoints.retrieve_latest(id=1, before="2d-ago")[0]

If you need the latest datapoint for multiple time series simply give a list of ids. Note that we are using external ids here, but either will work:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.datapoints.retrieve_latest(external_id=["abc", "def"])
>>> latest_abc = res[0][0]
>>> latest_def = res[1][0]

Insert data points

DatapointsAPI.insert(datapoints: Union[List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]], id: int = None, external_id: str = None) → None

Insert datapoints into a time series

Timestamps can be represented as milliseconds since epoch or datetime objects.

Parameters:
  • datapoints (Union[List[Dict], List[Tuple],Datapoints]) – The datapoints you wish to insert. Can either be a list of tuples, a list of dictionaries, or a Datapoints object. See examples below.
  • id (int) – Id of time series to insert datapoints into.
  • external_id (str) – External id of time series to insert datapoint into.
Returns:

None

Examples

Your datapoints can be a list of tuples where the first element is the timestamp and the second element is the value:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> # with datetime objects
>>> datapoints = [(datetime(2018,1,1), 1000), (datetime(2018,1,2), 2000)]
>>> c.datapoints.insert(datapoints, id=1)
>>> # with ms since epoch
>>> datapoints = [(150000000000, 1000), (160000000000, 2000)]
>>> c.datapoints.insert(datapoints, id=2)

Or they can be a list of dictionaries:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> # with datetime objects
>>> datapoints = [{"timestamp": datetime(2018,1,1), "value": 1000},
...    {"timestamp": datetime(2018,1,2), "value": 2000}]
>>> c.datapoints.insert(datapoints, external_id="abc")
>>> # with ms since epoch
>>> datapoints = [{"timestamp": 150000000000, "value": 1000},
...    {"timestamp": 160000000000, "value": 2000}]
>>> c.datapoints.insert(datapoints, external_id="def")

Or they can be a Datapoints object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data = c.datapoints.retrieve(external_id="abc",start=datetime(2018,1,1),end=datetime(2018,2,2))
>>> c.datapoints.insert(data, external_id="def")

Insert data points into multiple time series

DatapointsAPI.insert_multiple(datapoints: List[Dict[str, Union[str, int, List[T]]]]) → None

Insert datapoints into multiple time series

Parameters:datapoints (List[Dict]) – The datapoints you wish to insert along with the ids of the time series. See examples below.
Returns:None

Examples

Your datapoints can be a list of tuples where the first element is the timestamp and the second element is the value:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()

>>> datapoints = []
>>> # with datetime objects and id
>>> datapoints.append({"id": 1, "datapoints": [(datetime(2018,1,1), 1000), (datetime(2018,1,2), 2000)]})
>>> # with ms since epoch and externalId
>>> datapoints.append({"externalId": 1, "datapoints": [(150000000000, 1000), (160000000000, 2000)]})

>>> c.datapoints.insert_multiple(datapoints)

Or they can be a list of dictionaries:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()

>>> datapoints = []
>>> # with datetime objects and external id
>>> datapoints.append({"externalId": "1", "datapoints": [{"timestamp": datetime(2018,1,1), "value": 1000},
...                     {"timestamp": datetime(2018,1,2), "value": 2000}]})
>>> # with ms since epoch and id
>>> datapoints.append({"id": 1, "datapoints": [{"timestamp": 150000000000, "value": 1000},
...                     {"timestamp": 160000000000, "value": 2000}]})

>>> c.datapoints.insert_multiple(datapoints)

Insert pandas dataframe

DatapointsAPI.insert_dataframe(dataframe, external_id_headers: bool = False, dropna: bool = False)

Insert a dataframe.

The index of the dataframe must contain the timestamps. The names of the remaining columns specify the ids or external ids of the time series to which column contents will be written.

Said time series must already exist.

Parameters:
  • dataframe (pandas.DataFrame) – Pandas DataFrame Object containing the time series.
  • external_id_headers (bool) – Set to True if the column headers are external ids rather than internal ids. Defaults to False.
  • dropna (bool) – Set to True to skip NaNs in the given DataFrame, applied per column.
Returns:

None

Examples

Post a dataframe with white noise:

>>> import numpy as np
>>> import pandas as pd
>>> from cognite.client import CogniteClient
>>> from datetime import datetime, timedelta
>>>
>>> c = CogniteClient()
>>> ts_id = 123
>>> start = datetime(2018, 1, 1)
>>> x = pd.DatetimeIndex([start + timedelta(days=d) for d in range(100)])
>>> y = np.random.normal(0, 1, 100)
>>> df = pd.DataFrame({ts_id: y}, index=x)
>>> c.datapoints.insert_dataframe(df)

Delete a range of data points

DatapointsAPI.delete_range(start: Union[int, str, datetime.datetime], end: Union[int, str, datetime.datetime], id: int = None, external_id: str = None) → None

Delete a range of datapoints from a time series.

Parameters:
  • start (Union[int, str, datetime]) – Inclusive start of delete range
  • end (Union[int, str, datetime]) – Exclusvie end of delete range
  • id (int) – Id of time series to delete data from
  • external_id (str) – External id of time series to delete data from
Returns:

None

Examples

Deleting the last week of data from a time series:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.datapoints.delete_range(start="1w-ago", end="now", id=1)

Delete ranges of data points

DatapointsAPI.delete_ranges(ranges: List[Dict[str, Any]]) → None

Delete a range of datapoints from multiple time series.

Parameters:ranges (List[Dict[str, Any]]) – The list of datapoint ids along with time range to delete. See examples below.
Returns:None

Examples

Each element in the list ranges must be specify either id or externalId, and a range:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> ranges = [{"id": 1, "start": "2d-ago", "end": "now"},
...             {"externalId": "abc", "start": "2d-ago", "end": "now"}]
>>> c.datapoints.delete_ranges(ranges)

Data classes

class cognite.client.data_classes.datapoints.Datapoint(timestamp: Union[int, float] = None, value: Union[str, int, float] = None, average: float = None, max: float = None, min: float = None, count: int = None, sum: float = None, interpolation: float = None, step_interpolation: float = None, continuous_variance: float = None, discrete_variance: float = None, total_variation: float = None)

Bases: cognite.client.data_classes._base.CogniteResource

An object representing a datapoint.

Parameters:
  • timestamp (Union[int, float]) – The data timestamp in milliseconds since the epoch (Jan 1, 1970).
  • value (Union[str, int, float]) – The data value. Can be String or numeric depending on the metric
  • average (float) – The integral average value in the aggregate period
  • max (float) – The maximum value in the aggregate period
  • min (float) – The minimum value in the aggregate period
  • count (int) – The number of datapoints in the aggregate period
  • sum (float) – The sum of the datapoints in the aggregate period
  • interpolation (float) – The interpolated value of the series in the beginning of the aggregate
  • step_interpolation (float) – The last value before or at the beginning of the aggregate.
  • continuous_variance (float) – The variance of the interpolated underlying function.
  • discrete_variance (float) – The variance of the datapoint values.
  • total_variation (float) – The total variation of the interpolated underlying function.
to_pandas(camel_case=True) → pandas.DataFrame
Convert the datapoint into a pandas DataFrame.
camel_case (bool): Convert column names to camel case (e.g. stepInterpolation instead of step_interpolation)
Returns:The dataframe.
Return type:pandas.DataFrame
class cognite.client.data_classes.datapoints.Datapoints(id: int = None, external_id: str = None, is_string: bool = None, is_step: bool = None, unit: str = None, timestamp: List[Union[int, float]] = None, value: List[Union[int, str, float]] = None, average: List[float] = None, max: List[float] = None, min: List[float] = None, count: List[int] = None, sum: List[float] = None, interpolation: List[float] = None, step_interpolation: List[float] = None, continuous_variance: List[float] = None, discrete_variance: List[float] = None, total_variation: List[float] = None, error: List[Optional[str]] = None)

Bases: object

An object representing a list of datapoints.

Parameters:
  • id (int) – Id of the timeseries the datapoints belong to
  • external_id (str) – External id of the timeseries the datapoints belong to (Only if id is not set)
  • is_string (bool) – Whether the time series is string valued or not.
  • is_step (bool) – Whether the time series is a step series or not.
  • unit (str) – The physical unit of the time series.
  • timestamp (List[Union[int, float]]) – The data timestamps in milliseconds since the epoch (Jan 1, 1970).
  • value (List[Union[int, str, float]]) – The data values. Can be String or numeric depending on the metric
  • average (List[float]) – The integral average values in the aggregate period
  • max (List[float]) – The maximum values in the aggregate period
  • min (List[float]) – The minimum values in the aggregate period
  • count (List[int]) – The number of datapoints in the aggregate periods
  • sum (List[float]) – The sum of the datapoints in the aggregate periods
  • interpolation (List[float]) – The interpolated values of the series in the beginning of the aggregates
  • step_interpolation (List[float]) – The last values before or at the beginning of the aggregates.
  • continuous_variance (List[float]) – The variance of the interpolated underlying function.
  • discrete_variance (List[float]) – The variance of the datapoint values.
  • total_variation (List[float]) – The total variation of the interpolated underlying function.
dump(camel_case: bool = False) → Dict[str, Any]

Dump the datapoints into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A list of dicts representing the instance.
Return type:List[Dict[str, Any]]
plot(*args, **kwargs) → None

Plot the datapoints.

to_pandas(column_names: str = 'externalId', include_aggregate_name: bool = True, include_errors: bool = False) → pandas.DataFrame

Convert the datapoints into a pandas DataFrame.

Parameters:
  • column_names (str) – Which field to use as column header. Defaults to “externalId”, can also be “id”.
  • include_aggregate_name (bool) – Include aggregate in the column name
  • include_errors (bool) – For synthetic datapoint queries, include a column with errors.
Returns:

The dataframe.

Return type:

pandas.DataFrame

class cognite.client.data_classes.datapoints.DatapointsList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

plot(*args, **kwargs) → None

Plot the list of datapoints.

to_pandas(column_names: str = 'externalId', include_aggregate_name: bool = True) → pandas.DataFrame

Convert the datapoints list into a pandas DataFrame.

Parameters:
  • column_names (str) – Which field to use as column header. Defaults to “externalId”, can also be “id”.
  • include_aggregate_name (bool) – Include aggregate in the column name
Returns:

The datapoints list as a pandas DataFrame.

Return type:

pandas.DataFrame

class cognite.client.data_classes.datapoints.DatapointsQuery(start: Union[str, int, datetime.datetime], end: Union[str, int, datetime.datetime], id: Union[int, List[int], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, external_id: Union[str, List[str], Dict[str, Union[int, List[str]]], List[Dict[str, Union[int, List[str]]]]] = None, limit: int = None, aggregates: List[str] = None, granularity: str = None, include_outside_points: bool = None, ignore_unknown_ids: bool = False)

Bases: cognite.client.data_classes._base.CogniteResource

Parameters describing a query for datapoints.

Parameters:
  • start (Union[str, int, datetime]) – Get datapoints after this time. Format is N[timeunit]-ago where timeunit is w,d,h,m,s. Example: ‘2d-ago’ will get everything that is up to 2 days old. Can also send time in ms since epoch.
  • end (Union[str, int, datetime]) – Get datapoints up to this time. The format is the same as for start.
  • (Union[int, List[int], Dict[str, Any], List[Dict[str, Any]]] (id) –
    Id or list of ids. Can also be object
    specifying aggregates. See example below.
    external_id (Union[str, List[str], Dict[str, Any], List[Dict[str, Any]]]): External id or list of external
    ids. Can also be object specifying aggregates. See example below.
  • limit (int) – Return up to this number of datapoints.
  • aggregates (List[str]) – The aggregates to be returned. Use default if null. An empty string must be sent to get raw data if the default is a set of aggregates.
  • granularity (str) – The granularity size and granularity of the aggregates.
  • include_outside_points (bool) – Whether to include the last datapoint before the requested time period,and the first one after the requested period. This can be useful for interpolating data. Not available for aggregates.
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception. Note that in this case the function always returns a DatapointsList even when a single id is requested.

Sequences

Retrieve a sequence by id

SequencesAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.sequences.Sequence]

Retrieve a single sequence by id.

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested sequences or None if it does not exist.

Return type:

Optional[Sequence]

Examples

Get sequences by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.retrieve(id=1)

Get sequences by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.retrieve(external_id="1")

Retrieve multiple sequences by id

SequencesAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None) → cognite.client.data_classes.sequences.SequenceList

Retrieve multiple sequences by id.

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
Returns:

The requested sequences.

Return type:

SequenceList

Examples

Get sequences by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.retrieve_multiple(ids=[1, 2, 3])

Get sequences by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.retrieve_multiple(external_ids=["abc", "def"])

List sequences

SequencesAPI.list(name: str = None, external_id_prefix: str = None, metadata: Dict[str, str] = None, asset_ids: List[int] = None, root_asset_ids: List[int] = None, asset_subtree_ids: List[int] = None, asset_subtree_external_ids: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, limit: Optional[int] = 25) → cognite.client.data_classes.sequences.SequenceList

Iterate over sequences

Fetches sequences as they are iterated over, so you keep a limited number of objects in memory.

Parameters:
  • name (str) – Filter out sequences that do not have this exact name.
  • external_id_prefix (str) – Filter out sequences that do not have this string as the start of the externalId
  • metadata (Dict[str, Any]) – Filter out sequences that do not match these metadata fields and values (case-sensitive). Format is {“key1”:”value1”,”key2”:”value2”}.
  • asset_ids (List[int]) – Filter out sequences that are not linked to any of these assets.
  • root_asset_ids (List[int]) – Filter out sequences not linked to assets with one of these assets as the root asset.
  • asset_subtree_ids (List[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (List[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (List[int]) – Return only events in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only events in the specified data sets with these external ids.
  • created_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • last_updated_time (Union[Dict[str, int], TimestampRange]) – Range between two timestamps. Possible keys are min and max, with values given as time stamps in ms.
  • limit (int, optional) – Max number of sequences to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The requested sequences.

Return type:

SequenceList

Examples

List sequences:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.list(limit=5)

Iterate over sequences:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for seq in c.sequences:
...     seq # do something with the sequences

Iterate over chunks of sequences to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for seq_list in c.sequences(chunk_size=2500):
...     seq_list # do something with the sequences

Aggregate sequences

SequencesAPI.aggregate(filter: Union[cognite.client.data_classes.sequences.SequenceFilter, Dict[KT, VT]] = None) → List[cognite.client.data_classes.sequences.SequenceAggregate]

Aggregate sequences

Parameters:filter (Union[SequenceFilter, Dict]) – Filter on sequence filter with exact match
Returns:List of sequence aggregates
Return type:List[SequenceAggregate]

Examples

Aggregate sequences:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.aggregate(filter={"external_id_prefix": "prefix"})

Search for sequences

SequencesAPI.search(name: str = None, description: str = None, query: str = None, filter: Union[cognite.client.data_classes.sequences.SequenceFilter, Dict[KT, VT]] = None, limit: int = 100) → cognite.client.data_classes.sequences.SequenceList

Search for sequences. Primarily meant for human-centric use-cases and data exploration, not for programs, since matching and ordering may change over time. Use the list function if stable or exact matches are required.

