Quickstart

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 a user-defined string intended to give the client a unique identifier. You can provide the client_name by passing it directly to the ClientConfig constructor.

You may set a default client configuration which will be used if no config is passed to CogniteClient. All examples in this documentation assume that a default configuration has been set.

>>> from cognite.client import CogniteClient, ClientConfig, global_config
>>> from cognite.client.credentials import APIKey
>>> cnf = ClientConfig(client_name="my-special-client", base_url="https://<cluster>.cognitedata.com", project="my-project", credentials=APIKey("very-secret"))
>>> global_config.default_client_config = cnf
>>> c = CogniteClient()
>>> status = c.login.status()

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

Authenticate

The preferred way to authenticating against the Cognite API is using OpenID Connect (OIDC). To enable this, use one of the credential providers such as OAuthClientCredentials:

>>> from cognite.client import CogniteClient, ClientConfig
>>> from cognite.client.credentials import OAuthClientCredentials
>>>
>>> creds = OAuthClientCredentials(token_url=..., client_id=..., client_secret=..., scopes=[...])
>>> cnf = ClientConfig(client_name="my-special-client", base_url="https://<cluster>.cognitedata.com", project="my-project", credentials=creds)
>>> c = CogniteClient(cnf)

Examples for all OAuth credential providers can be found in the Credential Providers section.

You can also make your own credential provider:

>>> from cognite.client import CogniteClient, ClientConfig
>>> from cognite.client.credentials import Token
>>> def token_provider():
>>>     ...
>>>
>>> cnf = ClientConfig(client_name="my-special-client", base_url="https://<cluster>.cognitedata.com", project="my-project", credentials=Token(token_provider))
>>> c = CogniteClient(cnf)

If OIDC has not been enabled for your CDF project, you will want to authenticate using an API key.

>>> from cognite.client import CogniteClient, ClientConfig
>>> from cognite.client.credentials import APIKey
>>> cnf = ClientConfig(client_name="<your-client-name>", base_url="https://<cluster>.cognitedata.com", project="my-project", credentials=APIKey("very-secret"))
>>> c = CogniteClient(cnf)

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()

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.

global configuration

You can set global configuration options like this:

from cognite.client import global_config, ClientConfig
from cognite.client.credentials import APIKey
global_config.default_client_config = ClientConfig(
    client_name="my-client", project="myproj", credentials=APIKey("verysecret")
)
global_config.disable_pypi_version_check = True
global_config.disable_gzip = False
global_config.disable_ssl = False
global_config.max_retries = 10
global_config.max_retry_backoff = 10
global_config.max_connection_pool_size = 10
global_config.status_forcelist = {429, 502, 503}

These must be set prior to instantiating a CogniteClient in order for them to take effect.

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 max_workers config option.

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 max_connection_pool_size config option.

Extensions and optional dependencies

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.

How to install extra dependencies

If your application requires the functionality from e.g. the pandas, sympy, or geopandas dependencies, you should install the SDK along with its optional dependencies. The available extras are:

  • numpy: numpy
  • pandas: pandas
  • geo: geopanda, shapely
  • sympy: sympy
  • functions: pip
  • all (will install dependencies for all the above)

These can be installed with the following command:

pip

$ pip install cognite-sdk[pandas, geo]

poetry

$ poetry add cognite-sdk -E pandas -E geo

API

CogniteClient

class cognite.client.CogniteClient(config: Optional[cognite.client.config.ClientConfig] = None)

Main entrypoint into Cognite Python SDK.

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

Parameters:config (ClientConfig) – The configuration for this client.
get(url: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, Any]] = None) → requests.models.Response

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

post(url: str, json: Dict[str, Any], params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, Any]] = None) → requests.models.Response

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

put(url: str, json: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, Any]] = None) → requests.models.Response

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

delete(url: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, Any]] = None) → requests.models.Response

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
class cognite.client.config.ClientConfig(client_name: str, project: str, credentials: cognite.client.credentials.CredentialProvider, api_subversion: 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, debug: bool = False)

Configuration object for the client

Parameters:
  • client_name (str) – A user-defined name for the client. Used to identify number of unique applications/scripts running on top of CDF.
  • project (str) – CDF Project name.
  • credentials (CredentialProvider) – Credentials. e.g. APIKey, Token, ClientCredentials.
  • api_subversion (str) – API subversion
  • 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.
  • debug (bool) – Configures logger to log extra request details to stderr.
class cognite.client.config.GlobalConfig

Global configuration object

default_client_config

A default instance of a client configuration. This will be used by the CogniteClient constructor if no config is passed directly. Defaults to None.

Type:Optional[ClientConfig]
disable_gzip

Whether or not to disable gzipping of json bodies. Defaults to False.

Type:bool
disable_pypi_version_check

Whether or not to check for newer SDK versions when instantiating a new client. Defaults to False.

Type:bool
status_forcelist

HTTP status codes to retry. Defaults to {429, 502, 503, 504}

Type:Set[int]
max_retries

Max number of retries on a given http request. Defaults to 10.

Type:int
max_retry_backoff

Retry strategy employs exponential backoff. This parameter sets a max on the amount of backoff after any request failure. Defaults to 30.

Type:int
max_connection_pool_size

The maximum number of connections which will be kept in the SDKs connection pool. Defaults to 50.

Type:int
disable_ssl

Whether or not to disable SSL. Defaults to False

Type:bool
proxies

Dictionary mapping from protocol to url. e.g. {“https”: “http://10.10.1.10:1080”}

Type:Dict[str, str]

Authentication

Credential Providers

class cognite.client.credentials.Token(token: Union[str, Callable[[], str]])

Token credential provider

Parameters:token (Union[str, Callable[[], str]) – A token or a token factory.

Examples

>>> from cognite.client.credentials import Token
>>> token_provider = Token("my secret token")
>>> token_factory_provider = Token(lambda: "my secret token")
class cognite.client.credentials.OAuthClientCredentials(token_url: str, client_id: str, client_secret: str, scopes: List[str], **token_custom_args)

OAuth credential provider for the “Client Credentials” flow.

Parameters:
  • token_url (str) – OAuth token url
  • client_id (str) – Your application’s client id.
  • client_secret (str) – Your application’s client secret
  • scopes (List[str]) – A list of scopes.
  • **token_custom_args (Any) – Optional additional arguments to pass as query parameters to the token fetch request.

Examples

>>> from cognite.client.credentials import OAuthClientCredentials
>>> import os
>>> oauth_provider = OAuthClientCredentials(
...     token_url="https://login.microsoftonline.com/xyz/oauth2/v2.0/token",
...     client_id="abcd",
...     client_secret=os.environ["OAUTH_CLIENT_SECRET"],
...     scopes=["https://greenfield.cognitedata.com/.default"],
...     # Any additional IDP-specific token args. e.g.
...     audience="some-audience"
... )
class cognite.client.credentials.OAuthInteractive(authority_url: str, client_id: str, scopes: List[str], redirect_port: int = 53000)

OAuth credential provider for an interactive login flow.

Make sure you have http://localhost:port in Redirect URI in App Registration as type “Mobile and desktop applications”.

Parameters:
  • authority_url (str) – OAuth authority url
  • client_id (str) – Your application’s client id.
  • scopes (List[str]) – A list of scopes.
  • redirect_port (List[str]) – Redirect port defaults to 53000.

Examples

>>> from cognite.client.credentials import OAuthInteractive
>>> oauth_provider = OAuthInteractive(
...     authority_url="https://login.microsoftonline.com/xyz",
...     client_id="abcd",
...     scopes=["https://greenfield.cognitedata.com/.default"],
... )
class cognite.client.credentials.OAuthDeviceCode(authority_url: str, client_id: str, scopes: List[str])

OAuth credential provider for the device code login flow.

Parameters:
  • authority_url (str) – OAuth authority url
  • client_id (str) – Your application’s client id.
  • scopes (List[str]) – A list of scopes.

Examples

>>> from cognite.client.credentials import OAuthInteractive
>>> oauth_provider = OAuthDeviceCode(
...     authority_url="https://login.microsoftonline.com/xyz",
...     client_id="abcd",
...     scopes=["https://greenfield.cognitedata.com/.default"],
... )

Get login status

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

Check login status

Note

This endpoint is only applicable if the client has been authenticated with an api key. If not, use client.iam.token.inspect instead

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() → pandas.DataFrame

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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.assets.AssetList

Retrieve multiple assets by id.

Parameters:
  • ids (Sequence[int], optional) – IDs
  • external_ids (Sequence[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: Sequence[int] = None, parent_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[int] = None, asset_subtree_external_ids: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[str] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, geo_location: cognite.client.data_classes.shared.GeoLocationFilter = 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: Sequence[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 (Sequence[int]) – Return only the direct descendants of the specified assets.
  • parent_external_ids (Sequence[str]) – Return only the direct descendants of the specified assets.
  • asset_subtree_ids (Sequence[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (Sequence[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (Sequence[int]) – Return only assets in the specified data sets with these ids.
  • data_set_external_ids (Sequence[str]) – Return only assets in the specified data sets with these external ids.
  • labels (LabelFilter) – Return only the assets matching the specified label filter.
  • geo_location (GeoLocationFilter) – Only include files matching the specified geographic relation.
  • 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 (Sequence[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, Sequence[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, Sequence[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(asset)

Create asset hierarchy

AssetsAPI.create_hierarchy(assets: Sequence[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 (Sequence[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, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None, recursive: bool = False, ignore_unknown_ids: bool = False) → None

Delete one or more assets

Parameters:
  • id (Union[int, Sequence[int]) – Id or list of ids
  • external_id (Union[str, Sequence[str]]) – External ID or list of external 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, Sequence[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, Sequence[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, dict]] = None, geo_location: cognite.client.data_classes.shared.GeoLocation = 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: CogniteClient = 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.
  • geo_location (GeoLocation) – The geographic metadata of the asset.
  • 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) → 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]
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: Sequence[str] = ('metadata', 'aggregates'), ignore: List[str] = None, camel_case: bool = False) → pandas.DataFrame

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: Sequence[int] = None, parent_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[Dict[str, Any]] = None, data_set_ids: Sequence[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, geo_location: cognite.client.data_classes.shared.GeoLocationFilter = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on assets with strict matching.

Parameters:
  • name (str) – The name of the asset.
  • parent_ids (Sequence[int]) – Return only the direct descendants of the specified assets.
  • parent_external_ids (Sequence[str]) – Return only the direct descendants of the specified assets.
  • asset_subtree_ids (Sequence[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 (Sequence[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.
  • geo_location (GeoLocationFilter) – Only include files matching the specified geographic relation.
  • cognite_client (CogniteClient) – The client to associate with this object.
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.assets.AssetList(resources: Collection[Any], cognite_client: CogniteClient = 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: Sequence[int] = None, data_set_external_ids: Sequence[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 (Sequence[int]) – return only labels in the data sets with these ids.
  • data_set_external_ids (Sequence[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, Sequence[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, Sequence[LabelDefinition]]) – label definition or a list of label definitions to create.
Returns:Created label definition(s)
Return type:Union[LabelDefinition, LabelDefinitionList]

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, Sequence[str]] = None) → None

Delete one or more label definitions

Parameters:external_id (Union[str, Sequence[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: CogniteClient = 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: CogniteClient = 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: Collection[Any], cognite_client: CogniteClient = 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: CogniteClient = None)

Bases: dict, cognite.client.data_classes._base.CogniteFilter

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"])
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]

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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.events.EventList

Retrieve multiple events by id.