Parameters:
  • name (str, optional) – Prefix and fuzzy search on name.
  • description (str, optional) – Prefix and fuzzy search on description.
  • query (str, optional) – Search on name and description using wildcard search on each of the words (separated by spaces). Retrieves results where at least one word must match. Example: ‘some other’
  • filter (Union[SequenceFilter, Dict], optional) – Filter to apply. Performs exact match on these fields.
  • limit (int, optional) – Max number of results to return.
Returns:

List of requested sequences.

Return type:

SequenceList

Examples

Search for a sequence:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.search(name="some name")

Create a sequence

SequencesAPI.create(sequence: Union[cognite.client.data_classes.sequences.Sequence, List[cognite.client.data_classes.sequences.Sequence]]) → Union[cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.sequences.SequenceList]

Create one or more sequences.

Parameters:sequence (Union[Sequence, List[Sequence]]) – Sequence or list of Sequence to create. The Sequence columns parameter is a list of objects with fields externalId (external id of the column, when omitted, they will be given ids of ‘column0, column1, …’), valueType (data type of the column, either STRING, LONG, or DOUBLE, with default DOUBLE), name, description, metadata (optional fields to describe and store information about the data in the column). Other fields will be removed automatically, so a columns definition from a different sequence object can be passed here.
Returns:The created sequences.
Return type:Union[Sequence, SequenceList]

Examples

Create a new sequence:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Sequence
>>> c = CogniteClient()
>>> column_def = [{"valueType":"STRING","externalId":"user","description":"some description"}, {"valueType":"DOUBLE","externalId":"amount"}]
>>> seq = c.sequences.create(Sequence(external_id="my_sequence", columns=column_def))

Create a new sequence with the same column specifications as an existing sequence:

>>> seq2 = c.sequences.create(Sequence(external_id="my_copied_sequence", columns=seq.columns))

Delete sequences

SequencesAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None) → None

Delete one or more sequences.

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of external ids
Returns:

None

Examples

Delete sequences by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.sequences.delete(id=[1,2,3], external_id="3")

Update sequences

SequencesAPI.update(item: Union[cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.sequences.SequenceUpdate, List[Union[cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.sequences.SequenceUpdate]]]) → Union[cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.sequences.SequenceList]

Update one or more sequences.

Parameters:item (Union[Sequence, SequenceUpdate, List[Union[Sequence, SequenceUpdate]]]) – Sequences to update
Returns:Updated sequences.
Return type:Union[Sequence, SequenceList]

Examples

Update a sequence that you have fetched. This will perform a full update of the sequences:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.retrieve(id=1)
>>> res.description = "New description"
>>> res = c.sequences.update(res)

Perform a partial update on a sequence, updating the description and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate
>>> c = CogniteClient()
>>> my_update = SequenceUpdate(id=1).description.set("New description").metadata.add({"key": "value"})
>>> res = c.sequences.update(my_update)

Retrieve data

SequencesDataAPI.retrieve(start: int, end: Optional[int], column_external_ids: Optional[List[str]] = None, external_id: Union[str, List[str]] = None, id: Union[int, List[int]] = None, limit: int = None) → Union[cognite.client.data_classes.sequences.SequenceData, cognite.client.data_classes.sequences.SequenceDataList]

Retrieve data from a sequence

Parameters:
  • start (int) – Row number to start from (inclusive).
  • end (Union[int, None]) – Upper limit on the row number (exclusive). Set to None or -1 to get all rows until end of sequence.
  • column_external_ids (Optional[List[str]]) – List of external id for the columns of the sequence. If ‘None’ is passed, all columns will be retrieved.
  • id (int) – Id of sequence.
  • external_id (str) – External id of sequence.
  • limit (int) – Maximum number of rows to return per sequence.
Returns:

List of sequence data

Examples

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.data.retrieve(id=0, start=0, end=None)
>>> tuples = [(r,v) for r,v in res.items()] # You can use this iterator in for loops and list comprehensions,
>>> single_value = res[23] # ... get the values at a single row number,
>>> col = res.get_column(external_id='columnExtId') # ... get the array of values for a specific column,
>>> df = res.to_pandas() # ... or convert the result to a dataframe

Retrieve pandas dataframe

SequencesDataAPI.retrieve_dataframe(start: int, end: Optional[int], column_external_ids: Optional[List[str]] = None, external_id: str = None, column_names: str = None, id: int = None, limit: int = None)

Retrieve data from a sequence as a pandas dataframe

Parameters:
  • start (int) – (inclusive) row number to start from.
  • end (Union[int, None]) – (exclusive) upper limit on the row number. Set to None or -1 to get all rows until end of sequence.
  • column_external_ids (Optional[List[str]]) – List of external id for the columns of the sequence. If ‘None’ is passed, all columns will be retrieved.
  • id (int) – Id of sequence
  • external_id (str) – External id of sequence.
  • column_names (str) – Which field(s) to use as column header. Can use “externalId”, “id”, “columnExternalId”, “id|columnExternalId” or “externalId|columnExternalId”. Default is “externalId|columnExternalId” for queries on more than one sequence, and “columnExternalId” for queries on a single sequence.
  • limit (int) – Maximum number of rows to return per sequence.
Returns:

pandas.DataFrame

Examples

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> df = c.sequences.data.retrieve_dataframe(id=0, start=0, end=None)

Insert rows into a sequence

SequencesDataAPI.insert(rows: Union[Dict[int, List[Union[int, str, float]]], List[Tuple[int, Union[int, float, str]]], List[Dict[str, Any]], cognite.client.data_classes.sequences.SequenceData], column_external_ids: Optional[List[str]], id: int = None, external_id: str = None) → None

Insert rows into a sequence

Parameters:
  • column_external_ids (Optional[List[str]]) – List of external id for the columns of the sequence.
  • rows (Union[ Dict[int, List[Union[int, float, str]]], List[Tuple[int,Union[int, float, str]]], List[Dict[str,Any]], SequenceData]) – The rows you wish to insert. Can either be a list of tuples, a list of {“rowNumber”:… ,”values”: …} objects, a dictionary of rowNumber: data, or a SequenceData object. See examples below.
  • id (int) – Id of sequence to insert rows into.
  • external_id (str) – External id of sequence to insert rows into.
Returns:

None

Examples

Your rows of data can be a list of tuples where the first element is the rownumber and the second element is the data to be inserted:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> seq = c.sequences.create(Sequence(columns=[{"valueType": "STRING", "externalId":"col_a"},{"valueType": "DOUBLE", "externalId":"col_b"}]))
>>> data = [(1, ['pi',3.14]), (2, ['e',2.72]) ]
>>> c.sequences.data.insert(column_external_ids=["col_a","col_b"], rows=data, id=1)

They can also be provided as a list of API-style objects with a rowNumber and values field:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data = [{"rowNumber": 123, "values": ['str',3]}, {"rowNumber": 456, "values": ["bar",42]} ]
>>> c.sequences.data.insert(data, id=1, column_external_ids=["col_a","col_b"]) # implicit columns are retrieved from metadata

Or they can be a given as a dictionary with row number as the key, and the value is the data to be inserted at that row:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data = {123 : ['str',3], 456 : ['bar',42] }
>>> c.sequences.data.insert(column_external_ids=['stringColumn','intColumn'], rows=data, id=1)

Finally, they can be a SequenceData object retrieved from another request. In this case column_external_ids from this object are used as well.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> data = c.sequences.data.retrieve(id=2,start=0,end=10)
>>> c.sequences.data.insert(rows=data, id=1,column_external_ids=None)

Insert a pandas dataframe into a sequence

SequencesDataAPI.insert_dataframe(dataframe, external_id_headers: bool = True, id: int = None, external_id: str = None) → None

Insert a Pandas dataframe.

The index of the dataframe must contain the row numbers. The names of the remaining columns specify the column external ids. The sequence and columns must already exist.

Parameters:
  • dataframe (pandas.DataFrame) – Pandas DataFrame object containing the sequence data.
  • external_id_headers (bool) – Ignored parameter here for backwards compatibility. Dataframe columns should always match sequence column external ids.
  • id (int) – Id of sequence to insert rows into.
  • external_id (str) – External id of sequence to insert rows into.
Returns:

None

Examples

Multiply data in the sequence by 2:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> df = c.sequences.data.retrieve_dataframe(id=123, start=0, end=None)
>>> c.sequences.data.insert_dataframe(df*2, id=123)

Delete rows from a sequence

SequencesDataAPI.delete(rows: List[int], id: int = None, external_id: str = None) → None

Delete rows from a sequence

Parameters:
  • rows (List[int]) – List of row numbers.
  • id (int) – Id of sequence to delete rows from.
  • external_id (str) – External id of sequence to delete rows from.
Returns:

None

Examples

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.sequences.data.delete(id=0, rows=[1,2,42])

Delete a range of rows from a sequence

SequencesDataAPI.delete_range(start: int, end: Optional[int], id: int = None, external_id: str = None) → None

Delete a range of rows from a sequence. Note this operation is potentially slow, as retrieves each row before deleting.

Parameters:
  • start (int) – Row number to start from (inclusive).
  • end (Union[int, None]) – Upper limit on the row number (exclusive). Set to None or -1 to delete all rows until end of sequence.
  • id (int) – Id of sequence to delete rows from.
  • external_id (str) – External id of sequence to delete rows from.
Returns:

None

Examples

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.sequences.data.delete_range(id=0, start=0, end=None)

Data classes

class cognite.client.data_classes.sequences.Sequence(id: int = None, name: str = None, description: str = None, asset_id: int = None, external_id: str = None, metadata: Dict[str, Any] = None, columns: List[Dict[str, Any]] = None, created_time: int = None, last_updated_time: int = None, data_set_id: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Information about the sequence stored in the database

Parameters:
  • id (int) – Unique cognite-provided identifier for the sequence
  • name (str) – Name of the sequence
  • description (str) – Description of the sequence
  • asset_id (int) – Optional asset this sequence is associated with
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • metadata (Dict[str, Any]) – Custom, application specific metadata. String key -> String value. Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • columns (List[Dict[str, Any]]) – List of column definitions
  • created_time (int) – Time when this sequence was created in CDF in milliseconds since Jan 1, 1970.
  • last_updated_time (int) – The last time this sequence was updated in CDF, in milliseconds since Jan 1, 1970.
  • data_set_id (int) – Data set that this sequence belongs to
  • cognite_client (CogniteClient) – The client to associate with this object.
column_external_ids

Retrieves list of column external ids for the sequence, for use in e.g. data retrieve or insert methods

Returns:List of sequence column external ids
column_value_types

Retrieves list of column value types

Returns:List of column value types
rows(start: int, end: int) → List[dict]

Retrieves rows from this sequence.

Returns:List of sequence data.
class cognite.client.data_classes.sequences.SequenceAggregate(count: int = None, **kwargs)

Bases: dict

No description.

Parameters:count (int) – No description.
class cognite.client.data_classes.sequences.SequenceData(id: int = None, external_id: str = None, rows: List[dict] = None, row_numbers: List[int] = None, values: List[List[Union[int, str, float]]] = None, columns: List[dict] = None)

Bases: object

An object representing a list of rows from a sequence.

Parameters:
  • id (int) – Id of the sequence the data belong to
  • external_id (str) – External id of the sequence the data belong to
  • rows (List[dict]) – Combined row numbers and row data object from the API. If you pass this, row_numbers/values are ignored.
  • row_numbers (List[int]) – The data row numbers.
  • values (List[List[ Union[int, str, float]]]) – The data values, one row at a time.
  • columns – List[dict]: The column information, in the format returned by the API.
column_external_ids

Retrieves list of column external ids for the sequence, for use in e.g. data retrieve or insert methods.

Returns:List of sequence column external ids.
column_value_types

Retrieves list of column value types.

Returns:List of column value types
dump(camel_case: bool = False) → Dict[str, Any]

Dump the sequence data into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A list of dicts representing the instance.
Return type:List[Dict[str, Any]]
get_column(external_id: str) → List[Union[int, str, float]]

Get a column by external_id.

Parameters:external_id (str) – External id of the column.
Returns:A list of values for that column in the sequence
Return type:List[Union[int, str, float]]
items() → Generator[Tuple[int, List[Union[int, str, float]]], None, None]

Returns an iterator over tuples of (row number, values).

to_pandas(column_names: str = 'columnExternalId') → pandas.DataFrame

Convert the sequence data into a pandas DataFrame.

Parameters:column_names (str) – Which field(s) to use as column header. Can use “externalId”, “id”, “columnExternalId”, “id|columnExternalId” or “externalId|columnExternalId”.
Returns:The dataframe.
Return type:pandas.DataFrame
class cognite.client.data_classes.sequences.SequenceDataList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

to_pandas(column_names: str = 'externalId|columnExternalId') → pandas.DataFrame

Convert the sequence data list into a pandas DataFrame. Each column will be a sequence.

Parameters:
  • column_names (str) – Which field to use as column header. Can use any combination of “externalId”, “columnExternalId”, “id” and other characters as a template.
  • include_aggregate_name (bool) – Include aggregate in the column name
Returns:

The sequence data list as a pandas DataFrame.

Return type:

pandas.DataFrame

class cognite.client.data_classes.sequences.SequenceFilter(name: str = None, external_id_prefix: str = None, metadata: Dict[str, Any] = None, asset_ids: List[int] = None, root_asset_ids: List[int] = None, asset_subtree_ids: List[Dict[str, Any]] = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, last_updated_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, data_set_ids: List[Dict[str, Any]] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

No description.

Parameters:
  • name (str) – Return only sequences with this exact name.
  • external_id_prefix (str) – Filter by this (case-sensitive) prefix for the external ID.
  • metadata (Dict[str, Any]) – Filter the sequences by metadata fields and values (case-sensitive). Format is {“key1”:”value1”,”key2”:”value2”}.
  • asset_ids (List[int]) – Return only sequences linked to one of the specified assets.
  • root_asset_ids (List[int]) – Only include sequences that have a related asset in a tree rooted at any of these root assetIds.
  • asset_subtree_ids (List[Dict[str, Any]]) – Only include sequences that have a related asset in a subtree rooted at any of these assetIds (including the roots given). If the total size of the given subtrees exceeds 100,000 assets, an error will be returned.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • last_updated_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • data_set_ids (List[Dict[str, Any]]) – Only include sequences that belong to these datasets.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.sequences.SequenceList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.sequences.SequenceUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

No description.

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.

Raw

Databases

List databases

RawDatabasesAPI.list(limit: int = 25) → cognite.client.data_classes.raw.DatabaseList

List databases

Parameters:limit (int, optional) – Maximum number of databases to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:List of requested databases.
Return type:DatabaseList

Examples

List the first 5 databases:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> db_list = c.raw.databases.list(limit=5)

Iterate over databases:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for db in c.raw.databases:
...     db # do something with the db

Iterate over chunks of databases to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for db_list in c.raw.databases(chunk_size=2500):
...     db_list # do something with the dbs

Create new databases

RawDatabasesAPI.create(name: Union[str, List[str]]) → Union[cognite.client.data_classes.raw.Database, cognite.client.data_classes.raw.DatabaseList]

Create one or more databases.

Parameters:name (Union[str, List[str]]) – A db name or list of db names to create.
Returns:Database or list of databases that has been created.
Return type:Union[Database, DatabaseList]

Examples

Create a new database:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.raw.databases.create("db1")

Delete databases

RawDatabasesAPI.delete(name: Union[str, List[str]], recursive: bool = False) → None

Delete one or more databases.