Parameters:
  • ids (Sequence[int], optional) – IDs
  • external_ids (Sequence[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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[int] = None, asset_subtree_external_ids: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[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: Sequence[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 (Sequence[int]) – Asset IDs of related equipments that this event relates to.
  • asset_external_ids (Sequence[str]) – Asset External IDs of related equipment that this event relates to.
  • asset_subtree_ids (Sequence[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (Sequence[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (Sequence[int]) – Return only events in the specified data sets with these ids.
  • data_set_external_ids (Sequence[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 (Sequence[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: Sequence[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 (Sequence[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, Sequence[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, Sequence[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, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None, ignore_unknown_ids: bool = False) → None

Delete one or more events

Parameters:
  • id (Union[int, Sequence[int]) – Id or list of ids
  • external_id (Union[str, Sequence[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, Sequence[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, Sequence[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: Sequence[int] = None, source: str = None, id: int = None, last_updated_time: int = None, created_time: int = None, cognite_client: CogniteClient = 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 (Sequence[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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[Dict[str, Any]] = None, data_set_ids: Sequence[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: CogniteClient = 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 (Sequence[int]) – Asset IDs of equipment that this event relates to.
  • asset_external_ids (Sequence[str]) – Asset External IDs of equipment that this event relates to.
  • asset_subtree_ids (Sequence[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 (Sequence[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: Collection[Any], cognite_client: CogniteClient = 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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.data_sets.DataSetList

Retrieve multiple data sets by id.

Parameters:
  • ids (Sequence[int], optional) – IDs
  • external_ids (Sequence[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, Sequence[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, Sequence[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, Sequence[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, Sequence[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: CogniteClient = 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: CogniteClient = 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: Collection[Any], cognite_client: CogniteClient = 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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.files.FileMetadataList

Retrieve multiple file metadatas by id.

Parameters:
  • ids (Sequence[int], optional) – IDs
  • external_ids (Sequence[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 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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[int] = None, asset_subtree_external_ids: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[str] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, geo_location: cognite.client.data_classes.shared.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 (Sequence[int]) – Only include files that reference these specific asset IDs.
  • asset_subtree_external_ids (Sequence[str]) – Only include files that reference these specific asset external IDs.
  • asset_subtree_ids (Sequence[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 (Sequence[int]) – Return only files in the specified data sets with these ids.
  • data_set_external_ids (Sequence[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] = 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: Sequence[int] = None, source_created_time: int = None, source_modified_time: int = None, data_set_id: int = None, labels: Sequence[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.shared.GeoLocation = None, security_categories: Sequence[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 (Sequence[int]) – No description.
  • data_set_id (int) – ID of the data set.
  • labels (Sequence[Label]) – A list of the labels associated with this resource item.
  • geo_location (GeoLocation) – The geographic metadata of the file.
  • security_categories (Sequence[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: Sequence[int] = None, data_set_id: int = None, labels: Sequence[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.shared.GeoLocation = None, source_created_time: int = None, source_modified_time: int = None, security_categories: Sequence[int] = None, overwrite: bool = False) → cognite.client.data_classes.files.FileMetadata

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 (Sequence[int]) – No description.
  • data_set_id (int) – Id of the data set.
  • labels (Sequence[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 (Sequence[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])

Retrieve download urls

FilesAPI.retrieve_download_urls(id: Union[int, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None, extended_expiration: bool = False) → Dict[Union[int, str], str]

Get download links by id or external id

Parameters:
  • id (Union[int, Sequence[int]]) – Id or list of ids.
  • external_id (Union[str, Sequence[str]]) – External id or list of external ids.
  • extended_expiration (bool) – Extend expiration time of download url to 1 hour. Defaults to false.
Returns:

Dictionary containing download urls.

Return type:

Dict[Union[str, int], str]

Download files to disk

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

Download files by id or external id.

This method will stream all files to disk, never keeping more than 2MB in memory per worker. The files will be stored in the provided directory using the name retrieved from the file metadata in CDF.

Parameters:
  • directory (str) – Directory to download the file(s) to.
  • id (Union[int, Sequence[int]], optional) – Id or list of ids
  • external_id (Union[str, Sequence[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: Union[pathlib.Path, str], id: int = None, external_id: str = None) → 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, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None) → None

Delete files

Parameters:
  • id (Union[int, Sequence[int]]) – Id or list of ids
  • external_id (Union[str, Sequence[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, Sequence[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, Sequence[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: Sequence[int] = None, data_set_id: int = None, labels: Sequence[cognite.client.data_classes.labels.Label] = None, geo_location: cognite.client.data_classes.shared.GeoLocation = None, source_created_time: int = None, source_modified_time: int = None, security_categories: Sequence[int] = None, id: int = None, uploaded: bool = None, uploaded_time: int = None, created_time: int = None, last_updated_time: int = None, cognite_client: CogniteClient = 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 (Sequence[int]) – No description.
  • data_set_id (int) – The dataSet Id for the item.
  • labels (Sequence[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 (Sequence[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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, data_set_ids: Sequence[Dict[str, Any]] = None, labels: cognite.client.data_classes.labels.LabelFilter = None, geo_location: cognite.client.data_classes.shared.GeoLocationFilter = None, asset_subtree_ids: Sequence[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: CogniteClient = 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 (Sequence[int]) – Only include files that reference these specific asset IDs.
  • asset_external_ids (Sequence[str]) – Only include files that reference these specific asset external IDs.
  • data_set_ids (Sequence[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 (Sequence[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) → 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.files.FileMetadataList(resources: Collection[Any], cognite_client: CogniteClient = 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:

Functions

Create function

FunctionsAPI.create(name: str, folder: Optional[str] = None, file_id: Optional[int] = None, function_path: str = 'handler.py', function_handle: Optional[Callable] = None, external_id: Optional[str] = None, description: Optional[str] = '', owner: Optional[str] = '', api_key: Optional[str] = None, secrets: Optional[Dict[KT, VT]] = None, env_vars: Optional[Dict[KT, VT]] = None, cpu: Optional[numbers.Number] = None, memory: Optional[numbers.Number] = None, runtime: Optional[str] = None, metadata: Optional[Dict[KT, VT]] = None, index_url: Optional[str] = None, extra_index_urls: Optional[List[str]] = None) → cognite.client.data_classes.functions.Function

When creating a function, the source code can be specified in one of three ways:

  • Via the folder argument, which is the path to the folder where the source code is located. function_path must point to a python file in the folder within which a function named handle must be defined.
  • Via the file_id argument, which is the ID of a zip-file uploaded to the files API. function_path must point to a python file in the zipped folder within which a function named handle must be defined.
  • Via the function_handle argument, which is a reference to a function object, which must be named handle.

The function named handle is the entrypoint of the created function. Valid arguments to handle are data, client, secrets and function_call_info:

  • If the user calls the function with input data, this is passed through the data argument.
  • If the user gives an api_key when creating the function, a pre instantiated CogniteClient is passed through the client argument.
  • If the user gives one or more secrets when creating the function, these are passed through the secrets argument. The API key can be access through secrets[“apikey”].
  • Data about the function call can be accessed via the argument function_call_info, which is a dictionary with keys function_id and, if the call is scheduled, schedule_id and scheduled_time.
Parameters:
  • name (str) – The name of the function.
  • folder (str, optional) – Path to the folder where the function source code is located.
  • file_id (int, optional) – File ID of the code uploaded to the Files API.
  • function_path (str) – Relative path from the root folder to the file containing the handle function. Defaults to handler.py. Must be on POSIX path format.
  • function_handle (Callable, optional) – Reference to a function object, which must be named handle.
  • external_id (str, optional) – External id of the function.
  • description (str, optional) – Description of the function.
  • owner (str, optional) – Owner of this function. Typically used to know who created it.
  • api_key (str, optional) – API key that can be used inside the function to access data in CDF.
  • secrets (Dict[str, str]) – Additional secrets as key/value pairs. These can e.g. password to simulators or other data sources. Keys must be lowercase characters, numbers or dashes (-) and at most 15 characters. You can create at most 30 secrets, all keys must be unique, and cannot be apikey.
  • env_vars (Dict[str, str]) – Environment variables as key/value pairs. Keys can contain only letters, numbers or the underscore character. You can create at most 100 environment variables.
  • cpu (Number, optional) – Number of CPU cores per function. Allowed values are in the range [0.1, 0.6], and None translates to the API default which is 0.25 in GCP. The argument is unavailable in Azure.
  • memory (Number, optional) – Memory per function measured in GB. Allowed values are in the range [0.1, 2.5], and None translates to the API default which is 1 GB in GCP. The argument is unavailable in Azure.
  • runtime (str, optional) – The function runtime. Valid values are [“py37”, “py38”, “py39”, None], and None translates to the API default which currently is “py38”. The runtime “py38” resolves to the latest version of the Python 3.8 series.
  • metadata (Dict[str, str], optional) – Metadata for the function as key/value pairs. Key & values can be at most 32, 512 characters long respectively. You can have at the most 16 key-value pairs, with a maximum size of 512 bytes.
  • index_url (str, optional) – Index URL for Python Package Manager to use. Be aware of the intrinsic security implications of using the index_url option. More information can be found on official docs,
  • extra_index_urls (List[str], optional) –

    Extra Index URLs for Python Package Manager to use. Be aware of the intrinsic security implications of using the extra_index_urls option. More information can be found on official docs,

Returns:

The created function.

Return type:

Function

Examples

Create function with source code in folder:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> function = c.functions.create(name="myfunction", folder="path/to/code", function_path="path/to/function.py")

Create function with file_id from already uploaded source code:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> function = c.functions.create(name="myfunction", file_id=123, function_path="path/to/function.py")

Create function with predefined function object named handle:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> function = c.functions.create(name="myfunction", function_handle=handle)

Create function with predefined function object named handle with dependencies:

>>> from cognite.client import CogniteClient
>>>
>>> def handle(client, data):
>>>     """
>>>     [requirements]
>>>     numpy
>>>     [/requirements]
>>>     """
>>>     ...
>>>
>>> c = CogniteClient()
>>> function = c.functions.create(name="myfunction", function_handle=handle)

Note

When using a predefined function object, you can list dependencies between the tags [requirements] and [/requirements] in the function’s docstring. The dependencies will be parsed and validated in accordance with requirement format specified in PEP 508.

Delete function

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

Delete one or more functions.

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

None

Example

Delete functions by id or external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.functions.delete(id=[1,2,3], external_id="function3")

List functions

FunctionsAPI.list(name: str = None, owner: str = None, file_id: int = None, status: str = None, external_id_prefix: str = None, created_time: Union[Dict[str, int], cognite.client.data_classes.shared.TimestampRange] = None, limit: Optional[int] = 25) → cognite.client.data_classes.functions.FunctionList

List all functions.

Parameters:
  • name (str) – The name of the function.
  • owner (str) – Owner of the function.
  • file_id (int) – The file ID of the zip-file used to create the function.
  • status (str) – Status of the function. Possible values: [“Queued”, “Deploying”, “Ready”, “Failed”].
  • external_id_prefix (str) – External ID prefix to filter on.
  • 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.
  • limit (int) – Maximum number of functions to return. Pass in -1, float(‘inf’) or None to list all.
Returns:

List of functions

Return type:

FunctionList

Example

List functions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> functions_list = c.functions.list()

Retrieve function

FunctionsAPI.retrieve(id: Optional[int] = None, external_id: Optional[str] = None) → Union[cognite.client.data_classes.functions.FunctionList, cognite.client.data_classes.functions.Function, None]

Retrieve a single function by id.

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

Requested function or None if it does not exist.

Return type:

Optional[Function]

Examples

Get function by id:

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

Get function by external id:

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

Retrieve multiple functions

FunctionsAPI.retrieve_multiple(ids: Optional[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None) → Union[cognite.client.data_classes.functions.FunctionList, cognite.client.data_classes.functions.Function, None]

Retrieve multiple functions by id.

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

The requested functions.

Return type:

FunctionList

Examples

Get function by id:

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

Get functions by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.functions.retrieve_multiple(external_ids=["func1", "func2"])

Call function

FunctionsAPI.call(id: Optional[int] = None, external_id: Optional[str] = None, data: Optional[Dict[KT, VT]] = None, wait: bool = True) → cognite.client.data_classes.functions.FunctionCall

Call a function by its ID or external ID..

Parameters:
  • id (int, optional) – ID
  • external_id (str, optional) – External ID
  • data (Union[str, dict], optional) – Input data to the function (JSON serializable). This data is passed deserialized into the function through one of the arguments called data. WARNING: Secrets or other confidential information should not be passed via this argument. There is a dedicated secrets argument in FunctionsAPI.create() for this purpose.’
  • wait (bool) – Wait until the function call is finished. Defaults to True.
Returns:

A function call object.