Parameters:
  • name (Union[str, List[str]]) – A db name or list of db names to delete.
  • recursive (bool) – Recursively delete all tables in the database(s).
Returns:

None

Examples

Delete a list of databases:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.raw.databases.delete(["db1", "db2"])

Tables

List tables in a database

RawTablesAPI.list(db_name: str, limit: int = 25) → cognite.client.data_classes.raw.TableList

List tables

Parameters:
  • db_name (str) – The database to list tables from.
  • limit (int, optional) – Maximum number of tables to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested tables.

Return type:

TableList

Examples

List the first 5 tables:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> table_list = c.raw.tables.list("db1", limit=5)

Iterate over tables:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for table in c.raw.tables(db_name="db1"):
...     table # do something with the table

Iterate over chunks of tables to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for table_list in c.raw.tables(db_name="db1", chunk_size=2500):
...     table_list # do something with the tables

Create new tables in a database

RawTablesAPI.create(db_name: str, name: Union[str, List[str]]) → Union[cognite.client.data_classes.raw.Table, cognite.client.data_classes.raw.TableList]

Create one or more tables.

Parameters:
  • db_name (str) – Database to create the tables in.
  • name (Union[str, List[str]]) – A table name or list of table names to create.
Returns:

Table or list of tables that has been created.

Return type:

Union[Table, TableList]

Examples

Create a new table in a database:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.raw.tables.create("db1", "table1")

Delete tables from a database

RawTablesAPI.delete(db_name: str, name: Union[str, List[str]]) → None

Delete one or more tables.

Parameters:
  • db_name (str) – Database to delete tables from.
  • name (Union[str, List[str]]) – A table name or list of table names to delete.
Returns:

None

Examples

Delete a list of tables:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.raw.tables.delete("db1", ["table1", "table2"])

Rows

Get a row from a table

RawRowsAPI.retrieve(db_name: str, table_name: str, key: str) → Optional[cognite.client.data_classes.raw.Row]

Retrieve a single row by key.

Parameters:
  • db_name (str) – Name of the database.
  • table_name (str) – Name of the table.
  • key (str) – The key of the row to retrieve.
Returns:

The requested row.

Return type:

Optional[Row]

Examples

Retrieve a row with key ‘k1’ from tablew ‘t1’ in database ‘db1’:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> row = c.raw.rows.retrieve("db1", "t1", "k1")

List rows in a table

RawRowsAPI.list(db_name: str, table_name: str, min_last_updated_time: int = None, max_last_updated_time: int = None, columns: List[str] = None, limit: int = 25) → cognite.client.data_classes.raw.RowList

List rows in a table.

Parameters:
  • db_name (str) – Name of the database.
  • table_name (str) – Name of the table.
  • min_last_updated_time (int) – Rows must have been last updated after this time. ms since epoch.
  • max_last_updated_time (int) – Rows must have been last updated before this time. ms since epoch.
  • columns (List[str]) – List of column keys. Set to None for retrieving all, use [] to retrieve only row keys.
  • limit (int) – The number of rows to retrieve. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The requested rows.

Return type:

RowList

Examples

List rows:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> row_list = c.raw.rows.list("db1", "t1", limit=5)

Iterate over rows:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for row in c.raw.rows(db_name="db1", table_name="t1", columns=["col1","col2"]):
...     row # do something with the row

Iterate over chunks of rows to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for row_list in c.raw.rows(db_name="db1", table_name="t1", chunk_size=2500):
...     row_list # do something with the rows

Insert rows into a table

RawRowsAPI.insert(db_name: str, table_name: str, row: Union[List[cognite.client.data_classes.raw.Row], cognite.client.data_classes.raw.Row, Dict[KT, VT]], ensure_parent: bool = False) → None

Insert one or more rows into a table.

Parameters:
  • db_name (str) – Name of the database.
  • table_name (str) – Name of the table.
  • row (Union[List[Row], Row, Dict]) – The row(s) to insert
  • ensure_parent (bool) – Create database/table if they don’t already exist.
Returns:

None

Examples

Insert new rows into a table:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> rows = {"r1": {"col1": "val1", "col2": "val1"}, "r2": {"col1": "val2", "col2": "val2"}}
>>> res = c.raw.rows.insert("db1", "table1", rows)

Delete rows from a table

RawRowsAPI.delete(db_name: str, table_name: str, key: Union[str, List[str]]) → None

Delete rows from a table.

Parameters:
  • db_name (str) – Name of the database.
  • table_name (str) – Name of the table.
  • key (Union[str, List[str]]) – The key(s) of the row(s) to delete.
Returns:

None

Examples

Delete rows from table:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> keys_to_delete = ["k1", "k2", "k3"]
>>> c.raw.rows.delete("db1", "table1", keys_to_delete)

Data classes

class cognite.client.data_classes.raw.Database(name: str = None, created_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A NoSQL database to store customer data.

Parameters:
  • name (str) – Unique name of a database.
  • created_time (int) – Time the database was created.
  • cognite_client (CogniteClient) – The client to associate with this object.
tables(limit: int = None) → cognite.client.data_classes.raw.TableList

Get the tables in this database.

Parameters:limit (int) – The number of tables to return.
Returns:List of tables in this database.
Return type:TableList
class cognite.client.data_classes.raw.DatabaseList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.raw.Row(key: str = None, columns: Dict[str, Any] = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • key (str) – Unique row key
  • columns (Dict[str, Any]) – Row data stored as a JSON object.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • cognite_client (CogniteClient) – The client to associate with this object.
to_pandas()

Convert the instance into a pandas DataFrame.

Returns:The pandas DataFrame representing this instance.
Return type:pandas.DataFrame
class cognite.client.data_classes.raw.RowList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

to_pandas()

Convert the instance into a pandas DataFrame.

Returns:The pandas DataFrame representing this instance.
Return type:pandas.DataFrame
class cognite.client.data_classes.raw.Table(name: str = None, created_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A NoSQL database table to store customer data

Parameters:
  • name (str) – Unique name of the table
  • created_time (int) – Time the table was created.
  • cognite_client (CogniteClient) – The client to associate with this object.
rows(key: str = None, limit: int = None) → Union[cognite.client.data_classes.raw.Row, cognite.client.data_classes.raw.RowList]

Get the rows in this table.

Parameters:
  • key (str) – Specify a key to return only that row.
  • limit (int) – The number of rows to return.
Returns:

List of tables in this database.

Return type:

Union[Row, RowList]

class cognite.client.data_classes.raw.TableList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

Relationships

Retrieve a relationship by id

RelationshipsAPI.retrieve(external_id: str, fetch_resources: bool = False) → Optional[cognite.client.data_classes.relationships.Relationship]

Retrieve a single relationship by external id.

Parameters:
  • external_id (str) – External ID
  • fetch_resources (bool) – if true, will try to return the full resources referenced by the relationship in the source and target fields.
Returns:

Requested relationship or None if it does not exist.

Return type:

Optional[Relationship]

Examples

Get relationship by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.relationships.retrieve(external_id="1")

Retrieve multiple relationships by id

RelationshipsAPI.retrieve_multiple(external_ids: List[str], fetch_resources: bool = False) → cognite.client.data_classes.relationships.RelationshipList

Retrieve multiple relationships by external id.

Parameters:
  • external_ids (List[str]) – External IDs
  • fetch_resources (bool) – if true, will try to return the full resources referenced by the relationship in the source and target fields.
Returns:

The requested relationships.

Return type:

RelationshipList

Examples

Get relationships by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.relationships.retrieve_multiple(external_ids=["abc", "def"])

List relationships

RelationshipsAPI.list(source_external_ids: List[str] = None, source_types: List[str] = None, target_external_ids: List[str] = None, target_types: List[str] = None, data_set_ids: List[int] = None, data_set_external_ids: List[str] = None, start_time: Dict[str, int] = None, end_time: Dict[str, int] = None, confidence: Dict[str, int] = None, last_updated_time: Dict[str, int] = None, created_time: Dict[str, int] = None, active_at_time: Dict[str, int] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, limit: int = 100, partitions: int = None, fetch_resources: bool = False) → cognite.client.data_classes.relationships.RelationshipList

Lists relationships stored in the project based on a query filter given in the payload of this request. Up to 1000 relationships can be retrieved in one operation.

Parameters:
  • source_external_ids (List[str]) – Include relationships that have any of these values in their source External Id field
  • source_types (List[str]) – Include relationships that have any of these values in their source Type field
  • target_external_ids (List[str]) – Include relationships that have any of these values in their target External Id field
  • target_types (List[str]) – Include relationships that have any of these values in their target Type field
  • data_set_ids (List[int]) – Return only relationships in the specified data sets with these ids.
  • data_set_external_ids (List[str]) – Return only relationships in the specified data sets with these external ids.
  • start_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milli seconds (inclusive)
  • end_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milli seconds (inclusive)
  • confidence (Dict[str, int]) – Range to filter the field for. (inclusive)
  • last_updated_time (Dict[str, Any]) – Range to filter the field for. (inclusive)
  • created_time (Dict[str, int]) – Range to filter the field for. (inclusive)
  • active_at_time (Dict[str, int]) – Limits results to those active at any point within the given time range, i.e. if there is any overlap in the intervals [activeAtTime.min, activeAtTime.max] and [startTime, endTime], where both intervals are inclusive. If a relationship does not have a startTime, it is regarded as active from the begining of time by this filter. If it does not have an endTime is will be regarded as active until the end of time. Similarly, if a min is not supplied to the filter, the min will be implicitly set to the beginning of time, and if a max is not supplied, the max will be implicitly set to the end of time.
  • labels (LabelFilter) – Return only the resource matching the specified label constraints.
  • limit (int) – Maximum number of relationships to return. Defaults to 100. Set to -1, float(“inf”) or None to return all items.
  • partitions (int) – Retrieve relationships in parallel using this number of workers. Also requires limit=None to be passed.
  • fetch_resources (bool) – if true, will try to return the full resources referenced by the relationship in the source and target fields.
Returns:

List of requested relationships

Return type:

RelationshipList

Examples

List relationships:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> relationship_list = c.relationships.list(limit=5)

Iterate over relationships:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for relationship in c.relationships:
...     relationship # do something with the relationship

Create a relationship

RelationshipsAPI.create(relationship: Union[cognite.client.data_classes.relationships.Relationship, List[cognite.client.data_classes.relationships.Relationship]]) → Union[cognite.client.data_classes.relationships.Relationship, cognite.client.data_classes.relationships.RelationshipList]

Create one or more relationships.

Parameters:relationship (Union[Relationship, List[Relationship]]) – Relationship or list of relationships to create. Note: the source_type and target_type field in the Relationship(s) can be any string among “Asset”, “TimeSeries”, “FileMetadata”, “Event”, “Sequence”
Returns:Created relationship(s)
Return type:Union[Relationship, RelationshipList]

Examples

Create a new relationship specifying object type and external id for source and target:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Relationship
>>> c = CogniteClient()
>>> flowrel1 = Relationship(external_id="flow_1", source_external_id="source_ext_id", source_type="asset", target_external_id="target_ext_id", target_type="event", confidence=0.1, data_set_id=1234)
>>> flowrel2 = Relationship(external_id="flow_2", source_external_id="source_ext_id", source_type="asset", target_external_id="target_ext_id", target_type="event", confidence=0.1, data_set_id=1234)
>>> res = c.relationships.create([flowrel1,flowrel2])

Update relationships

RelationshipsAPI.update(item: Union[cognite.client.data_classes.relationships.Relationship, cognite.client.data_classes.relationships.RelationshipUpdate, List[Union[cognite.client.data_classes.relationships.Relationship, cognite.client.data_classes.relationships.RelationshipUpdate]]]) → Union[cognite.client.data_classes.relationships.Relationship, cognite.client.data_classes.relationships.RelationshipList]

Update one or more relationships Currently, a full replacement of labels on a relationship is not supported (only partial add/remove updates). See the example below on how to perform partial labels update.

Parameters:item (Union[Relationship, RelationshipUpdate, List[Union[Relationship, RelationshipUpdate]]]) – Relationships(s) to update
Returns:Updated relationship(s)
Return type:Union[Relationship, RelationshipsList]

Examples

Update a data set that you have fetched. This will perform a full update of the data set:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> rel = c.relationships.retrieve(external_id="flow1")
>>> rel.confidence = 0.75
>>> res = c.relationships.update(rel)

Perform a partial update on a relationship, setting a source_external_id and a confidence:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import RelationshipUpdate
>>> c = CogniteClient()
>>> my_update = RelationshipUpdate(external_id="flow_1").source_external_id.set("alternate_source").confidence.set(0.97)
>>> res1 = c.relationships.update(my_update)
>>> # Remove an already set optional field like so
>>> another_update = RelationshipUpdate(external_id="flow_1").confidence.set(None)
>>> res2 = c.relationships.update(another_update)

Attach labels to a relationship:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import RelationshipUpdate
>>> c = CogniteClient()
>>> my_update = RelationshipUpdate(external_id="flow_1").labels.add(["PUMP", "VERIFIED"])
>>> res = c.relationships.update(my_update)

Detach a single label from a relationship:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import RelationshipUpdate
>>> c = CogniteClient()
>>> my_update = RelationshipUpdate(external_id="flow_1").labels.remove("PUMP")
>>> res = c.relationships.update(my_update)

Delete relationships

RelationshipsAPI.delete(external_id: Union[str, List[str]], ignore_unknown_ids: bool = False) → None

Delete one or more relationships.

Parameters:
  • external_id (Union[str, List[str]]) – External ID or list of external ids
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete relationships by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.relationships.delete(external_id=["a","b"])

Data classes

class cognite.client.data_classes.relationships.Relationship(external_id: str = None, source_external_id: str = None, source_type: str = None, source: Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.events.Event, Dict[KT, VT]] = None, target_external_id: str = None, target_type: str = None, target: Union[cognite.client.data_classes.assets.Asset, cognite.client.data_classes.time_series.TimeSeries, cognite.client.data_classes.files.FileMetadata, cognite.client.data_classes.sequences.Sequence, cognite.client.data_classes.events.Event, Dict[KT, VT]] = None, start_time: int = None, end_time: int = None, confidence: float = None, data_set_id: int = None, labels: List[Union[cognite.client.data_classes.labels.Label, str, cognite.client.data_classes.labels.LabelDefinition]] = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Representation of a relationship in CDF, consists of a source and a target and some additional parameters.

Parameters:
  • external_id (str) – External id of the relationship, must be unique within the project.
  • source_external_id (str) – External id of the CDF resource that constitutes the relationship source.
  • source_type (str) – The CDF resource type of the relationship source. Must be one of the specified values.
  • source (Union[Asset, TimeSeries, FileMetadata, Event, Sequence, Dict]) – The full resource referenced by the source_external_id and source_type fields.
  • target_external_id (str) – External id of the CDF resource that constitutes the relationship target.
  • target_type (str) – The CDF resource type of the relationship target. Must be one of the specified values.
  • target (Union[Asset, TimeSeries, FileMetadata, Event, Sequence, Dict]) – The full resource referenced by the target_external_id and target_type fields.
  • start_time (int) – Time, in milliseconds since Jan. 1, 1970, when the relationship became active. If there is no startTime, relationship is active from the beginning of time until endTime.
  • end_time (int) – Time, in milliseconds since Jan. 1, 1970, when the relationship became inactive. If there is no endTime, relationship is active from startTime until the present or any point in the future. If endTime and startTime are set, then endTime must be strictly greater than startTime.
  • confidence (float) – Confidence value of the existence of this relationship. Generated relationships should provide a realistic score on the likelihood of the existence of the relationship. Relationships without a confidence value can be interpreted at the discretion of each project.
  • data_set_id (int) – The id of the dataset this relationship belongs to.
  • labels (List[Label]) – A list of the labels associated with this resource item.
  • created_time (int) – Time, in milliseconds since Jan. 1, 1970, when this relationship was created in CDF.
  • last_updated_time (int) – Time, in milliseconds since Jan. 1, 1970, when this relationship was last updated in CDF.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.relationships.RelationshipFilter(source_external_ids: List[str] = None, source_types: List[str] = None, target_external_ids: List[str] = None, target_types: List[str] = None, data_set_ids: List[Dict[str, Any]] = None, start_time: Dict[str, int] = None, end_time: Dict[str, int] = None, confidence: Dict[str, int] = None, last_updated_time: Dict[str, int] = None, created_time: Dict[str, int] = None, active_at_time: Dict[str, int] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on relationships with exact match. Multiple filter elments in one property, e.g. sourceExternalIds: [ “a”, “b” ], will return all relationships where the sourceExternalId field is either a or b. Filters in multiple properties will return the relationships that match all criteria. If the filter is not specified it default to an empty filter.

Parameters:
  • source_external_ids (List[str]) – Include relationships that have any of these values in their sourceExternalId field
  • source_types (List[str]) – Include relationships that have any of these values in their sourceType field
  • target_external_ids (List[str]) – Include relationships that have any of these values in their targetExternalId field
  • target_types (List[str]) – Include relationships that have any of these values in their targetType field
  • data_set_ids (List[Dict[str, Any]]) – Either one of internalId (int) or externalId (str)
  • start_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milli seconds (inclusive)
  • end_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milli seconds (inclusive)
  • confidence (Dict[str, int]) – Range to filter the field for. (inclusive)
  • last_updated_time (Dict[str, Any]) – Range to filter the field for. (inclusive)
  • created_time (Dict[str, int]) – Range to filter the field for. (inclusive)
  • active_at_time (Dict[str, int]) – Limits results to those active at any point within the given time range, i.e. if there is any overlap in the intervals [activeAtTime.min, activeAtTime.max] and [startTime, endTime], where both intervals are inclusive. If a relationship does not have a startTime, it is regarded as active from the begining of time by this filter. If it does not have an endTime is will be regarded as active until the end of time. Similarly, if a min is not supplied to the filter, the min will be implicitly set to the beginning of time, and if a max is not supplied, the max will be implicitly set to the end of time.
  • labels (LabelFilter) – Return only the resource matching the specified label constraints.
  • cognite_client (CogniteClient) – The client to associate with this object.
dump(camel_case: bool = False)

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
class cognite.client.data_classes.relationships.RelationshipList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.relationships.RelationshipUpdate(external_id: str)

Bases: cognite.client.data_classes._base.CogniteUpdate

Update applied to a single relationship

Parameters:external_id (str) – The external ID provided by the client. Must be unique for the resource type.

3D

Models

Retrieve a model by ID

ThreeDModelsAPI.retrieve(id: int) → cognite.client.data_classes.three_d.ThreeDModel

Retrieve a 3d model by id

Parameters:id (int) – Get the model with this id.
Returns:The requested 3d model.
Return type:ThreeDModel

Example

Get 3d model by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.models.retrieve(id=1)

List models

ThreeDModelsAPI.list(published: bool = None, limit: int = 25) → cognite.client.data_classes.three_d.ThreeDModelList

List 3d models.

Parameters:
  • published (bool) – Filter based on whether or not the model has published revisions.
  • limit (int) – Maximum number of models to retrieve. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The list of 3d models.

Return type:

ThreeDModelList

Examples

List 3d models:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> three_d_model_list = c.three_d.models.list()

Iterate over 3d models:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for three_d_model in c.three_d.models:
...     three_d_model # do something with the 3d model

Iterate over chunks of 3d models to reduce memory load:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for three_d_model in c.three_d.models(chunk_size=50):
...     three_d_model # do something with the 3d model

Create models

ThreeDModelsAPI.create(name: Union[str, List[str]]) → Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelList]

Create new 3d models.

Parameters:name (Union[str, List[str]) – The name of the 3d model(s) to create.
Returns:The created 3d model(s).
Return type:Union[ThreeDModel, ThreeDModelList]

Example

Create new 3d models:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.models.create(name="My Model")

Update models

ThreeDModelsAPI.update(item: Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelUpdate, List[Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelList]]]) → Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelList]

Update 3d models.

Parameters:item (Union[ThreeDModel, ThreeDModelUpdate, List[Union[ThreeDModel, ThreeDModelUpdate]]]) – ThreeDModel(s) to update
Returns:Updated ThreeDModel(s)
Return type:Union[ThreeDModel, ThreeDModelList]

Examples

Update 3d model that you have fetched. This will perform a full update of the model:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> three_d_model = c.three_d.models.retrieve(id=1)
>>> three_d_model.name = "New Name"
>>> res = c.three_d.models.update(three_d_model)

Perform a partial update on a 3d model:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ThreeDModelUpdate
>>> c = CogniteClient()
>>> my_update = ThreeDModelUpdate(id=1).name.set("New Name")
>>> res = c.three_d.models.update(my_update)

Delete models

ThreeDModelsAPI.delete(id: Union[int, List[int]]) → None

Delete 3d models.

Parameters:id (Union[int, List[int]]) – ID or list of IDs to delete.
Returns:None

Example

Delete 3d model by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.models.delete(id=1)

Revisions

Retrieve a revision by ID

ThreeDRevisionsAPI.retrieve(model_id: int, id: int) → cognite.client.data_classes.three_d.ThreeDModelRevision

Retrieve a 3d model revision by id

Parameters:
  • model_id (int) – Get the revision under the model with this id.
  • id (int) – Get the model revision with this id.
Returns:

The requested 3d model revision.

Return type:

ThreeDModelRevision

Example

Retrieve 3d model revision by model id and revision id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.retrieve(model_id=1, id=1)

Create a revision

ThreeDRevisionsAPI.create(model_id: int, revision: Union[cognite.client.data_classes.three_d.ThreeDModelRevision, List[cognite.client.data_classes.three_d.ThreeDModelRevision]]) → Union[cognite.client.data_classes.three_d.ThreeDModelRevision, cognite.client.data_classes.three_d.ThreeDModelRevisionList]

Create a revisions for a specified 3d model.

Parameters:
Returns:

The created revision(s)

Return type:

Union[ThreeDModelRevision, ThreeDModelRevisionList]

Example

Create 3d model revision:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ThreeDModelRevision
>>> c = CogniteClient()
>>> my_revision = ThreeDModelRevision(file_id=1)
>>> res = c.three_d.revisions.create(model_id=1, revision=my_revision)

List revisions

ThreeDRevisionsAPI.list(model_id: int, published: bool = False, limit: int = 25) → cognite.client.data_classes.three_d.ThreeDModelRevisionList

List 3d model revisions.

Parameters:
  • model_id (int) – List revisions under the model with this id.
  • published (bool) – Filter based on whether or not the revision is published.
  • limit (int) – Maximum number of models to retrieve. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The list of 3d model revisions.

Return type:

ThreeDModelRevisionList

Example

List 3d model revisions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.list(model_id=1, published=True, limit=100)

Update revisions

ThreeDRevisionsAPI.update(model_id: int, item: Union[cognite.client.data_classes.three_d.ThreeDModelRevision, cognite.client.data_classes.three_d.ThreeDModelRevisionUpdate, List[Union[cognite.client.data_classes.three_d.ThreeDModelRevision, cognite.client.data_classes.three_d.ThreeDModelRevisionList]]]) → Union[cognite.client.data_classes.three_d.ThreeDModelRevision, cognite.client.data_classes.three_d.ThreeDModelRevisionList]

Update 3d model revisions.

Parameters:
Returns:

Updated ThreeDModelRevision(s)

Return type:

Union[ThreeDModelRevision, ThreeDModelRevisionList]

Examples

Update a revision that you have fetched. This will perform a full update of the revision:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> revision = c.three_d.revisions.retrieve(model_id=1, id=1)
>>> revision.status = "New Status"
>>> res = c.three_d.revisions.update(model_id=1, item=revision)

Perform a partial update on a revision, updating the published property and adding a new field to metadata:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ThreeDModelRevisionUpdate
>>> c = CogniteClient()
>>> my_update = ThreeDModelRevisionUpdate(id=1).published.set(False).metadata.add({"key": "value"})
>>> res = c.three_d.revisions.update(model_id=1, item=my_update)

Delete revisions

ThreeDRevisionsAPI.delete(model_id: int, id: Union[int, List[int]]) → None

Delete 3d model revisions.

Parameters:
  • model_id (int) – Delete the revision under the model with this id.
  • id (Union[int, List[int]]) – ID or list of IDs to delete.
Returns:

None

Example

Delete 3d model revision by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.delete(model_id=1, id=1)

Update a revision thumbnail

ThreeDRevisionsAPI.update_thumbnail(model_id: int, revision_id: int, file_id: int) → None

Update a revision thumbnail.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • file_id (int) – Id of the thumbnail file in the Files API.
Returns:

None

Example

Update revision thumbnail:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.update_thumbnail(model_id=1, revision_id=1, file_id=1)

List nodes

ThreeDRevisionsAPI.list_nodes(model_id: int, revision_id: int, node_id: int = None, depth: int = None, sort_by_node_id: bool = False, partitions: int = None, limit: int = 25) → cognite.client.data_classes.three_d.ThreeDNodeList

Retrieves a list of nodes from the hierarchy in the 3D Model.

You can also request a specific subtree with the ‘nodeId’ query parameter and limit the depth of the resulting subtree with the ‘depth’ query parameter.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • node_id (int) – ID of the root node of the subtree you request (default is the root node).
  • depth (int) – Get sub nodes up to this many levels below the specified node. Depth 0 is the root node.
  • limit (int) – Maximun number of nodes to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
  • sort_by_node_id (bool) – Returns the nodes in nodeId order.
  • partitions (int) – The result is retrieved in this many parts in parallel. Requires sort_by_node_id to be set to true.
Returns:

The list of 3d nodes.

Return type:

ThreeDNodeList

Example

List nodes from the hierarchy in the 3d model:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.list_nodes(model_id=1, revision_id=1, limit=10)

Filter nodes

ThreeDRevisionsAPI.filter_nodes(model_id: int, revision_id: int, properties: Dict[str, Dict[str, List[str]]] = None, limit: int = 25, partitions: int = None) → cognite.client.data_classes.three_d.ThreeDNodeList

List nodes in a revision, filtered by node property values.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • properties (Dict[str, Dict[str, List[str]]]) – Properties for filtering. The object contains one or more category. Each category references one or more properties. Each property is associated with a list of values. For a node to satisfy the filter, it must, for each category/property in the filter, contain the catogery+property combination with a value that is contained within the corresponding list in the filter.
  • limit (int) – Maximun number of nodes to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
  • partitions (int) – The result is retrieved in this many parts in parallel. Requires sort_by_node_id to be set to true.
Returns:

The list of 3d nodes.

Return type:

ThreeDNodeList

Example

Filter nodes from the hierarchy in the 3d model that have one of the values “AB76”, “AB77” or “AB78” for property PDMS/Area AND that also have one of the values “PIPE”, “BEND” or “PIPESUP” for the property PDMS/Type.

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.filter_nodes(model_id=1, revision_id=1, properties={ "PDMS": { "Area": ["AB76", "AB77", "AB78"], "Type": ["PIPE", "BEND", "PIPESUP"] } }, limit=10)

List ancestor nodes

ThreeDRevisionsAPI.list_ancestor_nodes(model_id: int, revision_id: int, node_id: int = None, limit: int = 25) → cognite.client.data_classes.three_d.ThreeDNodeList

Retrieves a list of ancestor nodes of a given node, including itself, in the hierarchy of the 3D model

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • node_id (int) – ID of the node to get the ancestors of.
  • limit (int) – Maximun number of nodes to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The list of 3d nodes.

Return type:

ThreeDNodeList

Example

Get a list of ancestor nodes of a given node:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.revisions.list_ancestor_nodes(model_id=1, revision_id=1, node_id=5, limit=10)

Files

Retrieve a 3D file

ThreeDFilesAPI.retrieve(id: int) → bytes

Retrieve the contents of a 3d file by id.

Parameters:id (int) – The id of the file to retrieve.
Returns:The contents of the file.
Return type:bytes

Example

Retrieve the contents of a 3d file by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.files.retrieve(1)

Asset mappings

Create an asset mapping

ThreeDAssetMappingAPI.create(model_id: int, revision_id: int, asset_mapping: Union[cognite.client.data_classes.three_d.ThreeDAssetMapping, List[cognite.client.data_classes.three_d.ThreeDAssetMapping]]) → Union[cognite.client.data_classes.three_d.ThreeDAssetMapping, cognite.client.data_classes.three_d.ThreeDAssetMappingList]

Create 3d node asset mappings.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • asset_mapping (Union[ThreeDAssetMapping, List[ThreeDAssetMapping]]) – The asset mapping(s) to create.
Returns:

The created asset mapping(s).

Return type:

Union[ThreeDAssetMapping, ThreeDAssetMappingList]

Example

Create new 3d node asset mapping:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ThreeDAssetMapping
>>> my_mapping = ThreeDAssetMapping(node_id=1, asset_id=1)
>>> c = CogniteClient()
>>> res = c.three_d.asset_mappings.create(model_id=1, revision_id=1, asset_mapping=my_mapping)

List asset mappings

ThreeDAssetMappingAPI.list(model_id: int, revision_id: int, node_id: int = None, asset_id: int = None, limit: int = 25) → cognite.client.data_classes.three_d.ThreeDAssetMappingList

List 3D node asset mappings.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • node_id (int) – List only asset mappings associated with this node.
  • asset_id (int) – List only asset mappings associated with this asset.
  • limit (int) – Maximum number of asset mappings to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The list of asset mappings.

Return type:

ThreeDAssetMappingList

Example

List 3d node asset mappings:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.three_d.asset_mappings.list(model_id=1, revision_id=1)

Delete asset mappings

ThreeDAssetMappingAPI.delete(model_id: int, revision_id: int, asset_mapping: Union[cognite.client.data_classes.three_d.ThreeDAssetMapping, List[cognite.client.data_classes.three_d.ThreeDAssetMapping]]) → None

Delete 3d node asset mappings.

Parameters:
  • model_id (int) – Id of the model.
  • revision_id (int) – Id of the revision.
  • asset_mapping (Union[ThreeDAssetMapping, List[ThreeDAssetMapping]]) – The asset mapping(s) to delete.
Returns:

None

Example

Delete 3d node asset mapping:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> mapping_to_delete = c.three_d.asset_mappings.list(model_id=1, revision_id=1)[0]
>>> res = c.three_d.asset_mappings.delete(model_id=1, revision_id=1, asset_mapping=mapping_to_delete)

Data classes

class cognite.client.data_classes.three_d.BoundingBox3D(max: List[float] = None, min: List[float] = None, **kwargs)

Bases: dict

The bounding box of the subtree with this sector as the root sector. Is null if there are no geometries in the subtree.