Return type:

FunctionCall

Examples

Call a function by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> call = c.functions.call(id=1)

Call a function directly on the Function object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> func = c.functions.retrieve(id=1)
>>> call = func.call()

Function calls

List function calls

FunctionCallsAPI.list(function_id: Optional[int] = None, function_external_id: Optional[str] = None, status: Optional[str] = None, schedule_id: Optional[int] = None, start_time: Optional[Dict[str, int]] = None, end_time: Optional[Dict[str, int]] = None, limit: Optional[int] = 25) → cognite.client.data_classes.functions.FunctionCallList

List all calls associated with a specific function id. Either function_id or function_external_id must be specified.

Parameters:
  • function_id (int, optional) – ID of the function on which the calls were made.
  • function_external_id (str, optional) – External ID of the function on which the calls were made.
  • status (str, optional) – Status of the call. Possible values [“Running”, “Failed”, “Completed”, “Timeout”].
  • schedule_id (int, optional) – Schedule id from which the call belongs (if any).
  • start_time (Dict[str, int], optional) – Start time of the call. Possible keys are min and max, with values given as time stamps in ms.
  • end_time (Dict[str, int], optional) – End time of the call. Possible keys are min and max, with values given as time stamps in ms.
  • limit (int, optional) – Maximum number of function calls to list. Pass in -1, float(‘inf’) or None to list all Function Calls.
Returns:

List of function calls

Return type:

FunctionCallList

Examples

List function calls:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> calls = c.functions.calls.list(function_id=1)

List function calls directly on a function object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> func = c.functions.retrieve(id=1)
>>> calls = func.list_calls()

Retrieve function call

FunctionCallsAPI.retrieve(call_id: int, function_id: Optional[int] = None, function_external_id: Optional[str] = None) → Union[cognite.client.data_classes.functions.FunctionCallList, cognite.client.data_classes.functions.FunctionCall, None]

Retrieve a single function call by id.

Parameters:
  • call_id (int) – ID of the call.
  • function_id (int, optional) – ID of the function on which the call was made.
  • function_external_id (str, optional) – External ID of the function on which the call was made.
Returns:

Requested function call.

Return type:

Union[FunctionCallList, FunctionCall, None]

Examples

Retrieve single function call by id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> call = c.functions.calls.retrieve(call_id=2, function_id=1)

Retrieve function call directly on a function object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> func = c.functions.retrieve(id=1)
>>> call = func.retrieve_call(id=2)

Retrieve function call response

FunctionCallsAPI.get_response(call_id: int, function_id: Optional[int] = None, function_external_id: Optional[str] = None) → Dict[KT, VT]

Retrieve the response from a function call.

Parameters:
  • call_id (int) – ID of the call.
  • function_id (int, optional) – ID of the function on which the call was made.
  • function_external_id (str, optional) – External ID of the function on which the call was made.
Returns:

Response from the function call.

Examples

Retrieve function call response by call ID:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> response = c.functions.calls.get_response(call_id=2, function_id=1)

Retrieve function call response directly on a call object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> call = c.functions.calls.retrieve(call_id=2, function_id=1)
>>> response = call.get_response()

Retrieve function call logs

FunctionCallsAPI.get_logs(call_id: int, function_id: Optional[int] = None, function_external_id: Optional[str] = None) → cognite.client.data_classes.functions.FunctionCallLog

Retrieve logs for function call.

Parameters:
  • call_id (int) – ID of the call.
  • function_id (int, optional) – ID of the function on which the call was made.
  • function_external_id (str, optional) – External ID of the function on which the call was made.
Returns:

Log for the function call.

Return type:

FunctionCallLog

Examples

Retrieve function call logs by call ID:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> logs = c.functions.calls.get_logs(call_id=2, function_id=1)

Retrieve function call logs directly on a call object:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> call = c.functions.calls.retrieve(call_id=2, function_id=1)
>>> logs = call.get_logs()

Function schedules

List function schedules

FunctionSchedulesAPI.list(name: str = None, function_id: int = None, function_external_id: str = None, created_time: Union[Dict[str, int], cognite.client.data_classes.shared.TimestampRange] = None, cron_expression: str = None, limit: Optional[int] = 25) → cognite.client.data_classes.functions.FunctionSchedulesList

List all schedules associated with a specific project.

Parameters:
  • name (str) – Name of the function schedule.
  • function_id (int) – ID of the function the schedules are linked to.
  • function_external_id (str) – External ID of the function the schedules are linked to.
  • 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.
  • cron_expression (str) – Cron expression.
  • limit (int) – Maximum number of schedules to list. Pass in -1, float(‘inf’) or None to list all.
Returns:

List of function schedules

Return type:

FunctionSchedulesList

Examples

List function schedules:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> schedules = c.functions.schedules.list()

List schedules directly on a function object to get only schedules associated with this particular function:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> func = c.functions.retrieve(id=1)
>>> schedules = func.list_schedules(limit=None)

Create function schedule

FunctionSchedulesAPI.create(name: str, cron_expression: str, function_id: Optional[int] = None, function_external_id: Optional[str] = None, client_credentials: Optional[Dict[KT, VT]] = None, description: str = '', data: Optional[Dict[KT, VT]] = None) → cognite.client.data_classes.functions.FunctionSchedule

Create a schedule associated with a specific project.

Parameters:
  • name (str) – Name of the schedule.
  • function_id (optional, int) – Id of the function. This is required if the schedule is created with client_credentials.
  • function_external_id (optional, str) – External id of the function. This is deprecated and cannot be used together with client_credentials.
  • description (str) – Description of the schedule.
  • cron_expression (str) – Cron expression.
  • client_credentials – (optional, Dict): Dictionary containing client credentials: client_id client_secret
  • data (optional, Dict) – Data to be passed to the scheduled run. WARNING: Secrets or other confidential information should not be passed via this argument. There is a dedicated secrets argument in FunctionsAPI.create() for this purpose.
Returns:

Created function schedule.

Return type:

FunctionSchedule

Examples

Create function schedule:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> schedule = c.functions.schedules.create(
...     name= "My schedule",
...     function_id=123,
...     cron_expression="*/5 * * * *",
...     client_credentials={"client_id": "...", "client_secret": "..."},
...     description="This schedule does magic stuff."
... )

Delete function schedule

FunctionSchedulesAPI.delete(id: int) → None

Delete a schedule associated with a specific project.

Parameters:id (int) – Id of the schedule
Returns:None

Examples

Delete function schedule:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.functions.schedules.delete(id = 123)

Data classes

class cognite.client.data_classes.functions.Function(id: int = None, name: str = None, external_id: str = None, description: str = None, owner: str = None, status: str = None, file_id: int = None, function_path: str = None, created_time: int = None, api_key: str = None, secrets: Dict[KT, VT] = None, env_vars: Dict[KT, VT] = None, cpu: numbers.Number = None, memory: numbers.Number = None, runtime: str = None, runtime_version: str = None, metadata: Dict[KT, VT] = None, error: Dict[KT, VT] = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a Cognite Function.

Parameters:
  • id (int) – Id of the function.
  • name (str) – Name of the function.
  • external_id (str) – External id of the function.
  • description (str) – Description of the function.
  • owner (str) – Owner of the function.
  • status (str) – Status of the function.
  • file_id (int) – File id of the code represented by this object.
  • function_path (str) – Relative path from the root folder to the file containing the handle function. Defaults to handler.py. Must be on posix path format.
  • created_time (int) – Created time in UNIX.
  • api_key (str) – Api key attached to the function.
  • secrets (Dict[str, str]) – Secrets attached to the function ((key, value) pairs).
  • env_vars (Dict[str, str]) – User specified environment variables on the function ((key, value) pairs).
  • cpu (Number) – Number of CPU cores per function. Defaults to 0.25. Allowed values are in the range [0.1, 0.6].
  • memory (Number) – Memory per function measured in GB. Defaults to 1. Allowed values are in the range [0.1, 2.5].
  • runtime (str) – Runtime of the function. Allowed values are [“py37”, “py38”, “py39”]. The runtime “py38” resolves to the latest version of the Python 3.8 series. Will default to “py38” if not specified.
  • runtime_version (str) – The complete specification of the function runtime with major, minor and patch version numbers.
  • metadata (Dict[str, str) – Metadata associated with a function as a set of key:value pairs.
  • error (Dict[str, str]) – Dictionary with keys “message” and “trace”, which is populated if deployment fails.
  • cognite_client (CogniteClient) – An optional CogniteClient to associate with this data class.
call(data: Optional[Dict[KT, VT]] = None, wait: bool = True) → cognite.client.data_classes.functions.FunctionCall

Call this particular function.

Parameters:
  • data (Union[str, dict], optional) – Input data to the function (JSON serializable). This data is passed deserialized into the function through one of the arguments called data. WARNING: Secrets or other confidential information should not be passed via this argument. There is a dedicated secrets argument in FunctionsAPI.create() for this purpose.
  • wait (bool) – Wait until the function call is finished. Defaults to True.
Returns:

A function call object.

Return type:

FunctionCall

list_calls(status: Optional[str] = None, schedule_id: Optional[int] = None, start_time: Optional[Dict[str, int]] = None, end_time: Optional[Dict[str, int]] = None, limit: Optional[int] = 25) → cognite.client.data_classes.functions.FunctionCallList

List all calls to this function.

Parameters:
  • status (str, optional) – Status of the call. Possible values [“Running”, “Failed”, “Completed”, “Timeout”].
  • schedule_id (int, optional) – Schedule id from which the call belongs (if any).
  • start_time ([Dict[str, int], optional) – Start time of the call. Possible keys are min and max, with values given as time stamps in ms.
  • end_time (Dict[str, int], optional) – End time of the call. Possible keys are min and max, with values given as time stamps in ms.
  • limit (int, optional) – Maximum number of function calls to list. Pass in -1, float(‘inf’) or None to list all Function Calls.
Returns:

List of function calls

Return type:

FunctionCallList

list_schedules(limit: Optional[int] = 25) → cognite.client.data_classes.functions.FunctionSchedulesList

List all schedules associated with this function.

Parameters:limit (int) – Maximum number of schedules to list. Pass in -1, float(‘inf’) or None to list all.
Returns:List of function schedules
Return type:FunctionSchedulesList
retrieve_call(id: int) → cognite.client.data_classes.functions.FunctionCall

Retrieve call by id.

Parameters:id (int) – ID of the call.
Returns:Function call.
Return type:FunctionCall
update() → None

Update the function object. Can be useful to check for the latet status of the function (‘Queued’, ‘Deploying’, ‘Ready’ or ‘Failed’).

Returns:None
class cognite.client.data_classes.functions.FunctionCall(id: int = None, start_time: int = None, end_time: int = None, scheduled_time: int = None, status: str = None, schedule_id: int = None, error: dict = None, function_id: int = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a Cognite Function call.

Parameters:
  • id (int) – A server-generated ID for the object.
  • start_time (int) – Start time of the call, measured in number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • end_time (int) – End time of the call, measured in number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • scheduled_time (int) – Scheduled time of the call, measured in number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • status (str) – Status of the function call (“Running”, “Completed” or “Failed”).
  • schedule_id (int) – The schedule id belonging to the call.
  • error (dict) – Error from the function call. It contains an error message and the stack trace.
  • cognite_client (CogniteClient) – An optional CogniteClient to associate with this data class.
get_logs() → cognite.client.data_classes.functions.FunctionCallLog

Retrieve logs for this function call.

Returns:Log for the function call.
Return type:FunctionCallLog
get_response() → Dict[KT, VT]

Retrieve the response from this function call.

Returns:Response from the function call.
update() → None

Update the function call object. Can be useful if the call was made with wait=False.

Returns:None
class cognite.client.data_classes.functions.FunctionCallList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.functions.FunctionCallLog(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.functions.FunctionCallLogEntry(timestamp: int = None, message: str = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A log entry for a function call.

Parameters:
  • timestamp (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • message (str) – Single line from stdout / stderr.
class cognite.client.data_classes.functions.FunctionList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.functions.FunctionSchedule(id: int = None, name: str = None, function_id: str = None, function_external_id: str = None, description: str = None, created_time: int = None, cron_expression: str = None, session_id: int = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a Cognite Function Schedule.

Parameters:
  • id (int) – Id of the schedule.
  • name (str) – Name of the function schedule.
  • function_id (int) – Id of the function.
  • function_external_id (str) – External id of the function.
  • description (str) – Description of the function schedule.
  • cron_expression (str) – Cron expression
  • created_time (int) – The number of milliseconds since 00:00:00 Thursday, 1 January 1970, Coordinated Universal Time (UTC), minus leap seconds.
  • session_id (int) – ID of the session running with the schedule.
  • cognite_client (CogniteClient) – An optional CogniteClient to associate with this data class.
get_input_data() → dict

Retrieve the input data to the associated function.

Returns:Input data to the associated function. This data is passed deserialized into the function through the data argument.
class cognite.client.data_classes.functions.FunctionSchedulesList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.functions.FunctionsLimits(timeout_minutes: int, cpu_cores: Dict[str, float], memory_gb: Dict[str, float], runtimes: List[str], response_size_mb: Optional[int] = None)

Bases: cognite.client.data_classes._base.CogniteResponse

Service limits for the associated project.

Parameters:
  • timeout_minutes (int) – Timeout of each function call.
  • cpu_cores (Dict[str, float]) – The number of CPU cores per function exectuion (i.e. function call).
  • memory_gb (Dict[str, float]) – The amount of available memory in GB per function execution (i.e. function call).
  • runtimes (List[str]) – Available runtimes. For example, “py37” translates to the latest version of the Python 3.7.x series.
  • response_size_mb (Optional[int]) – Maximum response size of function calls.
class cognite.client.data_classes.functions.FunctionsStatus(status: str)

Bases: cognite.client.data_classes._base.CogniteResponse

Activation Status for the associated project.

Parameters:status (str) – Activation Status for the associated project.

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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.time_series.TimeSeriesList

Retrieve multiple time series by id.

Parameters:
  • ids (Sequence[int], optional) – IDs
  • external_ids (Sequence[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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[int] = None, asset_subtree_external_ids: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[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) → 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 (Sequence[int], optional) – List time series related to these assets.
  • asset_external_ids (Sequence[str], optional) – List time series related to these assets.
  • asset_subtree_ids (Sequence[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (Sequence[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (Sequence[int]) – Return only assets in the specified data sets with these ids.
  • data_set_external_ids (Sequence[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.
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, Sequence[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, Sequence[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, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None, ignore_unknown_ids: bool = False) → None

Delete one or more time series.

Parameters:
  • id (Union[int, Sequence[int]) – Id or list of ids
  • external_id (Union[str, Sequence[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, Sequence[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, Sequence[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: Sequence[int] = None, data_set_id: int = None, created_time: int = None, last_updated_time: int = None, legacy_name: str = None, cognite_client: CogniteClient = 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 (Sequence[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
Raises:ValueError – If the time series is string as count aggregate is only supported for numeric data
Returns:The total number of datapoints
Return type:int
first() → Optional[Datapoint]

Returns the first datapoint in this time series. If empty, returns None.

Returns:A datapoint object containing the value and timestamp of the first datapoint.
Return type:Datapoint
latest(before: Union[int, str, datetime.datetime] = None) → Optional[Datapoint]

Returns the latest datapoint in this time series. If empty, returns None.

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: Sequence[int] = None, asset_external_ids: Sequence[str] = None, asset_subtree_ids: Sequence[Dict[str, Any]] = None, data_set_ids: Sequence[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: CogniteClient = 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 (Sequence[int]) – Only include time series that reference these specific asset IDs.
  • asset_external_ids (Sequence[str]) – Asset External IDs of related equipment that this time series relates to.
  • asset_subtree_ids (Sequence[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 (Sequence[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: Collection[Any], cognite_client: CogniteClient = 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, Sequence[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", Sequence[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.time_series.data.synthetic.query(expressions="TS{id:123} + TS{externalId:'abc'}", start="2w-ago", end="now")

Use variables to re-use an expression:

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

Use sympy to build complex expressions:

>>> from sympy import symbols, cos, sin
>>> a = symbols('a')
>>> dps = c.time_series.data.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(*, id: Union[None, int, Dict[str, Any], Sequence[Union[int, Dict[str, Any]]]] = None, external_id: Union[None, str, Dict[str, Any], Sequence[Union[str, Dict[str, Any]]]] = None, start: Union[int, str, datetime.datetime, None] = None, end: Union[int, str, datetime.datetime, None] = None, aggregates: Union[str, List[str], None] = None, granularity: Optional[str] = None, limit: Optional[int] = None, include_outside_points: bool = False, ignore_unknown_ids: bool = False) → Union[None, cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsList]

Retrieve datapoints for one or more time series.

Performance guide:
In order to retrieve millions of datapoints as efficiently as possible, here are a few guidelines: 1. For best speed, and significantly lower memory usage, consider using retrieve_arrays(…) which uses numpy.ndarrays for data storage. 2. Unlimited queries are fastest as they are trivial to parallelize. Thus, specifying a very large finite limit, e.g. 1 million, comes with a performance penalty. 3. Try to avoid specifying start and end to be very far from the actual data: If you have data from 2000 to 2015, don’t set start=0 (1970).
Parameters:
  • start (Union[int, str, datetime, None]) – Inclusive start. Default: 1970-01-01 UTC.
  • end (Union[int, str, datetime, None]) – Exclusive end. Default: “now”
  • id (Union[None, int, Dict[str, Any], List[Union[int, Dict[str, Any]]]]) – Id, dict (with id) or (mixed) sequence of these. See examples below.
  • external_id (Union[None, str, Dict[str, Any], List[Union[str, Dict[str, Any]]]]) – External id, dict (with external id) or (mixed) sequence of these. See examples below.
  • aggregates (Union[str, List[str], None]) – Single aggregate or list of aggregates to retrieve. Default: None (raw datapoints returned)
  • granularity (str) – The granularity to fetch aggregates at. e.g. ’15s’, ‘2h’, ‘10d’. Default: None.
  • limit (int) – Maximum number of datapoints to return for each time series. Default: None (no limit)
  • include_outside_points (bool) – Whether or not to include outside points. Not allowed when fetching aggregates. Default: False
  • ignore_unknown_ids (bool) – Whether or not to ignore missing time series rather than raising an exception. Default: False
Returns:

A Datapoints object containing the requested data, or a DatapointsList if multiple time series were asked for (the ordering is ids first, then external_ids). If ignore_unknown_ids is True, a single time series is requested and it is not found, the function will return None.

Return type:

Union[None, Datapoints, DatapointsList]

Examples

You can specify the identifiers 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=42 from 2 weeks ago up until now:

>>> from cognite.client import CogniteClient
>>> client = CogniteClient()
>>> dps = client.time_series.data.retrieve(id=42, start="2w-ago")

You can also get aggregated values, such as max or average. You may also fetch more than one time series simultaneously. Here we are getting daily averages and maximum values for all of 2018, for two different time series, where we’re specifying start and end as integers (milliseconds after epoch). Note that we are fetching them using their external ids:

>>> dps_lst = client.time_series.data.retrieve(
...    external_id=["foo", "bar"],
...    start=1514764800000,
...    end=1546300800000,
...    aggregates=["max", "average"],
...    granularity="1d")

In the two code examples above, we have a dps object (an instance of Datapoints), and a dps_lst object (an instance of DatapointsList). On dps, which in this case contains raw datapoints, you may access the underlying data directly by using the .value attribute. This works for both numeric and string (raw) datapoints, but not aggregates - they must be accessed by their respective names, because you’re allowed to fetch up to 10 aggregates simultaneously, and they are stored on the same object:

>>> raw_data = dps.value
>>> first_dps = dps_lst[0]  # optionally: `dps_lst.get(external_id="foo")`
>>> avg_data = first_dps.average
>>> max_data = first_dps.max

You may also slice a Datapoints object (you get Datapoints back), or ask for “a row of data” at a single index in same way you would do with a built-in list (you get a Datapoint object back, note the singular name). You’ll also get Datapoint objects when iterating through a Datapoints object, but this should generally be avoided (consider this a performance warning):

>>> dps_slice = dps[-10:]  # Last ten values
>>> dp = dps[3]  # The third value
>>> for dp in dps_slice:
...     pass  # do something!

All parameters can be individually set if you pass (one or more) dictionaries (even ignore_unknown_ids, contrary to the API). If you also pass top-level parameters, these will be overruled by the individual parameters (where both exist). You are free to mix any kind of ids and external ids: Single identifiers, single dictionaries and (mixed) lists of these.

Let’s say you want different aggregates and end-times for a few time series (when only fetching a single aggregate, you may pass the string directly for convenience):

>>> dps_lst = client.time_series.data.retrieve(
...     id=[
...         {"id": 42, "end": "1d-ago", "aggregates": "average"},
...         {"id": 69, "end": "2d-ago", "aggregates": ["average"]},
...         {"id": 96, "end": "3d-ago", "aggregates": ["min", "max", "count"]},
...     ],
...     external_id={"external_id": "foo", "aggregates": "max"},
...     start="5d-ago",
...     granularity="1h")

When requesting multiple time series, an easy way to get the datapoints of a specific one is to use the .get method on the returned DatapointsList object, then specify if you want id or external_id. Note: If you fetch a time series by using id, you can still access it with its external_id (and the opposite way around), if you know it:

>>> from datetime import datetime, timezone
>>> utc = timezone.utc
>>> dps_lst = client.time_series.data.retrieve(
...     start=datetime(1907, 10, 14, tzinfo=utc),
...     end=datetime(1907, 11, 6, tzinfo=utc),
...     id=[42, 43, 44, ..., 499, 500],
... )
>>> ts_350 = dps_lst.get(id=350)  # `Datapoints` object

…but what happens if you request some duplicate ids or external_ids? In this example we will show how to get data from multiple disconnected periods. Let’s say you’re tasked to train a machine learning model to recognize a specific failure mode of a system, and you want the training data to only be from certain periods (when an alarm was on/high). Assuming these alarms are stored as events in CDF, with both start- and end times, we can use these directly in the query.

After fetching, the .get method will return a list of Datapoints instead, (assuming we have more than one event) in the same order, similar to how slicing works with non-unique indices on Pandas DataFrames:

>>> periods = client.events.list(type="alarm", subtype="pressure")
>>> sensor_xid = "foo-pressure-bar"
>>> dps_lst = client.time_series.data.retrieve(
...     id=[42, 43, 44],
...     external_id=[
...         {"external_id": sensor_xid, "start": ev.start_time, "end": ev.end_time}
...         for ev in periods
...     ])
>>> ts_44 = dps_lst.get(id=44)  # Single `Datapoints` object
>>> ts_lst = dps_lst.get(external_id=sensor_xid)  # List of `len(periods)` `Datapoints` objects

The API has an endpoint to “retrieve latest (before)”, but not “after”. Luckily, we can emulate that behaviour easily. Let’s say we have a very dense time series and do not want to fetch all of the available raw data (or fetch less precise aggregate data), just to get the very first datapoint of every month (from e.g. the year 2000 through 2010):

>>> import itertools
>>> month_starts = [
...     datetime(year, month, 1, tzinfo=utc)
...     for year, month in itertools.product(range(2000, 2011), range(1, 13))]
>>> dps_lst = client.time_series.data.retrieve(
...     external_id=[{"external_id": "foo", "start": start} for start in month_starts],
...     limit=1)

The last example here is just to showcase the great flexibility of the retrieve endpoint, with a very custom query:

>>> ts1 = 1337
>>> ts2 = {
...     "id": 42,
...     "start": -12345,  # Overrides `start` arg. below
...     "end": "1h-ago",
...     "limit": 1000,  # Overrides `limit` arg. below
...     "include_outside_points": True,
... }
>>> ts3 = {
...     "id": 11,
...     "end": "1h-ago",
...     "aggregates": ["max"],
...     "granularity": "42h",
...     "include_outside_points": False,
...     "ignore_unknown_ids": True,  # Overrides `ignore_unknown_ids` arg. below
... }
>>> dps_lst = client.time_series.data.retrieve(
...    id=[ts1, ts2, ts3], start="2w-ago", limit=None, ignore_unknown_ids=False)

Retrieve datapoints as numpy arrays

DatapointsAPI.retrieve_arrays(*, id: Union[None, int, Dict[str, Any], Sequence[Union[int, Dict[str, Any]]]] = None, external_id: Union[None, str, Dict[str, Any], Sequence[Union[str, Dict[str, Any]]]] = None, start: Union[int, str, datetime.datetime, None] = None, end: Union[int, str, datetime.datetime, None] = None, aggregates: Union[str, List[str], None] = None, granularity: Optional[str] = None, limit: Optional[int] = None, include_outside_points: bool = False, ignore_unknown_ids: bool = False) → Union[None, cognite.client.data_classes.datapoints.DatapointsArray, cognite.client.data_classes.datapoints.DatapointsArrayList]

Retrieve datapoints for one or more time series.