Parameters:
  • max (List[float]) – No description.
  • min (List[float]) – No description.
class cognite.client.data_classes.three_d.RevisionCameraProperties(target: List[float] = None, position: List[float] = None, **kwargs)

Bases: dict

Initial camera position and target.

Parameters:
  • target (List[float]) – Initial camera target.
  • position (List[float]) – Initial camera position.
class cognite.client.data_classes.three_d.ThreeDAssetMapping(node_id: int = None, asset_id: int = None, tree_index: int = None, subtree_size: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • node_id (int) – The ID of the node.
  • asset_id (int) – The ID of the associated asset (Cognite’s Assets API).
  • tree_index (int) – A number describing the position of this node in the 3D hierarchy, starting from 0. The tree is traversed in a depth-first order.
  • subtree_size (int) – The number of nodes in the subtree of this node (this number included the node itself).
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.three_d.ThreeDAssetMappingList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.three_d.ThreeDModel(name: str = None, id: int = None, created_time: int = None, metadata: Dict[str, str] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • name (str) – The name of the model.
  • id (int) – The ID of the model.
  • created_time (int) – The creation time of the resource, in milliseconds since January 1, 1970 at 00:00 UTC.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.three_d.ThreeDModelList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.three_d.ThreeDModelRevision(id: int = None, file_id: int = None, published: bool = None, rotation: List[float] = None, camera: Union[Dict[str, Any], cognite.client.data_classes.three_d.RevisionCameraProperties] = None, status: str = None, metadata: Dict[str, str] = None, thumbnail_threed_file_id: int = None, thumbnail_url: str = None, asset_mapping_count: int = None, created_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • id (int) – The ID of the revision.
  • file_id (int) – The file id.
  • published (bool) – True if the revision is marked as published.
  • rotation (List[float]) – No description.
  • camera (Union[Dict[str, Any], RevisionCameraProperties]) – Initial camera position and target.
  • status (str) – The status of the revision.
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 32 bytes, value 512 bytes, up to 16 key-value pairs.
  • thumbnail_threed_file_id (int) – The threed file ID of a thumbnail for the revision. Use /3d/files/{id} to retrieve the file.
  • thumbnail_url (str) – The URL of a thumbnail for the revision.
  • asset_mapping_count (int) – The number of asset mappings for this revision.
  • created_time (int) – The creation time of the resource, in milliseconds since January 1, 1970 at 00:00 UTC.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.three_d.ThreeDModelRevisionList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.three_d.ThreeDModelRevisionUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

No description.

Parameters:id (int) – A server-generated ID for the object.
class cognite.client.data_classes.three_d.ThreeDModelUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

No description.

Parameters:id (int) – A server-generated ID for the object.
class cognite.client.data_classes.three_d.ThreeDNode(id: int = None, tree_index: int = None, parent_id: int = None, depth: int = None, name: str = None, subtree_size: int = None, properties: Dict[str, Dict[str, str]] = None, bounding_box: Union[Dict[str, Any], cognite.client.data_classes.three_d.BoundingBox3D] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • id (int) – The ID of the node.
  • tree_index (int) – The index of the node in the 3D model hierarchy, starting from 0. The tree is traversed in a depth-first order.
  • parent_id (int) – The parent of the node, null if it is the root node.
  • depth (int) – The depth of the node in the tree, starting from 0 at the root node.
  • name (str) – The name of the node.
  • subtree_size (int) – The number of descendants of the node, plus one (counting itself).
  • properties (Dict[str, Dict[str, str]]) – Properties extracted from 3D model, with property categories containing key/value string pairs.
  • bounding_box (Union[Dict[str, Any], BoundingBox3D]) – The bounding box of the subtree with this sector as the root sector. Is null if there are no geometries in the subtree.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.three_d.ThreeDNodeList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

Contextualization

These APIs will return as soon as possible, defering a blocking wait until the last moment. Nevertheless, they can block for a long time awaiting results.

Fit Entity Matching Model

EntityMatchingAPI.fit(sources: List[Union[Dict[KT, VT], cognite.client.data_classes._base.CogniteResource]], targets: List[Union[Dict[KT, VT], cognite.client.data_classes._base.CogniteResource]], true_matches: List[Union[Dict[KT, VT], Tuple[Union[int, str], Union[int, str]]]] = None, match_fields: Union[Dict[KT, VT], List[Tuple[str, str]]] = None, feature_type: str = None, classifier: str = None, ignore_missing_fields: bool = False, name: str = None, description: str = None, external_id: str = None) → cognite.client.data_classes.contextualization.EntityMatchingModel

Fit entity matching model. Note: All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters:
  • sources – entities to match from, should have an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). Metadata fields are automatically flattened to “metadata.key” entries, such that they can be used in match_fields.
  • targets – entities to match to, should have an ‘id’ field. Tolerant to passing more than is needed or used.
  • true_matches – Known valid matches given as a list of dicts with keys ‘sourceId’, ‘sourceExternalId’, ‘sourceId’, ‘sourceExternalId’). If omitted, uses an unsupervised model. A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
  • match_fields – List of (from,to) keys to use in matching. Default in the API is [(‘name’,’name’)]. Also accepts {“source”: .., “target”: ..}.
  • feature_type (str) – feature type that defines the combination of features used, see API docs for details.
  • classifier (str) – classifier used in training.
  • ignore_missing_fields (bool) – whether missing data in match_fields should return error or be filled in with an empty string.
  • name (str) – Optional user-defined name of model.
  • description (str) – Optional user-defined description of model.
  • external_id (str) – Optional external id. Must be unique within the project.
Returns:

Resulting queued model.

Return type:

EntityMatchingModel

Re-fit Entity Matching Model

EntityMatchingAPI.refit(true_matches: List[Union[Dict[KT, VT], Tuple[Union[int, str], Union[int, str]]]], id: Optional[int] = None, external_id: Optional[str] = None) → cognite.client.data_classes.contextualization.EntityMatchingModel

Re-fits an entity matching model, using the combination of the old and new true matches. Note: All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters:
  • true_matches – Updated known valid matches given as a list of dicts with keys ‘fromId’, ‘fromExternalId’, ‘toId’, ‘toExternalId’). A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
  • id – ids of the model to use.
  • external_id – external ids of the model to use.
Returns:

new model refitted to true_matches.

Return type:

EntityMatchingModel

Retrieve Entity Matching Models

EntityMatchingAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.contextualization.EntityMatchingModel]

Retrieve model

Parameters:
  • id (int) – id of the model to retrieve.
  • external_id (str) – external id of the model to retrieve.
Returns:

Model requested.

Return type:

EntityMatchingModel

EntityMatchingAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None) → cognite.client.data_classes.contextualization.EntityMatchingModelList

Retrieve models

Parameters:
  • ids (List[int]) – ids of the model to retrieve.
  • external_ids (List[str]) – external ids of the model to retrieve.
Returns:

Models requested.

Return type:

EntityMatchingModelList

EntityMatchingAPI.list(name: str = None, description: str = None, original_id: int = None, feature_type: str = None, classifier: str = None, limit=100) → cognite.client.data_classes.contextualization.EntityMatchingModelList

List models

Parameters:
  • name (str) – Optional user-defined name of model.
  • description (str) – Optional user-defined description of model.
  • feature_type (str) – feature type that defines the combination of features used.
  • classifier (str) – classifier used in training.
  • original_id (int) – id of the original model for models that were created with refit.
  • limit (int, optional) – Maximum number of items to return. Defaults to 100. Set to -1, float(“inf”) or None to return all items.
Returns:

List of models.

Return type:

EntityMatchingModelList

Delete Entity Matching Models

EntityMatchingAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None) → None

Delete models

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of external ids

Update Entity Matching Models

EntityMatchingAPI.update(item: Union[cognite.client.data_classes.contextualization.EntityMatchingModel, cognite.client.data_classes.contextualization.EntityMatchingModelUpdate, List[Union[cognite.client.data_classes.contextualization.EntityMatchingModel, cognite.client.data_classes.contextualization.EntityMatchingModelUpdate]]]) → Union[cognite.client.data_classes.contextualization.EntityMatchingModel, List[cognite.client.data_classes.contextualization.EntityMatchingModel]]

Update model

Parameters:item (Union[EntityMatchingModel,EntityMatchingModelUpdate,List[Union[EntityMatchingModel,EntityMatchingModelUpdate]]) – Model(s) to update

Predict Using an Entity Matching Model

EntityMatchingAPI.predict(sources: Optional[List[Dict[KT, VT]]] = None, targets: Optional[List[Dict[KT, VT]]] = None, num_matches=1, score_threshold=None, id: Optional[int] = None, external_id: Optional[str] = None) → cognite.client.data_classes.contextualization.ContextualizationJob

Predict entity matching. NB. blocks and waits for the model to be ready if it has been recently created. Note: All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint.

Parameters:
  • sources – entities to match from, does not need an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). If omitted, will use data from fit.
  • targets – entities to match to, does not need an ‘id’ field. Tolerant to passing more than is needed or used. If omitted, will use data from fit.
  • num_matches (int) – number of matches to return for each item.
  • score_threshold (float) – only return matches with a score above this threshold
  • ignore_missing_fields (bool) – whether missing data in match_fields should be filled in with an empty string.
  • id – ids of the model to use.
  • external_id – external ids of the model to use.
Returns:

object which can be used to wait for and retrieve results.

Return type:

ContextualizationJob

Contextualization Data Classes

class cognite.client.data_classes.contextualization.ContextualizationJob(job_id=None, model_id=None, status=None, error_message=None, created_time=None, start_time=None, status_time=None, status_path=None, cognite_client=None, **kwargs)

Bases: cognite.client.data_classes._base.CogniteResource

dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
result

Waits for the job to finish and returns the results.

to_pandas(expand: List[str] = ('metadata', ), ignore: List[str] = None, camel_case: bool = True)

Convert the instance into a pandas DataFrame.

Parameters:
  • expand (List[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
  • ignore (List[str]) – List of row keys to not include when converting to a data frame.
  • camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns:

The dataframe.

Return type:

pandas.DataFrame

update_status() → str

Updates the model status and returns it

wait_for_completion(timeout=None, interval=1)

Waits for job completion, raising ModelFailedException if fit failed - generally not needed to call as it is called by result. :param timeout: Time out after this many seconds. (None means wait indefinitely) :param interval: Poll status every this many seconds.

class cognite.client.data_classes.contextualization.ContextualizationJobList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

append(item)

S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S
copy()
count(value) → integer -- return number of occurrences of value
dump(camel_case: bool = False) → List[Dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A list of dicts representing the instance.
Return type:List[Dict[str, Any]]
extend(other)

S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int = None, external_id: str = None) → Optional[cognite.client.data_classes._base.CogniteResource]

Get an item from this list by id or exernal_id.

Parameters:
  • id (int) – The id of the item to get.
  • external_id (str) – The external_id of the item to get.
Returns:

The requested item

Return type:

Optional[CogniteResource]

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item)

S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

remove(item)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

sort(*args, **kwds)
to_pandas(camel_case=True) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Returns:The dataframe.
Return type:pandas.DataFrame
class cognite.client.data_classes.contextualization.ContextualizationJobType

Bases: enum.Enum

An enumeration.

ENTITY_MATCHING = 'entity_matching'
class cognite.client.data_classes.contextualization.EntityMatchingModel(id=None, status=None, error_message=None, created_time=None, start_time=None, status_time=None, cognite_client=None, classifier=None, feature_type=None, match_fields=None, model_type=None, name=None, description=None, external_id=None)

Bases: cognite.client.data_classes._base.CogniteResource

dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
predict(sources: Optional[List[Dict[KT, VT]]] = None, targets: Optional[List[Dict[KT, VT]]] = None, num_matches=1, score_threshold=None) → cognite.client.data_classes.contextualization.ContextualizationJob

Predict entity matching. NB. blocks and waits for the model to be ready if it has been recently created.

Parameters:
  • sources – entities to match from, does not need an ‘id’ field. Tolerant to passing more than is needed or used (e.g. json dump of time series list). If omitted, will use data from fit.
  • targets – entities to match to, does not need an ‘id’ field. Tolerant to passing more than is needed or used. If omitted, will use data from fit.
  • num_matches (int) – number of matches to return for each item.
  • score_threshold (float) – only return matches with a score above this threshold
  • ignore_missing_fields (bool) – whether missing data in match_fields should be filled in with an empty string.
Returns:

object which can be used to wait for and retrieve results.

Return type:

ContextualizationJob

refit(true_matches: List[Union[Dict[KT, VT], Tuple[Union[int, str], Union[int, str]]]]) → cognite.client.data_classes.contextualization.EntityMatchingModel

Re-fits an entity matching model, using the combination of the old and new true matches.

Parameters:true_matches – Updated known valid matches given as a list of dicts with keys ‘fromId’, ‘fromExternalId’, ‘toId’, ‘toExternalId’). A tuple can be used instead of the dictionary for convenience, interpreted as id/externalId based on type.
Returns:new model refitted to true_matches.
Return type:EntityMatchingModel
to_pandas(expand: List[str] = ('metadata', ), ignore: List[str] = None, camel_case: bool = True)

Convert the instance into a pandas DataFrame.

Parameters:
  • expand (List[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
  • ignore (List[str]) – List of row keys to not include when converting to a data frame.
  • camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns:

The dataframe.

Return type:

pandas.DataFrame

update_status() → str

Updates the model status and returns it

wait_for_completion(timeout: int = None, interval: int = 1) → None

Waits for model completion, raising ModelFailedException if fit failed - generally not needed to call as it is called by predict

Parameters:
  • timeout – Time out after this many seconds. (None means wait indefinitely)
  • interval – Poll status every this many seconds.
class cognite.client.data_classes.contextualization.EntityMatchingModelList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

append(item)

S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S
copy()
count(value) → integer -- return number of occurrences of value
dump(camel_case: bool = False) → List[Dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A list of dicts representing the instance.
Return type:List[Dict[str, Any]]
extend(other)

S.extend(iterable) – extend sequence by appending elements from the iterable

get(id: int = None, external_id: str = None) → Optional[cognite.client.data_classes._base.CogniteResource]

Get an item from this list by id or exernal_id.

Parameters:
  • id (int) – The id of the item to get.
  • external_id (str) – The external_id of the item to get.
Returns:

The requested item

Return type:

Optional[CogniteResource]

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(i, item)

S.insert(index, value) – insert value before index

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

remove(item)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

sort(*args, **kwds)
to_pandas(camel_case=True) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Returns:The dataframe.
Return type:pandas.DataFrame
class cognite.client.data_classes.contextualization.EntityMatchingModelUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes applied to entity matching model

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
description
dump()

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
name
class cognite.client.data_classes.contextualization.JobStatus

Bases: enum.Enum

An enumeration.

COLLECTING = 'Collecting'
COMPLETED = 'Completed'
DISTRIBUTED = 'Distributed'
DISTRIBUTING = 'Distributing'
FAILED = 'Failed'
QUEUED = 'Queued'
RUNNING = 'Running'
is_not_finished() → bool

Templates

Create Template groups

TemplateGroupsAPI.create(template_groups: Union[cognite.client.data_classes.templates.TemplateGroup, List[cognite.client.data_classes.templates.TemplateGroup]]) → Union[cognite.client.data_classes.templates.TemplateGroup, cognite.client.data_classes.templates.TemplateGroupList]

Create one or more template groups.