Note: This method requires numpy to be installed.

Parameters:
  • start (Union[int, str, datetime, None]) – Inclusive start. Default: 1970-01-01 UTC.
  • end (Union[int, str, datetime, None]) – Exclusive end. Default: “now”
  • id (Union[None, int, Dict[str, Any], List[Union[int, Dict[str, Any]]]]) – Id, dict (with id) or (mixed) sequence of these. See examples below.
  • external_id (Union[None, str, Dict[str, Any], List[Union[str, Dict[str, Any]]]]) – External id, dict (with external id) or (mixed) sequence of these. See examples below.
  • aggregates (Union[str, List[str], None]) – Single aggregate or list of aggregates to retrieve. Default: None (raw datapoints returned)
  • granularity (str) – The granularity to fetch aggregates at. e.g. ’15s’, ‘2h’, ‘10d’. Default: None.
  • limit (int) – Maximum number of datapoints to return for each time series. Default: None (no limit)
  • include_outside_points (bool) – Whether or not to include outside points. Not allowed when fetching aggregates. Default: False
  • ignore_unknown_ids (bool) – Whether or not to ignore missing time series rather than raising an exception. Default: False
Returns:

A DatapointsArray object containing the requested data, or a DatapointsArrayList if multiple time series were asked for (the ordering is ids first, then external_ids). If ignore_unknown_ids is True, a single time series is requested and it is not found, the function will return None.

Return type:

Union[None, DatapointsArray, DatapointsArrayList]

Examples

Note: For more usage examples, see DatapointsAPI.retrieve method (which accepts exactly the same arguments).

Get weekly min and max aggregates for a time series with id=42 since the year 2000, then compute the range of values:

>>> from cognite.client import CogniteClient
>>> from datetime import datetime, timezone
>>> client = CogniteClient()
>>> dps = client.time_series.data.retrieve_arrays(
...     id=42,
...     start=datetime(2020, 1, 1, tzinfo=timezone.utc),
...     aggregates=["min", "max"],
...     granularity="7d")
>>> weekly_range = dps.max - dps.min

Get up-to 2 million raw datapoints for the last 48 hours for a noisy time series with external_id=”ts-noisy”, then use a small and wide moving average filter to smooth it out:

>>> import numpy as np
>>> dps = client.time_series.data.retrieve_arrays(
...     external_id="ts-noisy",
...     start="2d-ago",
...     limit=2_000_000)
>>> smooth = np.convolve(dps.value, np.ones(5) / 5)  # doctest: +SKIP
>>> smoother = np.convolve(dps.value, np.ones(20) / 20)  # doctest: +SKIP

Get raw datapoints for multiple time series, that may or may not exist, from the last 2 hours, then find the largest gap between two consecutive values for all time series, also taking the previous value into account (outside point).

>>> id_lst = [42, 43, 44]
>>> dps_lst = client.time_series.data.retrieve_arrays(
...     id=id_lst,
...     start="2h-ago",
...     include_outside_points=True,
...     ignore_unknown_ids=True)
>>> largest_gaps = [np.max(np.diff(dps.timestamp)) for dps in dps_lst]

Get raw datapoints for a time series with external_id=”bar” from the last 10 weeks, then convert to a pandas.Series (you can of course also use the to_pandas() convenience method if you want a pandas.DataFrame):

>>> import pandas as pd
>>> dps = client.time_series.data.retrieve_arrays(external_id="bar", start="10w-ago")
>>> series = pd.Series(dps.value, index=dps.timestamp)

Retrieve datapoints in pandas dataframe

DatapointsAPI.retrieve_dataframe(*, id: Union[None, int, Dict[str, Any], Sequence[Union[int, Dict[str, Any]]]] = None, external_id: Union[None, str, Dict[str, Any], Sequence[Union[str, Dict[str, Any]]]] = None, start: Union[int, str, datetime, None] = None, end: Union[int, str, datetime, None] = None, aggregates: Union[str, List[str], None] = None, granularity: Optional[str] = None, limit: Optional[int] = None, include_outside_points: bool = False, ignore_unknown_ids: bool = False, uniform_index: bool = False, include_aggregate_name: bool = True, include_granularity_name: bool = False, column_names: Literal[('id', 'external_id')] = 'external_id') → pd.DataFrame

Get datapoints directly in a pandas dataframe.

Note: If you have duplicated time series in your query, the dataframe columns will also contain duplicates.

Parameters:
  • start (Union[int, str, datetime, None]) – Inclusive start. Default: 1970-01-01 UTC.
  • end (Union[int, str, datetime, None]) – Exclusive end. Default: “now”
  • id (Union[None, int, Dict[str, Any], List[Union[int, Dict[str, Any]]]]) – Id, dict (with id) or (mixed) sequence of these. See examples below.
  • external_id (Union[None, str, Dict[str, Any], List[Union[str, Dict[str, Any]]]]) – External id, dict (with external id) or (mixed) sequence of these. See examples below.
  • aggregates (Union[str, List[str], None]) – Single aggregate or list of aggregates to retrieve. Default: None (raw datapoints returned)
  • granularity (str) – The granularity to fetch aggregates at. e.g. ’15s’, ‘2h’, ‘10d’. Default: None.
  • limit (int) – Maximum number of datapoints to return for each time series. Default: None (no limit)
  • include_outside_points (bool) – Whether or not to include outside points. Not allowed when fetching aggregates. Default: False
  • ignore_unknown_ids (bool) – Whether or not to ignore missing time series rather than raising an exception. Default: False
  • uniform_index (bool) – If only querying aggregates AND a single granularity is used AND no limit is used, specifying uniform_index=True will return a dataframe with an equidistant datetime index from the earliest start to the latest end (missing values will be NaNs). If these requirements are not met, a ValueError is raised. Default: False
  • include_aggregate_name (bool) – Include ‘aggregate’ in the column name, e.g. my-ts|average. Ignored for raw time series. Default: True
  • include_granularity_name (bool) – Include ‘granularity’ in the column name, e.g. my-ts|12h. Added after ‘aggregate’ when present. Ignored for raw time series. Default: False
  • column_names ("id" | "external_id") – Use either ids or external ids as column names. Time series missing external id will use id as backup. Default: “external_id”
Returns:

A pandas DataFrame containing the requested time series. The ordering of columns is ids first, then external_ids. For time series with multiple aggregates, they will be sorted in alphabetical order (“average” before “max”).

Return type:

pandas.DataFrame

Examples

Get a pandas dataframe using a single id, and use this id as column name, with no more than 100 datapoints:

>>> from cognite.client import CogniteClient
>>> client = CogniteClient()
>>> df = client.time_series.data.retrieve_dataframe(
...     id=12345,
...     start="2w-ago",
...     end="now",
...     limit=100,
...     column_names="id")

Get the pandas dataframe with a uniform index (fixed spacing between points) of 1 day, for two time series with individually specified aggregates, from 1990 through 2020:

>>> from datetime import datetime, timezone
>>> df = client.time_series.data.retrieve_dataframe(
...     id=[
...         {"external_id": "foo", "aggregates": ["discrete_variance"]},
...         {"external_id": "bar", "aggregates": ["total_variation", "continuous_variance"]},
...     ],
...     granularity="1d",
...     start=datetime(1990, 1, 1, tzinfo=timezone.utc),
...     end=datetime(2020, 12, 31, tzinfo=timezone.utc),
...     uniform_index=True)

Get a pandas dataframe containing the ‘average’ aggregate for two time series using a 30 day granularity, starting Jan 1, 1970 all the way up to present, without having the aggregate name in the column names:

>>> df = client.time_series.data.retrieve_dataframe(
...     external_id=["foo", "bar"],
...     aggregates=["average"],
...     granularity="30d",
...     include_aggregate_name=False)

Retrieve latest datapoint

DatapointsAPI.retrieve_latest(id: Union[int, List[int], None] = None, external_id: Union[str, List[str], None] = None, before: Union[int, str, datetime.datetime, None] = 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:
  • id (Union[int, List[int]]) – 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.time_series.data.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.time_series.data.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.time_series.data.retrieve_latest(external_id=["abc", "def"])
>>> latest_abc = res[0][0]
>>> latest_def = res[1][0]

Insert data points

DatapointsAPI.insert(datapoints: Union[cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsArray, Sequence[Dict[str, Union[int, float, str, datetime.datetime]]], Sequence[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]], id: Optional[int] = None, external_id: Optional[str] = None) → None

Insert datapoints into a time series

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

Parameters:
  • datapoints (Union[Datapoints, DatapointsArray, List[Dict], List[Tuple]]) – The datapoints you wish to insert. Can either be a list of tuples, a list of dictionaries, a Datapoints object or a DatapointsArray 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
>>> from datetime import datetime, timezone
>>> c = CogniteClient()
>>> # With datetime objects:
>>> datapoints = [
...     (datetime(2018,1,1, tzinfo=timezone.utc), 1000),
...     (datetime(2018,1,2, tzinfo=timezone.utc), 2000),
... ]
>>> c.time_series.data.insert(datapoints, id=1)
>>> # With ms since epoch:
>>> datapoints = [(150000000000, 1000), (160000000000, 2000)]
>>> c.time_series.data.insert(datapoints, id=2)

Or they can be a list of dictionaries:

>>> datapoints = [
...     {"timestamp": 150000000000, "value": 1000},
...     {"timestamp": 160000000000, "value": 2000},
... ]
>>> c.time_series.data.insert(datapoints, external_id="abcd")

Or they can be a Datapoints or DatapointsArray object (with raw datapoints only). Note that the id or external_id set on these objects are not inspected/used (as they belong to the “from-time-series”, and not the “to-time-series”), and so you must explicitly pass the identifier of the time series you want to insert into, which in this example is external_id=”efgh”:

>>> data = c.time_series.data.retrieve(external_id="abc", start="1w-ago", end="now")
>>> c.time_series.data.insert(data, external_id="efgh")

Insert data points into multiple time series

DatapointsAPI.insert_multiple(datapoints: List[Dict[str, Union[str, int, List[T], cognite.client.data_classes.datapoints.Datapoints, cognite.client.data_classes.datapoints.DatapointsArray]]]) → 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 dictionaries, each containing datapoints for a different (presumably) time series. These dictionaries must have the key “datapoints” (containing the data) specified as a Datapoints object, a DatapointsArray object, or list of either tuples (timestamp, value) or dictionaries, {“timestamp”: ts, “value”: value}:

>>> from cognite.client import CogniteClient
>>> from datetime import datetime, timezone
>>> client = CogniteClient()

>>> datapoints = []
>>> # With datetime objects and id
>>> datapoints.append(
...     {"id": 1, "datapoints": [
...         (datetime(2018,1,1,tzinfo=timezone.utc), 1000),
...         (datetime(2018,1,2,tzinfo=timezone.utc), 2000),
... ]})

>>> # With ms since epoch and externalId
>>> datapoints.append({"externalId": "foo", "datapoints": [(150000000000, 1000), (160000000000, 2000)]})

>>> # With raw data in a Datapoints object (or DatapointsArray)
>>> data_to_clone = client.time_series.data.retrieve(external_id="bar")
>>> datapoints.append({"externalId": "bar-clone", "datapoints": data_to_clone})
>>> client.time_series.data.insert_multiple(datapoints)

Insert pandas dataframe

DatapointsAPI.insert_dataframe(df: pd.DataFrame, external_id_headers: bool = True, dropna: bool = True) → None

Insert a dataframe (columns must be unique).

The index of the dataframe must contain the timestamps (pd.DatetimeIndex). The names of the columns specify the ids or external ids of the time series to which the datapoints will be written.

Said time series must already exist.

Parameters:
  • df (pandas.DataFrame) – Pandas DataFrame object containing the time series.
  • external_id_headers (bool) – Interpret the column names as external id. Pass False if using ids. Default: True.
  • dropna (bool) – Set to True to ignore NaNs in the given DataFrame, applied per column. Default: True.
Returns:

None

Examples

Post a dataframe with white noise:

>>> import numpy as np
>>> import pandas as pd
>>> from cognite.client import CogniteClient
>>>
>>> client = CogniteClient()
>>> ts_xid = "my-foo-ts"
>>> idx = pd.date_range(start="2018-01-01", periods=100, freq="1d")
>>> noise = np.random.normal(0, 1, 100)
>>> df = pd.DataFrame({ts_xid: noise}, index=idx)
>>> client.time_series.data.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: Optional[int] = None, external_id: Optional[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.time_series.data.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.time_series.data.delete_ranges(ranges)

Data classes

class cognite.client.data_classes.datapoints.Datapoint(timestamp: Optional[int] = None, value: Union[str, float, None] = None, average: Optional[float] = None, max: Optional[float] = None, min: Optional[float] = None, count: Optional[int] = None, sum: Optional[float] = None, interpolation: Optional[float] = None, step_interpolation: Optional[float] = None, continuous_variance: Optional[float] = None, discrete_variance: Optional[float] = None, total_variation: Optional[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). Can be negative to define a date before 1970. Minimum timestamp is 1900.01.01 00:00:00 UTC
  • value (Union[str, float]) – The data value. Can be string or numeric
  • 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: bool = False) → pandas.DataFrame

Convert the datapoint into a pandas DataFrame.

Parameters:camel_case (bool) – Convert column names to camel case (e.g. stepInterpolation instead of step_interpolation)
Returns:pandas.DataFrame
class cognite.client.data_classes.datapoints.Datapoints(id: Optional[int] = None, external_id: Optional[str] = None, is_string: Optional[bool] = None, is_step: Optional[bool] = None, unit: Optional[str] = None, granularity: Optional[str] = None, timestamp: Optional[Sequence[int]] = None, value: Union[Sequence[str], Sequence[float], None] = None, average: Optional[List[float]] = None, max: Optional[List[float]] = None, min: Optional[List[float]] = None, count: Optional[List[int]] = None, sum: Optional[List[float]] = None, interpolation: Optional[List[float]] = None, step_interpolation: Optional[List[float]] = None, continuous_variance: Optional[List[float]] = None, discrete_variance: Optional[List[float]] = None, total_variation: Optional[List[float]] = None, error: Optional[List[Union[None, str]]] = None)

Bases: cognite.client.data_classes._base.CogniteResource

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
  • 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.
  • granularity (str) – The granularity of the aggregate datapoints (does not apply to raw data)
  • timestamp (List[int]) – The data timestamps in milliseconds since the epoch (Jan 1, 1970). Can be negative to define a date before 1970. Minimum timestamp is 1900.01.01 00:00:00 UTC
  • value (Union[List[str], List[float]]) – The data values. Can be string or numeric
  • 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 dictionary representing the instance.
Return type:Dict[str, Any]
to_pandas(column_names: str = 'external_id', include_aggregate_name: bool = True, include_granularity_name: bool = False, 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 “external_id”, can also be “id”. For time series with no external ID, ID will be used instead.
  • include_aggregate_name (bool) – Include aggregate in the column name
  • include_granularity_name (bool) – Include granularity in the column name (after aggregate if present)
  • 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.DatapointsArray(id: int = None, external_id: str = None, is_string: bool = None, is_step: bool = None, unit: str = None, granularity: str = None, timestamp: NumpyDatetime64NSArray = None, value: Union[NumpyFloat64Array, NumpyObjArray] = None, average: NumpyFloat64Array = None, max: NumpyFloat64Array = None, min: NumpyFloat64Array = None, count: NumpyInt64Array = None, sum: NumpyFloat64Array = None, interpolation: NumpyFloat64Array = None, step_interpolation: NumpyFloat64Array = None, continuous_variance: NumpyFloat64Array = None, discrete_variance: NumpyFloat64Array = None, total_variation: NumpyFloat64Array = None)

Bases: cognite.client.data_classes._base.CogniteResource

An object representing datapoints using numpy arrays.

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

Dump the DatapointsArray into a json serializable Python data type.

Parameters:
  • camel_case (bool) – Use camelCase for attribute names. Default: False.
  • convert_timestamps (bool) – Convert timestamps to ISO 8601 formatted strings. Default: False (returns as integer, milliseconds since epoch)
Returns:

A dictionary representing the instance.

Return type:

Dict[str, Any]

to_pandas(column_names: Literal[id, external_id] = 'external_id', include_aggregate_name: bool = True, include_granularity_name: bool = False) → pandas.DataFrame

Convert the DatapointsArray into a pandas DataFrame.

Parameters:
  • column_names (str) – Which field to use as column header. Defaults to “external_id”, can also be “id”. For time series with no external ID, ID will be used instead.
  • include_aggregate_name (bool) – Include aggregate in the column name
  • include_granularity_name (bool) – Include granularity in the column name (after aggregate if present)
Returns:

The datapoints as a pandas DataFrame.

Return type:

pandas.DataFrame

class cognite.client.data_classes.datapoints.DatapointsArrayList(resources: Collection[Any], cognite_client: Optional[CogniteClient] = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

dump(camel_case: bool = False, convert_timestamps: 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. Default: False.
  • convert_timestamps (bool) – Convert timestamps to ISO 8601 formatted strings. Default: False (returns as integer, milliseconds since epoch)
Returns:

A list of dicts representing the instance.

Return type:

List[Dict[str, Any]]

get(id: Optional[int] = None, external_id: Optional[str] = None) → Union[None, cognite.client.data_classes.datapoints.DatapointsArray, List[cognite.client.data_classes.datapoints.DatapointsArray]]

Get a specific DatapointsArray from this list by id or exernal_id.

Note: For duplicated time series, returns a list of DatapointsArray.

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

The requested item(s)

Return type:

Union[None, DatapointsArray, List[DatapointsArray]]

to_pandas(column_names: Literal[id, external_id] = 'external_id', include_aggregate_name: bool = True, include_granularity_name: bool = False) → pandas.DataFrame

Convert the DatapointsArrayList into a pandas DataFrame.

Parameters:
  • column_names (str) – Which field to use as column header. Defaults to “external_id”, can also be “id”. For time series with no external ID, ID will be used instead.
  • include_aggregate_name (bool) – Include aggregate in the column name
  • include_granularity_name (bool) – Include granularity in the column name (after aggregate if present)
Returns:

The datapoints as a pandas DataFrame.

Return type:

pandas.DataFrame

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

Bases: cognite.client.data_classes._base.CogniteResourceList

get(id: Optional[int] = None, external_id: Optional[str] = None) → Union[None, cognite.client.data_classes.datapoints.Datapoints, List[cognite.client.data_classes.datapoints.Datapoints]]

Get a specific Datapoints from this list by id or exernal_id.

Note: For duplicated time series, returns a list of Datapoints.

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

The requested item(s)

Return type:

Union[None, Datapoints, List[Datapoints]]

to_pandas(column_names: Literal[id, external_id] = 'external_id', include_aggregate_name: bool = True, include_granularity_name: bool = False) → pandas.DataFrame

Convert the datapoints list into a pandas DataFrame.

Parameters:
  • column_names (str) – Which field to use as column header. Defaults to “external_id”, can also be “id”. For time series with no external ID, ID will be used instead.
  • include_aggregate_name (bool) – Include aggregate in the column name
  • include_granularity_name (bool) – Include granularity in the column name (after aggregate if present)
Returns:

The datapoints list as a pandas DataFrame.

Return type:

pandas.DataFrame

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[Sequence[int]] = None, external_ids: Optional[Sequence[str]] = None, ignore_unknown_ids: bool = False) → cognite.client.data_classes.sequences.SequenceList

Retrieve multiple sequences by id.

Parameters:
  • ids (SequenceType[int], optional) – IDs
  • external_ids (SequenceType[str], optional) – External IDs
  • ignore_unknown_ids (bool, optional) – Ignore IDs and external IDs that are not found rather than throw an exception.
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: Sequence[int] = None, asset_subtree_ids: Sequence[int] = None, asset_subtree_external_ids: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[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 (SequenceType[int]) – Filter out sequences that are not linked to any of these assets.
  • asset_subtree_ids (SequenceType[int]) – List of asset subtrees ids to filter on.
  • asset_subtree_external_ids (SequenceType[str]) – List of asset subtrees external ids to filter on.
  • data_set_ids (SequenceType[int]) – Return only events in the specified data sets with these ids.
  • data_set_external_ids (SequenceType[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, Sequence[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, SequenceType[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, Sequence[int]] = None, external_id: Union[str, Sequence[str]] = None) → None

Delete one or more sequences.

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

Updating column definitions:

Currently, updating the column definitions of a sequence is only supported through partial update, using add, remove and modify methods on the columns property.

Add a single new column:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate
>>> c = CogniteClient()
>>>
>>> my_update = SequenceUpdate(id=1).columns.add({"valueType":"STRING","externalId":"user","description":"some description"})
>>> res = c.sequences.update(my_update)

Add multiple new columns:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate
>>> c = CogniteClient()
>>>
>>> column_def = [{"valueType":"STRING","externalId":"user","description":"some description"}, {"valueType":"DOUBLE","externalId":"amount"}]
>>> my_update = SequenceUpdate(id=1).columns.add(column_def)
>>> res = c.sequences.update(my_update)

Remove a single column:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate
>>> c = CogniteClient()
>>>
>>> my_update = SequenceUpdate(id=1).columns.remove("col_external_id1")
>>> res = c.sequences.update(my_update)

Remove multiple columns:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate
>>> c = CogniteClient()
>>>
>>> my_update = SequenceUpdate(id=1).columns.remove(["col_external_id1","col_external_id2"])
>>> res = c.sequences.update(my_update)

Update existing columns:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes import SequenceUpdate, SequenceColumnUpdate
>>> c = CogniteClient()
>>>
>>> column_updates = [                      SequenceColumnUpdate(external_id="col_external_id_1").external_id.set("new_col_external_id"),                      SequenceColumnUpdate(external_id="col_external_id_2").description.set("my new description"),                    ]
>>> my_update = SequenceUpdate(id=1).columns.modify(column_updates)
>>> res = c.sequences.update(my_update)

Retrieve data

SequencesDataAPI.retrieve(start: int, end: Optional[int], column_external_ids: Optional[Sequence[str]] = None, external_id: Union[str, Sequence[str]] = None, id: Union[int, Sequence[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[SequenceType[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. 10000 is the maximum limit per request.
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) → pandas.DataFrame

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[SequenceType[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, Sequence[Union[int, str, float]]], Sequence[Tuple[int, Sequence[Union[int, str, float]]]], Sequence[Dict[str, Any]], cognite.client.data_classes.sequences.SequenceData], column_external_ids: Optional[Sequence[str]], id: int = None, external_id: str = None) → None

Insert rows into a sequence

Parameters:
  • column_external_ids (Optional[SequenceType[str]]) – List of external id for the columns of the sequence.
  • rows (Union[ Dict[int, SequenceType[Union[int, float, str]]], SequenceType[Tuple[int, SequenceType[Union[int, float, str]]]], SequenceType[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: pandas.DataFrame, 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.
  • 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: Sequence[int], id: int = None, external_id: str = None) → None

Delete rows from a sequence

Parameters:
  • rows (SequenceType[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: Sequence[Dict[str, Any]] = None, created_time: int = None, last_updated_time: int = None, data_set_id: int = None, cognite_client: CogniteClient = 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 (SequenceType[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.SequenceColumnUpdate(id: int = None, external_id: str = None)

Bases: cognite.client.data_classes._base.CogniteUpdate

No description.