Parameters:template_groups (Union[TemplateGroup, List[TemplateGroup]]) –
Returns:Created template group(s)
Return type:Union[TemplateGroup, TemplateGroupList]

Examples

Create a new template group:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TemplateGroup
>>> c = CogniteClient()
>>> template_group_1 = TemplateGroup("sdk-test-group", "This is a test group")
>>> template_group_2 = TemplateGroup("sdk-test-group-2", "This is another test group")
>>> c.templates.groups.create([template_group_1, template_group_2])

Upsert Template groups

TemplateGroupsAPI.upsert(template_groups: Union[cognite.client.data_classes.templates.TemplateGroup, List[cognite.client.data_classes.templates.TemplateGroup]]) → Union[cognite.client.data_classes.templates.TemplateGroup, cognite.client.data_classes.templates.TemplateGroupList]

Upsert one or more template groups. Will overwrite existing template group(s) with the same external id(s).

Parameters:template_groups (Union[TemplateGroup, List[TemplateGroup]]) –
Returns:Created template group(s)
Return type:Union[TemplateGroup, TemplateGroupList]

Examples

Upsert a template group:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TemplateGroup
>>> c = CogniteClient()
>>> template_group_1 = TemplateGroup("sdk-test-group", "This is a test group")
>>> template_group_2 = TemplateGroup("sdk-test-group-2", "This is another test group")
>>> c.templates.groups.upsert([template_group_1, template_group_2])

Retrieve Template groups

TemplateGroupsAPI.retrieve_multiple(external_ids: List[str], ignore_unknown_ids: bool = False) → cognite.client.data_classes.templates.TemplateGroupList

Retrieve multiple template groups by external id.

Parameters:
  • external_ids (List[str]) – External IDs
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

The requested template groups.

Return type:

TemplateGroupList

Examples

Get template groups by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.templates.groups.retrieve_multiple(external_ids=["abc", "def"])

List Template groups

TemplateGroupsAPI.list(limit: int = 25, owners: List[str] = None) → cognite.client.data_classes.templates.TemplateGroupList

Lists template groups stored in the project based on a query filter given in the payload of this request. Up to 1000 template groups can be retrieved in one operation.

Parameters:
  • owners (List[str]) – Include template groups that have any of these values in their owner field.
  • limit (int) – Maximum number of template groups to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested template groups

Return type:

TemplateGroupList

Examples

List template groups:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> template_group_list = c.templates.groups.list(limit=5)

Delete Template groups

TemplateGroupsAPI.delete(external_ids: Union[str, List[str]], ignore_unknown_ids: bool = False) → Union[cognite.client.data_classes.templates.TemplateGroup, cognite.client.data_classes.templates.TemplateGroupList]

Delete one or more template groups.

Parameters:
  • external_ids (Union[str, List[str]]) – External ID or list of external ids
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete template groups by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.groups.delete(external_ids=["a", "b"])

Upsert a Template group version

TemplateGroupVersionsAPI.upsert(external_id: str, version: cognite.client.data_classes.templates.TemplateGroupVersion)

Upsert a template group version. A Template Group update supports specifying different conflict modes, which is used when an existing schema already exists.

Patch -> It diffs the new schema with the old schema and fails if there are breaking changes. Update -> It sets the new schema as schema of a new version. Force -> It ignores breaking changes and replaces the old schema with the new schema. The default mode is “patch”.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (TemplateGroupVersion) – The GraphQL schema of this version.
Returns:

Created template group version

Return type:

TemplateGroupVersion

Examples

create a new template group version modeling Covid-19:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TemplateGroup
>>> c = CogniteClient()
>>> template_group = TemplateGroup("sdk-test-group", "This template group models Covid-19 spread")
>>> c.templates.groups.create(template_group)
>>> schema = '''
>>>     type Demographics @template {
>>>         "The amount of people"
>>>         populationSize: Int,
>>>         "The population growth rate"
>>>         growthRate: Float,
>>>     }
>>>     type Country @template {
>>>         name: String,
>>>         demographics: Demographics,
>>>         deaths: TimeSeries,
>>>         confirmed: TimeSeries,
>>>     }'''
>>> template_group_version = TemplateGroupVersion(schema)
>>> c.templates.versions.upsert(template_group.external_id, template_group_version)

List Temple Group versions

TemplateGroupVersionsAPI.list(external_id: str, limit: int = 25, min_version: Optional[int] = None, max_version: Optional[int] = None) → cognite.client.data_classes.templates.TemplateGroupVersionList

Lists versions of a specified template group. Up to 1000 template group version can be retrieved in one operation.

Parameters:
  • external_id (str) – The external id of the template group.
  • limit (int) – Maximum number of template group versions to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
  • min_version – (Optional[int]): Exclude versions with a version number smaller than this.
  • max_version – (Optional[int]): Exclude versions with a version number larger than this.
Returns:

List of requested template group versions

Return type:

TemplateGroupVersionList

Examples

List template group versions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> template_group_list = c.templates.versions.list("template-group-ext-id", limit=5)

Delete a Temple Group version

TemplateGroupVersionsAPI.delete(external_id: str, version: int) → None

Delete a template group version.

Parameters:
  • external_id (Union[str, List[str]]) – External ID of the template group.
  • version (int) – The version of the template group to delete.
Returns:

None

Examples

Delete template groups by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.versions.delete("sdk-test-group", 1)

Run a GraphQL query

TemplatesAPI.graphql_query(external_id: str, version: int, query: str) → cognite.client.data_classes.templates.GraphQlResponse

Run a GraphQL Query. To learn more, see https://graphql.org/learn/

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group to run the query on.
  • query (str) – The GraphQL query to run.
Returns:

the result of the query.

Return type:

GraphQlResponse

Examples

Run a GraphQL query:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> query = '''
>>>    {
>>>        countryQuery {
>>>           name,
>>>           demographics {
>>>               populationSize,
>>>               growthRate
>>>           },
>>>           deaths {
>>>               datapoints(limit: 100) {
>>>                   timestamp,
>>>                   value
>>>               }
>>>           }
>>>        }
>>>    }
>>>    '''
>>> result = c.templates.graphql_query("template-group-ext-id", 1, query)

Create Template instances

TemplateInstancesAPI.create(external_id: str, version: int, instances: Union[cognite.client.data_classes.templates.TemplateInstance, List[cognite.client.data_classes.templates.TemplateInstance]]) → Union[cognite.client.data_classes.templates.TemplateInstance, cognite.client.data_classes.templates.TemplateInstanceList]

Create one or more template instances.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group to create instances for.
  • instances (Union[TemplateInstance, List[TemplateInstance]]) – The instances to create.
Returns:

Created template instance(s).

Return type:

Union[TemplateInstance, TemplateInstanceList]

Examples

create new template instances for Covid-19 spread:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TemplateInstance
>>> c = CogniteClient()
>>> template_instance_1 = TemplateInstance(external_id="norway",
>>>                               template_name="Country",
>>>                               field_resolvers={
>>>                                   "name": ConstantResolver("Norway"),
>>>                                   "demographics": ConstantResolver("norway_demographics"),
>>>                                   "deaths": ConstantResolver("Norway_deaths"),
>>>                                   "confirmed": ConstantResolver("Norway_confirmed"),
>>>                                   }
>>>                               )
>>> template_instance_2 = TemplateInstance(external_id="norway_demographics",
>>>                               template_name="Demographics",
>>>                               field_resolvers={
>>>                                   "populationSize": ConstantResolver(5328000),
>>>                                   "growthRate": ConstantResolver(value=0.02)
>>>                                   }
>>>                               )
>>> c.templates.instances.create("sdk-test-group", 1, [template_instance_1, template_instance_2])

Upsert Template instances

TemplateInstancesAPI.upsert(external_id: str, version: int, instances: Union[cognite.client.data_classes.templates.TemplateGroup, List[cognite.client.data_classes.templates.TemplateGroup]]) → Union[cognite.client.data_classes.templates.TemplateInstance, cognite.client.data_classes.templates.TemplateInstanceList]

Upsert one or more template instances. Will overwrite existing instances.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group to create instances for.
  • instances (Union[TemplateInstance, List[TemplateInstance]]) – The instances to create.
Returns:

Created template instance(s).

Return type:

Union[TemplateInstance, TemplateInstanceList]

Examples

create new template instances for Covid-19 spread:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TemplateInstance
>>> c = CogniteClient()
>>> template_instance_1 = TemplateInstance(external_id="norway",
>>>        template_name="Country",
>>>        field_resolvers={
>>>            "name": ConstantResolver("Norway"),
>>>            "demographics": ConstantResolver("norway_demographics"),
>>>            "deaths": ConstantResolver("Norway_deaths"),
>>>            "confirmed": ConstantResolver("Norway_confirmed"),
>>>        }
>>>    )
>>> template_instance_2 = TemplateInstance(external_id="norway_demographics",
>>>       template_name="Demographics",
>>>       field_resolvers={
>>>           "populationSize": ConstantResolver(5328000),
>>>           "growthRate": ConstantResolver(0.02)
>>>           }
>>>   )
>>> c.templates.instances.upsert("sdk-test-group", 1, [template_instance_1, template_instance_2])

Update Template instances .. automethod:: cognite.client._api.templates.TemplateInstancesAPI.update

Retrieve Template instances

TemplateInstancesAPI.retrieve_multiple(external_id: str, version: int, external_ids: List[str], ignore_unknown_ids: bool = False) → cognite.client.data_classes.templates.TemplateInstanceList

Retrieve multiple template instances by external id.

Parameters:
  • external_id (str) – The template group to retrieve instances from.
  • version (int) – The version of the template group.
  • external_ids (List[str]) – External IDs of the instances.
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

The requested template groups.

Return type:

TemplateInstanceList

Examples

Get template instances by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.templates.instances.retrieve_multiple(external_id="sdk-test-group", version=1, external_ids=["abc", "def"])

List Template instances

TemplateInstancesAPI.list(external_id: str, version: int, limit: int = 25, data_set_ids: Optional[List[int]] = None, template_names: Optional[List[str]] = None) → cognite.client.data_classes.templates.TemplateInstanceList

Lists instances in a template group. Up to 1000 template instances can be retrieved in one operation.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group.
  • limit (int) – Maximum number of template group versions to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
  • data_set_ids – (Optional[List[int]]): Only include instances which has one of these values in their data_set_id field.
  • template_names – (Optional[List[str]]): Only include instances which has one of these values in their template_name field.
Returns:

List of requested template instances

Return type:

TemplateInstanceList

Examples

List template instances:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> template_instances_list = c.templates.instances.list("template-group-ext-id", 1, limit=5)

Delete Template instances

TemplateInstancesAPI.delete(external_id: str, version: int, external_ids: List[str], ignore_unknown_ids: bool = False) → None

Delete one or more template instances.

Parameters:
  • external_id (Union[str, List[str]]) – External ID of the template group.
  • version (int) – The version of the template group.
  • external_ids (List[str]) – The external ids of the template instances to delete
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete template groups by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.instances.delete("sdk-test-group", 1, external_ids=["a", "b"])

Create Views

TemplateViewsAPI.create(external_id: str, version: int, views: Union[cognite.client.data_classes.templates.View, List[cognite.client.data_classes.templates.View]]) → Union[cognite.client.data_classes.templates.View, cognite.client.data_classes.templates.ViewList]

Create one or more template views.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group to create views for.
  • views (Union[View, List[View]]) – The views to create.
Returns:

Created view(s).

Return type:

Union[View, ViewList]

Examples

Create new views:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import View
>>> c = CogniteClient()
>>> view = View(external_id="view",
>>>             source=Source(
>>>                 type: 'events',
>>>                 filter: {
>>>                     startTime: {
>>>                         min: "$startTime"
>>>                     },
>>>                     type: "Test",
>>>                 }
>>>                 mappings: {
>>>                     author: "metadata/author"
>>>                 }
>>>             )
>>>        )
>>> c.templates.views.create("sdk-test-group", 1, [view])

Upsert Views

TemplateViewsAPI.upsert(external_id: str, version: int, views: Union[cognite.client.data_classes.templates.View, List[cognite.client.data_classes.templates.View]]) → Union[cognite.client.data_classes.templates.View, cognite.client.data_classes.templates.ViewList]

Upsert one or more template views.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group to create views for.
  • views (Union[View, List[View]]) – The views to create.
Returns:

Created view(s).

Return type:

Union[View, ViewList]

Examples

Upsert new views:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import View
>>> c = CogniteClient()
>>> view = View(external_id="view",
>>>             source=Source(
>>>                 type: 'events',
>>>                 filter: {
>>>                     startTime: {
>>>                         min: "$startTime"
>>>                     },
>>>                     type: "Test",
>>>                 }
>>>                 mappings: {
>>>                     author: "metadata/author"
>>>                 }
>>>             )
>>>        )
>>> c.templates.views.upsert("sdk-test-group", 1, [view])

List Views

TemplateViewsAPI.list(external_id: str, version: int, limit: int = 25) → cognite.client.data_classes.templates.ViewList

Lists view in a template group. Up to 1000 views can be retrieved in one operation.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group.
  • limit (int) – Maximum number of views to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested views

Return type:

ViewList

Examples

List views:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.views.list("template-group-ext-id", 1, limit=5)

Resolve View

TemplateViewsAPI.resolve(external_id: str, version: int, view_external_id: str, input: Optional[Dict[str, any]], limit: int = 25) → cognite.client.data_classes.templates.ViewResolveList

Resolves a View. It resolves the source specified in a View with the provided input and applies the mapping rules to the response.

Parameters:
  • external_id (str) – The external id of the template group.
  • version (int) – The version of the template group.
  • input (Optional[Dict[str, any]]) – The input for the View.
  • limit (int) – Maximum number of views to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

The resolved items.

Return type:

ViewResolveList

Examples

Resolve view:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.views.resolve("template-group-ext-id", 1, "view", { "startTime": 10 }, limit=5)

Delete Views

TemplateViewsAPI.delete(external_id: str, version: int, view_external_id: Union[List[str], str], ignore_unknown_ids: bool = False) → None

Delete one or more views.

Parameters:
  • external_id (Union[str, List[str]]) – External ID of the template group.
  • version (int) – The version of the template group.
  • view_external_id (Union[List[str], str]) – The external ids of the views to delete
  • ignore_unknown_ids (bool) – Ignore external IDs that are not found rather than throw an exception.
Returns:

None

Examples

Delete views by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.templates.views.delete("sdk-test-group", 1, external_id=["a", "b"])

Data classes

class cognite.client.data_classes.templates.ConstantResolver(value: any = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Resolves a field to a constant value. The value can be of any supported JSON type.

Parameters:value (any) – The value of the field.
class cognite.client.data_classes.templates.RawResolver(db_name: str = None, table_name: str = None, row_key: str = None, column_name: str = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Resolves a field to a RAW column.

Parameters:
  • db_name (str) – The database name.
  • table_name (str) – The table name.
  • row_key (str) – The row key.
  • column_name (str) – The column to fetch the value from.
class cognite.client.data_classes.templates.Source(type: str = None, filter: Dict[str, any] = None, mappings: Dict[str, str] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A source defines the data source with filters and a mapping table.