Parameters:external_id (str) – The external ID provided by the client. Must be unique for the resource type.
class cognite.client.data_classes.sequences.SequenceData(id: int = None, external_id: str = None, rows: Sequence[dict] = None, row_numbers: Sequence[int] = None, values: Sequence[Sequence[Union[int, str, float]]] = None, columns: Sequence[Dict[str, Any]] = None)

Bases: cognite.client.data_classes._base.CogniteResource

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 (SequenceType[dict]) – Combined row numbers and row data object from the API. If you pass this, row_numbers/values are ignored.
  • row_numbers (SequenceType[int]) – The data row numbers.
  • values (SequenceType[SequenceType[ Union[int, str, float]]]) – The data values, one row at a time.
  • columns – SequenceType[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 dictionary representing the instance.
Return type: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: Collection[Any], cognite_client: CogniteClient = 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: Sequence[int] = None, asset_subtree_ids: Sequence[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: Sequence[Dict[str, Any]] = None, cognite_client: CogniteClient = 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 (SequenceType[int]) – Return only sequences linked to one of the specified assets.
  • asset_subtree_ids (SequenceType[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 (SequenceType[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: Collection[Any], cognite_client: CogniteClient = 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, Sequence[str]], recursive: bool = False) → None

Delete one or more databases.

Parameters:
  • name (Union[str, Sequence[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, Sequence[str]]) → None

Delete one or more tables.

Parameters:
  • db_name (str) – Database to delete tables from.
  • name (Union[str, Sequence[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[Sequence[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[Sequence[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"}}
>>> c.raw.rows.insert("db1", "table1", rows)

Delete rows from a table

RawRowsAPI.delete(db_name: str, table_name: str, key: Union[str, Sequence[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, Sequence[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)

Retrieve pandas dataframe

RawRowsAPI.retrieve_dataframe(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) → pandas.DataFrame

Retrieve rows in a table as a pandas dataframe.

Rowkeys are used as the index.

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 in a pandas dataframe.

Return type:

pandas.DataFrame

Examples

Get dataframe:

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

Insert pandas dataframe

RawRowsAPI.insert_dataframe(db_name: str, table_name: str, dataframe: Any, ensure_parent: bool = False) → None

Insert pandas dataframe into a table

Use index as rowkeys.

Parameters:
  • db_name (str) – Name of the database.
  • table_name (str) – Name of the table.
  • dataframe (pandas.DataFrame) – The dataframe to insert. Index will be used as rowkeys.
  • ensure_parent (bool) – Create database/table if they don’t already exist.
Returns:

None

Examples

Insert new rows into a table:

>>> import pandas as pd
>>> from cognite.client import CogniteClient
>>>
>>> c = CogniteClient()
>>> df = pd.DataFrame(data={"a": 1, "b": 2}, index=["r1", "r2", "r3"])
>>> res = c.raw.rows.insert_dataframe("db1", "table1", df)

Data classes

class cognite.client.data_classes.raw.Database(name: str = None, created_time: int = None, cognite_client: CogniteClient = 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: Collection[Any], cognite_client: CogniteClient = 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: CogniteClient = 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() → pandas.DataFrame

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: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

to_pandas() → pandas.DataFrame

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: CogniteClient = 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: Collection[Any], cognite_client: CogniteClient = 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: Sequence[str], fetch_resources: bool = False) → cognite.client.data_classes.relationships.RelationshipList

Retrieve multiple relationships by external id.

Parameters:
  • external_ids (Sequence[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: Sequence[str] = None, source_types: Sequence[str] = None, target_external_ids: Sequence[str] = None, target_types: Sequence[str] = None, data_set_ids: Sequence[int] = None, data_set_external_ids: Sequence[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 (Sequence[str]) – Include relationships that have any of these values in their source External Id field
  • source_types (Sequence[str]) – Include relationships that have any of these values in their source Type field
  • target_external_ids (Sequence[str]) – Include relationships that have any of these values in their target External Id field
  • target_types (Sequence[str]) – Include relationships that have any of these values in their target Type field
  • data_set_ids (Sequence[int]) – Return only relationships in the specified data sets with these ids.
  • data_set_external_ids (Sequence[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, Sequence[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, Sequence[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", "File", "Event", "Sequence";
- do not provide the value for the source and target arguments of the Relationship class, only source_external_id / source_type and target_external_id / target_type. These (source and target) are used as part of fetching actual resources specified in other fields.
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, Sequence[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, Sequence[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, Sequence[str]], ignore_unknown_ids: bool = False) → None

Delete one or more relationships.

Parameters:
  • external_id (Union[str, Sequence[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: Sequence[Union[cognite.client.data_classes.labels.Label, str, cognite.client.data_classes.labels.LabelDefinition, dict]] = None, created_time: int = None, last_updated_time: int = None, cognite_client: CogniteClient = 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 (SequenceType[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: Sequence[str] = None, source_types: Sequence[str] = None, target_external_ids: Sequence[str] = None, target_types: Sequence[str] = None, data_set_ids: Sequence[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: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteFilter

Filter on relationships with exact match. Multiple filter elements 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 (Sequence[str]) – Include relationships that have any of these values in their sourceExternalId field
  • source_types (Sequence[str]) – Include relationships that have any of these values in their sourceType field
  • target_external_ids (Sequence[str]) – Include relationships that have any of these values in their targetExternalId field
  • target_types (Sequence[str]) – Include relationships that have any of these values in their targetType field
  • data_set_ids (Sequence[Dict[str, Any]]) – Either one of internalId (int) or externalId (str)
  • start_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milliseconds (inclusive)
  • end_time (Dict[str, int]) – Range between two timestamps, minimum and maximum milliseconds (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) → 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.relationships.RelationshipList(resources: Collection[Any], cognite_client: CogniteClient = 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.

Geospatial

Note

Check https://github.com/cognitedata/geospatial-examples for some complete examples.

Create feature types

GeospatialAPI.create_feature_types(feature_type: Union[cognite.client.data_classes.geospatial.FeatureType, Sequence[cognite.client.data_classes.geospatial.FeatureType]]) → Union[cognite.client.data_classes.geospatial.FeatureType, cognite.client.data_classes.geospatial.FeatureTypeList]

Creates feature types <https://docs.cognite.com/api/v1/#operation/createFeatureTypes>

Parameters:feature_type (Union[FeatureType, Sequence[FeatureType]]) – feature type definition or list of feature type definitions to create.
Returns:Created feature type definition(s)
Return type:Union[FeatureType, FeatureTypeList]

Examples

Create new type definitions:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes.geospatial import FeatureType
>>> c = CogniteClient()
>>> feature_types = [
...     FeatureType(external_id="wells", properties={"location": {"type": "POINT", "srid": 4326}})
...     FeatureType(
...       external_id="cities",
...       properties={"name": {"type": "STRING", "size": 10}},
...       search_spec={"name_index": {"properties": ["name"]}}
...     )
... ]
>>> res = c.geospatial.create_feature_types(feature_types)

Delete feature types

GeospatialAPI.delete_feature_types(external_id: Union[str, Sequence[str]], recursive: bool = False) → None

Delete one or more feature type <https://docs.cognite.com/api/v1/#operation/GeospatialDeleteFeatureTypes>

Parameters:
  • external_id (Union[str, Sequence[str]]) – External ID or list of external ids
  • recursive (bool) – if true the features will also be dropped
Returns:

None

Examples

Delete feature type definitions external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.geospatial.delete_feature_types(external_id=["wells", "cities"])

List feature types

GeospatialAPI.list_feature_types() → cognite.client.data_classes.geospatial.FeatureTypeList

List feature types <https://docs.cognite.com/api/v1/#operation/listFeatureTypes>

Returns:List of feature types
Return type:FeatureTypeList

Examples

Iterate over feature type definitions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for feature_type in c.geospatial.list_feature_types():
...     feature_type # do something with the feature type definition

Retrieve feature types

GeospatialAPI.retrieve_feature_types(external_id: Union[str, List[str]]) → Union[cognite.client.data_classes.geospatial.FeatureType, cognite.client.data_classes.geospatial.FeatureTypeList]

Retrieve feature types <https://docs.cognite.com/api/v1/#operation/getFeatureTypesByIds>

Parameters:external_id (Union[str, List[str]]) – External ID
Returns:Requested Type or None if it does not exist.
Return type:FeatureTypeList

Examples

Get Type by external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.geospatial.retrieve_feature_types(external_id="1")

Update feature types

GeospatialAPI.patch_feature_types(patch: Union[cognite.client.data_classes.geospatial.FeatureTypePatch, Sequence[cognite.client.data_classes.geospatial.FeatureTypePatch]]) → cognite.client.data_classes.geospatial.FeatureTypeList

Patch feature types <https://docs.cognite.com/api/v1/#operation/updateFeatureTypes>

Parameters:patch (Union[FeatureTypePatch, Sequence[FeatureTypePatch]]) – the patch to apply
Returns:The patched feature types.
Return type:FeatureTypeList

Examples

Add one property to a feature type and add indexes

>>> from cognite.client.data_classes.geospatial import Patches
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.geospatial.patch_feature_types(
...    patch=FeatureTypePatch(
...       external_id="wells",
...       property_patches=Patches(add={"altitude": {"type": "DOUBLE"}}),
...       search_spec_patches=Patches(
...         add={
...           "altitude_idx": {"properties": ["altitude"]},
...           "composite_idx": {"properties": ["location", "altitude"]}
...         }
...       )
...    )
... )

Add an additional index to an existing property

>>> from cognite.client.data_classes.geospatial import Patches
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.geospatial.patch_feature_types(
...    patch=FeatureTypePatch(
...         external_id="wells",
...         search_spec_patches=Patches(add={"location_idx": {"properties": ["location"]}})
... ))

Create features

GeospatialAPI.create_features(feature_type_external_id: str, feature: Union[cognite.client.data_classes.geospatial.Feature, Sequence[cognite.client.data_classes.geospatial.Feature], cognite.client.data_classes.geospatial.FeatureList], allow_crs_transformation: bool = False, chunk_size: int = None) → Union[cognite.client.data_classes.geospatial.Feature, cognite.client.data_classes.geospatial.FeatureList]

Creates features <https://docs.cognite.com/api/v1/#operation/createFeatures>

Parameters:
  • feature_type_external_id – Feature type definition for the features to create.
  • feature – one feature or a list of features to create or a FeatureList object
  • allow_crs_transformation – If true, then input geometries will be transformed into the Coordinate Reference System defined in the feature type specification. When it is false, then requests with geometries in Coordinate Reference System different from the ones defined in the feature type will result in CogniteAPIError exception.
  • chunk_size – maximum number of items in a single request to the api
Returns:

Created features

Return type:

Union[Feature, FeatureList]

Examples

Create a new feature type and corresponding feature:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> feature_types = [
...     FeatureType(
...         external_id="my_feature_type",
...         properties={
...             "location": {"type": "POINT", "srid": 4326},
...             "temperature": {"type": "DOUBLE"}
...         }
...     )
... ]
>>> res = c.geospatial.create_feature_types(feature_types)
>>> res = c.geospatial.create_features(
...     feature_type_external_id="my_feature_type",
...     feature=Feature(
...         external_id="my_feature",
...         location={"wkt": "POINT(1 1)"},
...         temperature=12.4
...     )
... )

Delete features

GeospatialAPI.delete_features(feature_type_external_id: str, external_id: Union[str, Sequence[str]] = None) → None

Delete one or more feature <https://docs.cognite.com/api/v1/#operation/deleteFeatures>

Parameters:
  • feature_type_external_id – Feature type external id for the features to delete.
  • external_id (Union[str, Sequence[str]]) – External ID or list of external ids
Returns:

None

Examples

Delete feature type definitions external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.geospatial.delete_features(
...     feature_type_external_id="my_feature_type",
...     external_id=my_feature
... )

Retrieve features

GeospatialAPI.retrieve_features(feature_type_external_id: str, external_id: Union[str, List[str]], properties: Dict[str, Any] = None) → Union[cognite.client.data_classes.geospatial.FeatureList, cognite.client.data_classes.geospatial.Feature]

Retrieve features <https://docs.cognite.com/api/v1/#operation/getFeaturesByIds>

Parameters:
  • feature_type_external_id – Feature type external id for the features to retrieve.
  • external_id (Union[str, List[str]]) – External ID or list of external ids
  • properties (Dict[str, Any]) – the output property selection
Returns:

Requested features or None if it does not exist.