Parameters:
  • type (str) – The type of source. Possible values are: “events”, “assets”, “sequences”, “timeSeries”, “files”.
  • filter (Dict[str, any]) – The filter to apply to the source when resolving the source. A filter also supports binding view input to the filter, by prefixing the input name with ‘$’.
  • mappings (Dict[str, str]) – The mapping between source result and expected schema.
class cognite.client.data_classes.templates.SyntheticTimeSeriesResolver(expression: str = None, name: Optional[str] = None, description: Optional[str] = None, metadata: Optional[Dict[str, str]] = None, is_step: Optional[bool] = None, is_string: Optional[bool] = None, unit: Optional[str] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Resolves a field of type ‘SyntheticTimeSeries’ to a Synthetic Time Series.

Parameters:
  • expression (str) – The synthetic time series expression. See this for syntax https://docs.cognite.com/api/v1/#tag/Synthetic-Time-Series.
  • name (Optional[str]) – The name of the Time Series.
  • description (Optional[str]) – The description for the Time Series.
  • metadata (Optional[Dict[str, str]]) – Specifies metadata for the Time Series.
  • is_step (Optional[bool]) – Specifies if the synthetic time series is step based.
  • is_string (Optional[bool]) – Specifies if the synthetic time series returned contains string values.
  • unit (Optional[str]) – The unit of the time series.
class cognite.client.data_classes.templates.TemplateGroup(external_id: str = None, description: str = None, owners: Optional[List[str]] = None, data_set_id: int = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A template group is a high level concept encapsulating a schema and a set of template instances. It also has query capability support.

Template groups are versioned, so there can be multiple template groups with the same external ID. The versioning is happening automatically whenever a template groups is changed.

GraphQL schema definition language is used as the language to describe the structure of the templates and data types.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • description (str) – The description of the template groups.
  • owners (List[str]) – The list of owners for the template groups.
  • data_set_id (int) – The dataSet which this Template Group belongs to
class cognite.client.data_classes.templates.TemplateGroupList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.templates.TemplateGroupVersion(schema: str = None, version: int = None, conflict_mode: str = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A Template Group Version supports specifying different conflict modes, which is used when an existing schema already exists.

Patch -> It diffs the new schema with the old schema and fails if there are breaking changes. Update -> It sets the new schema as schema of a new version. Force -> It ignores breaking changes and replaces the old schema with the new schema. The default mode is “patch”.

Parameters:
  • schema (str) – The GraphQL schema.
  • version (int) – Incremented by the server whenever the schema of a template groups changes.
  • conflict_mode (str) – Can be set to ‘Patch’, ‘Update’ or ‘Force’.
class cognite.client.data_classes.templates.TemplateGroupVersionList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.templates.TemplateInstance(external_id: str = None, template_name: str = None, field_resolvers: Dict[str, Union[cognite.client.data_classes.templates.ConstantResolver, cognite.client.data_classes.templates.RawResolver, cognite.client.data_classes.templates.SyntheticTimeSeriesResolver, str, cognite.client.data_classes.templates.ViewResolver]] = None, data_set_id: Optional[int] = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A template instance that implements a template by specifying a resolver per field.

Parameters:
  • external_id (str) – The id of the template instance.
  • template_name (str) – The template name to implement.
  • field_resolvers (Dict[str, FieldResolvers]) – A set of field resolvers where the dictionary key correspond to the field name.
  • data_set_id (int) – The id of the dataset this instance belongs to.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
class cognite.client.data_classes.templates.TemplateInstanceList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.templates.TemplateInstanceUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes applied to template instance

Parameters:external_id (str) – The external ID provided by the client. Must be unique for the resource type.
class cognite.client.data_classes.templates.View(external_id: str = None, source: cognite.client.data_classes.templates.Source = None, created_time: int = None, last_updated_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A view is used to map existing data to a type in the template group. A view supports input, that can be bound to the underlying filter.

Parameters:
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • source (Source) – Defines the data source for the view.
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
class cognite.client.data_classes.templates.ViewList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.templates.ViewResolveList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.templates.ViewResolver(external_id: str = None, input: Optional[Dict[str, any]] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

Resolves the field by loading the data from a view.

Parameters:
  • external_id (str) – The external id of the view.
  • input (Optional[Dict[str, any]]) – The input used to resolve the view.

Identity and access management

Service accounts

List service accounts

ServiceAccountsAPI.list() → cognite.client.data_classes.iam.ServiceAccountList

List service accounts.

Returns:List of service accounts.
Return type:ServiceAccountList

Example

List service accounts:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.service_accounts.list()

Create service accounts

ServiceAccountsAPI.create(service_account: Union[cognite.client.data_classes.iam.ServiceAccount, List[cognite.client.data_classes.iam.ServiceAccount]]) → Union[cognite.client.data_classes.iam.ServiceAccount, cognite.client.data_classes.iam.ServiceAccountList]

Create one or more new service accounts.

Parameters:service_account (Union[ServiceAccount, List[ServiceAccount]]) – The service account(s) to create.
Returns:The created service account(s).
Return type:Union[ServiceAccount, ServiceAccountList]

Example

Create service account:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ServiceAccount
>>> c = CogniteClient()
>>> my_account = ServiceAccount(name="my@service.com", groups=[1, 2, 3])
>>> res = c.iam.service_accounts.create(my_account)

Delete service accounts

ServiceAccountsAPI.delete(id: Union[int, List[int]]) → None

Delete one or more service accounts.

Parameters:id (Union[int, List[int]]) – ID or list of IDs to delete.
Returns:None

Example

Delete service account by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.iam.service_accounts.delete(1)

API keys

List API keys

APIKeysAPI.list(include_deleted: bool = False, all: bool = False, service_account_id: bool = None) → cognite.client.data_classes.iam.APIKeyList

List api keys.

Parameters:
  • include_deleted (bool) – Whether or not to include deleted api keys. Defaults to False.
  • all (bool) – Whether or not to return all api keys for this project. Requires users:list acl. Defaults to False.
  • service_account_id (int) – Get api keys for this service account only. Only available to admin users.
Returns:

List of api keys.

Return type:

APIKeyList

Example

List api keys:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.api_keys.list()

Create API keys

APIKeysAPI.create(service_account_id: Union[int, List[int]]) → Union[cognite.client.data_classes.iam.APIKey, cognite.client.data_classes.iam.APIKeyList]

Create a new api key for one or more service accounts.

Parameters:service_account_id (Union[int, List[int]]) – ID or list of IDs of service accounts to create an api key for.
Returns:API key or list of api keys.
Return type:Union[APIKey, APIKeyList]

Example

Create new api key for a given service account:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.api_keys.create(1)

Delete API keys

APIKeysAPI.delete(id: Union[int, List[int]]) → None

Delete one or more api keys.

Parameters:id (Union[int, List[int]]) – ID or list of IDs of api keys to delete.
Returns:None

Example

Delete api key for a given service account:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.iam.api_keys.delete(1)

Groups

List groups

GroupsAPI.list(all: bool = False) → cognite.client.data_classes.iam.GroupList

List groups.

Parameters:all (bool) – Whether to get all groups, only available with the groups:list acl.
Returns:List of groups.
Return type:GroupList

Example

List groups:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.groups.list()

Create groups

GroupsAPI.create(group: Union[cognite.client.data_classes.iam.Group, List[cognite.client.data_classes.iam.Group]]) → Union[cognite.client.data_classes.iam.Group, cognite.client.data_classes.iam.GroupList]

Create one or more groups.

Parameters:group (Union[Group, List[Group]]) – Group or list of groups to create.
Returns:The created group(s).
Return type:Union[Group, GroupList]

Example

Create group:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import Group
>>> c = CogniteClient()
>>> my_capabilities = [{"groupsAcl": {"actions": ["LIST"],"scope": {"all": { }}}}]
>>> my_group = Group(name="My Group", capabilities=my_capabilities)
>>> res = c.iam.groups.create(my_group)

Delete groups

GroupsAPI.delete(id: Union[int, List[int]]) → None

Delete one or more groups.

Parameters:id (Union[int, List[int]]) – ID or list of IDs of groups to delete.
Returns:None

Example

Delete group:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.iam.groups.delete(1)

List service accounts in a group

GroupsAPI.list_service_accounts(id: int) → cognite.client.data_classes.iam.ServiceAccountList

List service accounts in a group.

Parameters:id (int) – List service accounts which are a member of this group.
Returns:List of service accounts.
Return type:ServiceAccountList

Example

List service accounts in a group:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.groups.list_service_accounts(1)

Add service accounts to a group

GroupsAPI.add_service_account(id: int, service_account_id: Union[int, List[int]]) → None

Add one or more service accounts to a group.

Parameters:
  • id (int) – Add service accounts to the group with this id.
  • service_account_id (Union[int, List[int]]) – Add these service accounts to the specified group.
Returns:

None

Example

Add service account to group:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.groups.add_service_account(id=1, service_account_id=1)

Remove service accounts from a group

GroupsAPI.remove_service_account(id: int, service_account_id: Union[int, List[int]]) → None

Remove one or more service accounts from a group.

Parameters:
  • id (int) – Remove service accounts from the group with this id.
  • service_account_id – Remove these service accounts from the specified group.
Returns:

None

Example

Remove service account from group:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.groups.remove_service_account(id=1, service_account_id=1)

Security categories

List security categories

SecurityCategoriesAPI.list(limit: int = 25) → cognite.client.data_classes.iam.SecurityCategoryList

List security categories.

Parameters:limit (int) – Max number of security categories to return. Defaults to 25.
Returns:List of security categories
Return type:SecurityCategoryList

Example

List security categories:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.iam.security_categories.list()

Create security categories

SecurityCategoriesAPI.create(security_category: Union[cognite.client.data_classes.iam.SecurityCategory, List[cognite.client.data_classes.iam.SecurityCategory]]) → Union[cognite.client.data_classes.iam.SecurityCategory, cognite.client.data_classes.iam.SecurityCategoryList]

Create one or more security categories.

Parameters:security_category (Union[SecurityCategory, List[SecurityCategory]]) – Security category or list of categories to create.
Returns:The created security category or categories.
Return type:Union[SecurityCategory, SecurityCategoryList]

Example

Create security category:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SecurityCategory
>>> c = CogniteClient()
>>> my_category = SecurityCategory(name="My Category")
>>> res = c.iam.security_categories.create(my_category)

Delete security categories

SecurityCategoriesAPI.delete(id: Union[int, List[int]]) → None

Delete one or more security categories.

Parameters:id (Union[int, List[int]]) – ID or list of IDs of security categories to delete.
Returns:None

Example

Delete security category:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.iam.security_categories.delete(1)

Data classes

class cognite.client.data_classes.iam.APIKey(id: int = None, service_account_id: int = None, created_time: int = None, status: str = None, value: str = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • id (int) – The internal ID for the API key.
  • service_account_id (int) – The ID of the service account.
  • created_time (int) – The time of creation in Unix milliseconds.
  • status (str) – The status of the API key.
  • value (str) – The API key to be used against the API.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.iam.APIKeyList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.iam.Group(name: str = None, source_id: str = None, capabilities: List[Dict[str, Any]] = None, id: int = None, is_deleted: bool = None, deleted_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • name (str) – Name of the group
  • source_id (str) – ID of the group in the source. If this is the same ID as a group in the IDP, a service account in that group will implicitly be a part of this group as well.
  • capabilities (List[Dict[str, Any]]) – No description.
  • id (int) – No description.
  • is_deleted (bool) – No description.
  • deleted_time (int) – No description.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.iam.GroupList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.iam.ProjectSpec(url_name: str, groups: List[int])

Bases: cognite.client.data_classes._base.CogniteResponse

A cdf project spec :param url_name: The url name for the project :type url_name: str :param groups: Group ids in the project :type groups: List[int]

class cognite.client.data_classes.iam.SecurityCategory(name: str = None, id: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • name (str) – Name of the security category
  • id (int) – Id of the security category
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.iam.SecurityCategoryList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.iam.ServiceAccount(name: str = None, groups: List[int] = None, id: int = None, is_deleted: bool = None, deleted_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

No description.

Parameters:
  • name (str) – Unique name of the service account
  • groups (List[int]) – List of group ids
  • id (int) – No description.
  • is_deleted (bool) – If this service account has been logically deleted
  • deleted_time (int) – Time of deletion
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.iam.ServiceAccountList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.iam.TokenInspection(subject: str, projects: List[cognite.client.data_classes.iam.ProjectSpec], capabilities: List[Dict[KT, VT]])

Bases: cognite.client.data_classes._base.CogniteResponse

Current login status

Parameters:
  • subject (str) – Subject (sub claim) of JWT.
  • projects (List[ProjectSpec]) – Projects this token is valid for.
  • capabilities (List[Dict]) – Capabilities associated with this token.
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]

Extraction pipelines

List extraction pipelines

ExtractionPipelinesAPI.list(limit: int = 25) → cognite.client.data_classes.extractionpipelines.ExtractionPipelineList

List extraction pipelines

Parameters:limit (int, optional) – Maximum number of ExtractionPipelines to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:List of requested ExtractionPipelines
Return type:ExtractionPipelineList

Examples

List ExtractionPipelines:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> ep_list = c.extraction_pipelines.list(limit=5)

Create extraction pipeline

ExtractionPipelinesAPI.create(extractionPipeline: Union[cognite.client.data_classes.extractionpipelines.ExtractionPipeline, List[cognite.client.data_classes.extractionpipelines.ExtractionPipeline]]) → Union[cognite.client.data_classes.extractionpipelines.ExtractionPipeline, cognite.client.data_classes.extractionpipelines.ExtractionPipelineList]

`Create one or more extraction pipelines.`_

You can create an arbitrary number of extraction pipeline, and the SDK will split the request into multiple requests if necessary.

Parameters:extractionPipeline (Union[ExtractionPipeline, List[ExtractionPipeline]]) – Extraction pipeline or list of extraction pipelines to create.
Returns:Created extraction pipeline(s)
Return type:Union[ExtractionPipeline, ExtractionPipelineList]

Examples

Create new extraction pipeline:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ExtractionPipeline
>>> c = CogniteClient()
>>> extpipes = [ExtractionPipeline(name="extPipe1",...), ExtractionPipeline(name="extPipe2",...)]
>>> res = c.extraction_pipelines.create(extpipes)

Retrieve an extraction pipeline by ID

ExtractionPipelinesAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Optional[cognite.client.data_classes.extractionpipelines.ExtractionPipeline]

`Retrieve a single extraction pipeline by id.`_

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
Returns:

Requested extraction pipeline or None if it does not exist.

Return type:

Optional[ExtractionPipeline]

Examples

Get extraction pipeline by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.extraction_pipelines.retrieve(id=1)

Get extraction pipeline by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.extraction_pipelines.retrieve(external_id="1")

Retrieve multiple extraction pipelines by ID

ExtractionPipelinesAPI.retrieve_multiple(ids: Optional[List[int]] = None, external_ids: Optional[List[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.extractionpipelines.ExtractionPipelineList

`Retrieve multiple extraction pipelines by ids and external ids.`_

Parameters:
  • ids (List[int], optional) – IDs
  • external_ids (List[str], optional) – External IDs
  • ignore_unknown_ids (bool) – Ignore IDs and external IDs that are not found rather than throw an exception.
Returns:

The requested ExtractionPipelines.

Return type:

ExtractionPipelineList

Examples

Get ExtractionPipelines by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.extraction_pipelines.retrieve_multiple(ids=[1, 2, 3])

Get assets by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.extraction_pipelines.retrieve_multiple(external_ids=["abc", "def"], ignore_unknown_ids=True)

Update extraction pipelines

ExtractionPipelinesAPI.update(item: Union[cognite.client.data_classes.extractionpipelines.ExtractionPipeline, cognite.client.data_classes.extractionpipelines.ExtractionPipelineUpdate, List[Union[cognite.client.data_classes.extractionpipelines.ExtractionPipeline, cognite.client.data_classes.extractionpipelines.ExtractionPipelineUpdate]]]) → Union[cognite.client.data_classes.extractionpipelines.ExtractionPipeline, cognite.client.data_classes.extractionpipelines.ExtractionPipelineList]

`Update one or more extraction pipelines`_

Parameters:item (Union[ExtractionPipeline, ExtractionPipelineUpdate, List[Union[ExtractionPipeline, ExtractionPipelineUpdate]]]) – Extraction pipeline(s) to update
Returns:Updated extraction pipeline(s)
Return type:Union[ExtractionPipeline, ExtractionPipelineList]

Examples

Update an extraction pipeline that you have fetched. This will perform a full update of the extraction pipeline:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> update = ExtractionPipelineUpdate(id=1)
>>> update.description.set("Another new extpipe")
>>> res = c.extraction_pipelines.update(update)

Delete extraction pipelines

ExtractionPipelinesAPI.delete(id: Union[int, List[int]] = None, external_id: Union[str, List[str]] = None) → None

`Delete one or more extraction pipelines`_

Parameters:
  • id (Union[int, List[int]) – Id or list of ids
  • external_id (Union[str, List[str]]) – External ID or list of external ids
Returns:

None

Examples

Delete extraction pipelines by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.extraction_pipelines.delete(id=[1,2,3], external_id="3")

Extraction pipeline runs

List runs for an extraction pipeline

ExtractionPipelineRunsAPI.list(external_id: str, statuses: List[str] = None, message_substring: str = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, limit: int = 25) → cognite.client.data_classes.extractionpipelines.ExtractionPipelineRunList

`List runs for an extraction pipeline with given external_id <>`_

Parameters:
  • external_id (str) – Extraction pipeline external Id.
  • statuses (List[str]) – One or more among “success” / “failure” / “seen”.
  • message_substring (str) – Failure message part.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • limit (int, optional) – Maximum number of ExtractionPipelines to return. Defaults to 25. Set to -1, float(“inf”) or None to return all items.
Returns:

List of requested extraction pipeline runs

Return type:

ExtractionPipelineRunList

Examples

List extraction pipeline runs:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> runsList = c.extraction_pipeline_runs.list(external_id="test ext id", limit=5)

Filter extraction pipeline runs on a given status:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> runsList = c.extraction_pipeline_runs.list(external_id="test ext id", statuses=["seen"], statuslimit=5)

Report new runs

ExtractionPipelineRunsAPI.create(run: Union[cognite.client.data_classes.extractionpipelines.ExtractionPipelineRun, List[cognite.client.data_classes.extractionpipelines.ExtractionPipelineRun]]) → Union[cognite.client.data_classes.extractionpipelines.ExtractionPipelineRun, cognite.client.data_classes.extractionpipelines.ExtractionPipelineRunList]

`Create one or more extraction pipeline runs.`_

You can create an arbitrary number of extraction pipeline runs, and the SDK will split the request into multiple requests.

Parameters:run (Union[ExtractionPipelineRun, List[ExtractionPipelineRun]]) – Extraction pipeline or list of extraction pipeline runs to create.
Returns:Created extraction pipeline run(s)
Return type:Union[ExtractionPipelineRun, ExtractionPipelineRunList]

Examples

Report a new extraction pipeline run:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import ExtractionPipelineRun
>>> c = CogniteClient()
>>> res = c.extraction_pipeline_runs.create(ExtractionPipelineRun(status="success", external_id="extId"))

Data classes

class cognite.client.data_classes.extractionpipelines.ExtractionPipeline(id: int = None, external_id: str = None, name: str = None, description: str = None, data_set_id: int = None, raw_tables: List[Dict[str, str]] = None, last_success: int = None, last_failure: int = None, last_message: str = None, last_seen: int = None, schedule: str = None, contacts: List[cognite.client.data_classes.extractionpipelines.ExtractionPipelineContact] = None, metadata: Dict[str, str] = None, source: str = None, documentation: str = None, created_time: int = None, last_updated_time: int = None, created_by: str = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

An extraction pipeline is a representation of a process writing data to CDF, such as an extractor or an ETL tool.

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
  • name (str) – The name of the extraction pipepeline.
  • description (str) – The description of the extraction pipepeline.
  • data_set_id (int) – The id of the dataset this extraction pipepeline related with.
  • raw_tables (List[Dict[str, str]) – list of raw tables in list format: [{“dbName”: “value”, “tableName” : “value”}].
  • last_success (int) – Milliseconds value of last success status.
  • last_failure (int) – Milliseconds value of last failure status.
  • last_message (str) – Message of last failure.
  • last_seen (int) – Milliseconds value of last seen status.
  • schedule (str) – None/On trigger/Continuous/cron regex.
  • contacts (List[ExtractionPipelineContact]) – list of contacts
  • metadata (Dict[str, str]) – Custom, application specific metadata. String key -> String value. Limits: Maximum length of key is 128 bytes, value 10240 bytes, up to 256 key-value pairs, of total size at most 10240.
  • source (str) – Source text value for extraction pipepeline.
  • documentation (str) – Documentation text value for extraction pipepeline.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • last_updated_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • created_by (str) – Extraction pipepeline creator, usually an email.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.extractionpipelines.ExtractionPipelineContact(name: str, email: str, role: str, send_notification: bool)

Bases: dict

A contact for an extraction pipeline

Parameters:
  • name (str) – Name of contact
  • email (str) – Email address of contact
  • role (str) – Role of contact, such as Owner, Maintainer, etc.
  • send_notification (bool) – Whether to send notifications to this contact or not
class cognite.client.data_classes.extractionpipelines.ExtractionPipelineList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.extractionpipelines.ExtractionPipelineRun(external_id: str = None, status: str = None, message: str = None, created_time: int = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of an extraction pipeline run.

Parameters:
  • external_id (str) – The external ID of the extraction pipeline.
  • status (str) – success/failure/seen.
  • message (str) – Optional status message.
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.extractionpipelines.ExtractionPipelineRunFilter(external_id: str = None, statuses: List[str] = None, message: cognite.client.data_classes.extractionpipelines.StringFilter = None, created_time: Union[Dict[str, Any], cognite.client.data_classes.shared.TimestampRange] = None, cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter runs with exact matching

Parameters:
  • external_id (str) – The external ID of related ExtractionPipeline provided by the client. Must be unique for the resource type.
  • statuses (List[str]) – success/failure/seen.
  • message (StringFilter) – message filter.
  • created_time (Union[Dict[str, Any], TimestampRange]) – Range between two timestamps.
  • cognite_client (CogniteClient) – The client to associate with this object.
class cognite.client.data_classes.extractionpipelines.ExtractionPipelineRunList(resources: List[Any], cognite_client=None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.extractionpipelines.ExtractionPipelineUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

Changes applied to an extraction pipeline

Parameters:
  • id (int) – A server-generated ID for the object.
  • external_id (str) – The external ID provided by the client. Must be unique for the resource type.
class cognite.client.data_classes.extractionpipelines.StringFilter(substring: str = None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter runs on substrings of the message

Parameters:substring (str) – Part of message

Base data classes

CogniteResource

class cognite.client.data_classes._base.CogniteResource
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]
to_pandas(expand: List[str] = ('metadata', ), ignore: List[str] = None, camel_case: bool = True)

Convert the instance into a pandas DataFrame.

Parameters:
  • expand (List[str]) – List of row keys to expand, only works if the value is a Dict. Will expand metadata by default.
  • ignore (List[str]) – List of row keys to not include when converting to a data frame.
  • camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns:

The dataframe.

Return type:

pandas.DataFrame

CogniteResourceList

class cognite.client.data_classes._base.CogniteResourceList(resources: List[Any], cognite_client=None)
dump(camel_case: bool = False) → List[Dict[str, Any]]

Dump the instance into a json serializable Python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A list of dicts representing the instance.
Return type:List[Dict[str, Any]]
get(id: int = None, external_id: str = None) → Optional[cognite.client.data_classes._base.CogniteResource]

Get an item from this list by id or exernal_id.

Parameters:
  • id (int) – The id of the item to get.
  • external_id (str) – The external_id of the item to get.
Returns:

The requested item

Return type:

Optional[CogniteResource]

to_pandas(camel_case=True) → pandas.DataFrame

Convert the instance into a pandas DataFrame.

Returns:The dataframe.
Return type:pandas.DataFrame

CogniteResponse

class cognite.client.data_classes._base.CogniteResponse
dump(camel_case: bool = False) → Dict[str, Any]

Dump the instance into a json serializable python data type.

Parameters:camel_case (bool) – Use camelCase for attribute names. Defaults to False.
Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]

CogniteFilter

class cognite.client.data_classes._base.CogniteFilter
dump(camel_case: bool = False)

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]

CogniteUpdate

class cognite.client.data_classes._base.CogniteUpdate(id: int = None, external_id: str = None)
dump()

Dump the instance into a json serializable Python data type.

Returns:A dictionary representation of the instance.
Return type:Dict[str, Any]

Exceptions

CogniteAPIError

exception cognite.client.exceptions.CogniteAPIError(message: str, code: int = None, x_request_id: str = None, missing: List[T] = None, duplicated: List[T] = None, successful: List[T] = None, failed: List[T] = None, unknown: List[T] = None, unwrap_fn: Callable = None, extra: Dict[KT, VT] = None)

Cognite API Error

Raised if a given request fails. If one or more of concurrent requests fails, this exception will also contain information about which items were successfully processed (2xx), which may have been processed (5xx), and which have failed to be processed (4xx).

Parameters:
  • message (str) – The error message produced by the API
  • code (int) – The error code produced by the failure
  • x_request_id (str) – The request-id generated for the failed request.
  • extra (Dict) – A dict of any additional information.
  • successful (List) – List of items which were successfully proccessed.
  • failed (List) – List of items which failed.
  • unknown (List) – List of items which may or may not have been successfully processed.

Examples

Catching an API-error and handling it based on the error code:

from cognite.client import CogniteClient
from cognite.client.exceptions import CogniteAPIError

c = CogniteClient()

try:
    c.login.status()
except CogniteAPIError as e:
    if e.code == 401:
        print("You are not authorized")
    elif e.code == 400:
        print("Something is wrong with your request")
    elif e.code == 500:
        print("Something went terribly wrong. Here is the request-id: {}".format(e.x_request_id)
    print("The message returned from the API: {}".format(e.message))

CogniteNotFoundError

exception cognite.client.exceptions.CogniteNotFoundError(not_found: List[T], successful: List[T] = None, failed: List[T] = None, unknown: List[T] = None, unwrap_fn: Callable = None)

Cognite Not Found Error

Raised if one or more of the referenced ids/external ids are not found.

Parameters:
  • not_found (List) – The ids not found.
  • successful (List) – List of items which were successfully proccessed.
  • failed (List) – List of items which failed.
  • unknown (List) – List of items which may or may not have been successfully processed.

CogniteDuplicatedError

exception cognite.client.exceptions.CogniteDuplicatedError(duplicated: List[T], successful: List[T] = None, failed: List[T] = None, unknown: List[T] = None, unwrap_fn: Callable = None)

Cognite Duplicated Error

Raised if one or more of the referenced ids/external ids have been duplicated in the request.

Parameters:
  • duplicated (list) – The duplicated ids.
  • successful (List) – List of items which were successfully proccessed.
  • failed (List) – List of items which failed.
  • unknown (List) – List of items which may or may not have been successfully processed.

CogniteAPIKeyError

exception cognite.client.exceptions.CogniteAPIKeyError

Cognite API Key Error.

Raised if the API key is missing or invalid.

CogniteImportError

exception cognite.client.exceptions.CogniteImportError(module: str, message: str = None)

Cognite Import Error

Raised if the user attempts to use functionality which requires an uninstalled package.

Parameters:
  • module (str) – Name of the module which could not be imported
  • message (str) – The error message to output.

CogniteMissingClientError

exception cognite.client.exceptions.CogniteMissingClientError

Cognite Missing Client Error

Raised if the user attempts to make use of a method which requires the cognite_client being set, but it is not.

CogniteDuplicateColumnsError

exception cognite.client.exceptions.CogniteDuplicateColumnsError(dups)

Cognite Duplicate Columns Error

Raised if the user attempts to create a dataframe through include_aggregate_names=False which results in duplicate column names.

Utils

Convert timestamp to milliseconds since epoch

cognite.client.utils.timestamp_to_ms(timestamp: Union[int, float, str, datetime.datetime]) → int

Returns the ms representation of some timestamp given by milliseconds, time-ago format or datetime object

Parameters:timestamp (Union[int, float, str, datetime]) – Convert this timestamp to ms.
Returns:Milliseconds since epoch representation of timestamp
Return type:int

Convert milliseconds since epoch to datetime

cognite.client.utils.ms_to_datetime(ms: Union[int, float]) → datetime.datetime

Converts milliseconds since epoch to datetime object.

Parameters:ms (Union[int, float]) – Milliseconds since epoch
Returns:Datetime object.
Return type:datetime

Testing

Object to use as a mock for CogniteClient

class cognite.client.testing.CogniteClientMock(*args, **kwargs)

Mock for CogniteClient object

All APIs are replaced with specced MagicMock objects.

Use a context manager to monkeypatch CogniteClient

cognite.client.testing.monkeypatch_cognite_client()

Context manager for monkeypatching the CogniteClient.

Will patch all clients and replace them with specced MagicMock objects.

Yields:CogniteClientMock – The mock with which the CogniteClient has been replaced

Examples

In this example we can run the following code without actually executing the underlying API calls:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TimeSeries
>>> from cognite.client.testing import monkeypatch_cognite_client
>>>
>>> with monkeypatch_cognite_client():
>>>     c = CogniteClient()
>>>     c.time_series.create(TimeSeries(external_id="blabla"))

This example shows how to set the return value of a given method:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import TimeSeries
>>> from cognite.client.data_classes import LoginStatus
>>> from cognite.client.testing import monkeypatch_cognite_client
>>>
>>> with monkeypatch_cognite_client() as c_mock:
>>>     c_mock.login.status.return_value = LoginStatus(
>>>         user="user", project="dummy", project_id=1, logged_in=True, api_key_id=1
>>>     )
>>>     c = CogniteClient()
>>>     res = c.login.status()
>>>     assert "user" == res.user

Here you can see how to have a given method raise an exception:

>>> from cognite.client import CogniteClient
>>> from cognite.client.exceptions import CogniteAPIError
>>> from cognite.client.testing import monkeypatch_cognite_client
>>>
>>> with monkeypatch_cognite_client() as c_mock:
>>>     c_mock.login.status.side_effect = CogniteAPIError(message="Something went wrong", code=400)
>>>     c = CogniteClient()
>>>     try:
>>>         res = c.login.status()
>>>     except CogniteAPIError as e:
>>>         assert 400 == e.code
>>>         assert "Something went wrong" == e.message

Experimental features

Warning

These features are subject to breaking changes and should not be used in production code.