Return type:

FeatureList

Examples

Retrieve one feature by its external id:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> c.geospatial.retrieve_features(
...     feature_type_external_id="my_feature_type",
...     external_id="my_feature"
... )

Update features

GeospatialAPI.update_features(feature_type_external_id: str, feature: Union[cognite.client.data_classes.geospatial.Feature, Sequence[cognite.client.data_classes.geospatial.Feature]], allow_crs_transformation: bool = False, chunk_size: int = None) → cognite.client.data_classes.geospatial.FeatureList

Update features <https://docs.cognite.com/api/v1/#operation/updateFeatures>

Parameters:
  • feature_type_external_id – Feature type definition for the features to update.
  • feature (Union[Feature, Sequence[Feature]]) – feature or list of features.
  • allow_crs_transformation – If true, then input geometries will be transformed into the Coordinate Reference System defined in the feature type specification. When it is false, then requests with geometries in Coordinate Reference System different from the ones defined in the feature type will result in CogniteAPIError exception.
  • chunk_size – maximum number of items in a single request to the api
Returns:

Updated features

Return type:

FeatureList

Examples

Update one feature:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature = c.geospatial.create_features(
...     feature_type_external_id="my_feature_type",
...     feature=Feature(external_id="my_feature", temperature=12.4)
... )
>>> my_updated_feature = c.geospatial.update_features(
...     feature_type_external_id="my_feature_type",
...     feature=Feature(external_id="my_feature", temperature=6.237)
... )

List features

GeospatialAPI.list_features(feature_type_external_id: str, filter: Optional[Dict[str, Any]] = None, properties: Dict[str, Any] = None, limit: int = 100, allow_crs_transformation: bool = False) → cognite.client.data_classes.geospatial.FeatureList

List features <https://docs.cognite.com/api/v1/#operation/listFeatures>

This method allows to filter all features.

Parameters:
  • feature_type_external_id – the feature type to list features for
  • filter (Dict[str, Any]) – the list filter
  • limit (int, optional) – Maximum number of features to return. Defaults to 25. Set to -1, float(“inf”) or None to return all features.
  • properties (Dict[str, Any]) – the output property selection
  • allow_crs_transformation – If true, then input geometries if existing in the filter will be transformed into the Coordinate Reference System defined in the feature type specification. When it is false, then requests with geometries in Coordinate Reference System different from the ones defined in the feature type will result in CogniteAPIError exception.
Returns:

the filtered features

Return type:

FeatureList

Examples

List features:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature_type = c.geospatial.retrieve_feature_types(
...     external_id="my_feature_type"
... )
>>> my_feature = c.geospatial.create_features(
...     feature_type_external_id=my_feature_type,
...     feature=Feature(
...         external_id="my_feature",
...         temperature=12.4,
...         location={"wkt": "POINT(0 1)"}
...     )
... )
>>> res = c.geospatial.list_features(
...     feature_type_external_id="my_feature_type",
...     filter={"range": {"property": "temperature", "gt": 12.0}}
... )
>>> for f in res:
...     # do something with the features

Search for features and select output properties:

>>> res = c.geospatial.list_features(
...     feature_type_external_id=my_feature_type,
...     filter={},
...     properties={"temperature": {}, "pressure": {}}
... )

Search for features with spatial filters:

>>> res = c.geospatial.list_features(
...     feature_type_external_id=my_feature_type,
...     filter={"stWithin": {
...         "property": "location",
...         "value": {"wkt": "POLYGON((0 0, 0 1, 1 1, 0 0))"}
...     }}
... )

Search features

GeospatialAPI.search_features(feature_type_external_id: str, filter: Optional[Dict[str, Any]] = None, properties: Dict[str, Any] = None, limit: int = 100, order_by: Sequence[cognite.client.data_classes.geospatial.OrderSpec] = None, allow_crs_transformation: bool = False) → cognite.client.data_classes.geospatial.FeatureList

Search for features <https://docs.cognite.com/api/v1/#operation/searchFeatures>

This method allows to order the result by one or more of the properties of the feature type. However, the number of items returned is limited to 1000 and there is no support for cursors yet. If you need to return more than 1000 items, use the stream_features(…) method instead.

Parameters:
  • feature_type_external_id – the feature type to search for
  • filter (Dict[str, Any]) – the search filter
  • limit (int) – maximum number of results
  • properties (Dict[str, Any]) – the output property selection
  • order_by (Sequence[OrderSpec]) – the order specification
  • allow_crs_transformation – If true, then input geometries will be transformed into the Coordinate Reference System defined in the feature type specification. When it is false, then requests with geometries in Coordinate Reference System different from the ones defined in the feature type will result in CogniteAPIError exception.
Returns:

the filtered features

Return type:

FeatureList

Examples

Search for features:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature_type = c.geospatial.retrieve_feature_types(
...     external_id="my_feature_type"
... )
>>> my_feature = c.geospatial.create_features(
...     feature_type_external_id=my_feature_type,
...     feature=Feature(
...         external_id="my_feature",
...         temperature=12.4,
...         location={"wkt": "POINT(0 1)"}
...     )
... )
>>> res = c.geospatial.search_features(
...     feature_type_external_id="my_feature_type",
...     filter={"range": {"property": "temperature", "gt": 12.0}}
... )
>>> for f in res:
...     # do something with the features

Search for features and select output properties:

>>> res = c.geospatial.search_features(
...     feature_type_external_id=my_feature_type,
...     filter={},
...     properties={"temperature": {}, "pressure": {}}
... )

Search for features and order results:

>>> res = c.geospatial.search_features(
...     feature_type_external_id=my_feature_type,
...     filter={},
...     order_by=[
...         OrderSpec("temperature", "ASC"),
...         OrderSpec("pressure", "DESC")]
... )

Search for features with spatial filters:

>>> res = c.geospatial.search_features(
...     feature_type_external_id=my_feature_type,
...     filter={"stWithin": {
...         "property": "location",
...         "value": {"wkt": "POLYGON((0 0, 0 1, 1 1, 0 0))"}
...     }}
... )

Combining multiple filters:

>>> res = c.geospatial.search_features(
...     feature_type_external_id=my_feature_type,
...     filter={"and": [
...         {"range": {"property": "temperature", "gt": 12.0}},
...         {"stWithin": {
...             "property": "location",
...             "value": {"wkt": "POLYGON((0 0, 0 1, 1 1, 0 0))"}
...         }}
...     ]}
... )
>>> res = c.geospatial.search_features(
...     feature_type_external_id=my_feature_type,
...     filter={"or": [
...         {"range": {"property": "temperature", "gt": 12.0}},
...         {"stWithin": {
...             "property": "location",
...             "value": {"wkt": "POLYGON((0 0, 0 1, 1 1, 0 0))"}
...         }}
...     ]}
... )

Stream features

GeospatialAPI.stream_features(feature_type_external_id: str, filter: Optional[Dict[str, Any]] = None, properties: Dict[str, Any] = None, allow_crs_transformation: bool = False) → Generator[cognite.client.data_classes.geospatial.Feature, None, None]

Stream features <https://docs.cognite.com/api/v1/#operation/searchFeaturesStreaming>

This method allows to return any number of items until the underlying api calls times out. The order of the result items is not deterministic. If you need to order the results, use the search_features(…) method instead.

Parameters:
  • feature_type_external_id – the feature type to search for
  • filter (Dict[str, Any]) – the search filter
  • properties (Dict[str, Any]) – the output property selection
  • allow_crs_transformation – If true, then input geometries will be transformed into the Coordinate Reference System defined in the feature type specification. When it is false, then requests with geometries in Coordinate Reference System different from the ones defined in the feature type will result in CogniteAPIError exception.
Returns:

a generator for the filtered features

Return type:

Generator[Feature]

Examples

Stream features:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature = c.geospatial.create_features(
...     feature_type_external_id="my_feature_type",
...     feature=Feature(external_id="my_feature", temperature=12.4)
... )
>>> features = c.geospatial.stream_features(
...     feature_type_external_id="my_feature_type",
...     filter={"range": {"property": "temperature", "gt": 12.0}}
... )
>>> for f in features:
...     # do something with the features

Stream features and select output properties:

>>> features = c.geospatial.stream_features(
...     feature_type_external_id="my_feature_type",
...     filter={},
...     properties={"temperature": {}, "pressure": {}}
... )
>>> for f in features:
...     # do something with the features

Aggregate features

GeospatialAPI.aggregate_features(feature_type_external_id: str, property: str = None, aggregates: Sequence[str] = None, filter: Optional[Dict[str, Any]] = None, group_by: Sequence[str] = None, order_by: Sequence[cognite.client.data_classes.geospatial.OrderSpec] = None, output: Dict[str, Any] = None) → cognite.client.data_classes.geospatial.FeatureAggregateList

Aggregate filtered features <https://docs.cognite.com/api/v1/#operation/aggregateFeatures>

Parameters:
  • feature_type_external_id – the feature type to filter features from
  • filter (Dict[str, Any]) – the search filter
  • property (str) – the property for which aggregates should be calculated
  • aggregates (Sequence[str]) – list of aggregates to be calculated
  • group_by (Sequence[str]) – list of properties to group by with
  • order_by (Sequence[OrderSpec]) – the order specification
  • output (Dict[str, Any]) – the aggregate output
Returns:

the filtered features

Return type:

FeatureAggregateList

Examples

Aggregate property of features:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature = c.geospatial.create_features(
...     feature_type_external_id="my_feature_type",
...     feature=Feature(external_id="my_feature", temperature=12.4)
... )
>>> res_deprecated = c.geospatial.aggregate_features(
...     feature_type_external_id="my_feature_type",
...     filter={"range": {"property": "temperature", "gt": 12.0}},
...     property="temperature",
...     aggregates=["max", "min"],
...     group_by=["category"],
...     order_by=[OrderSpec("category", "ASC")]
... ) # deprecated
>>> res = c.geospatial.aggregate_features(
...     feature_type_external_id="my_feature_type",
...     filter={"range": {"property": "temperature", "gt": 12.0}},
...     group_by=["category"],
...     order_by=[OrderSpec("category", "ASC")],
...     output={"min_temperature": {"min": {"property": "temperature"}},
...         "max_volume": {"max": {"property": "volume"}}
...     }
... )
>>> for a in res:
...     # loop over aggregates in different groups

Get coordinate reference systems

GeospatialAPI.get_coordinate_reference_systems(srids: Union[int, Sequence[int]]) → cognite.client.data_classes.geospatial.CoordinateReferenceSystemList

Get Coordinate Reference Systems <https://docs.cognite.com/api/v1/#operation/getCoordinateReferenceSystem>

Parameters:srids – (Union[int, Sequence[int]]): SRID or list of SRIDs
Returns:Requested CRSs.
Return type:CoordinateReferenceSystemList

Examples

Get two CRS definitions:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> crs = c.geospatial.get_coordinate_reference_systems(srids=[4326, 4327])

List coordinate reference systems

GeospatialAPI.list_coordinate_reference_systems(only_custom: bool = False) → cognite.client.data_classes.geospatial.CoordinateReferenceSystemList

List Coordinate Reference Systems <https://docs.cognite.com/api/v1/#operation/listGeospatialCoordinateReferenceSystems>

Parameters:only_custom (bool) – list only custom CRSs or not
Returns:list of CRSs.
Return type:CoordinateReferenceSystemList

Examples

Fetch all custom CRSs:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> crs = c.geospatial.list_coordinate_reference_systems(only_custom=True)

Create coordinate reference systems

GeospatialAPI.create_coordinate_reference_systems(crs: Union[cognite.client.data_classes.geospatial.CoordinateReferenceSystem, Sequence[cognite.client.data_classes.geospatial.CoordinateReferenceSystem]]) → cognite.client.data_classes.geospatial.CoordinateReferenceSystemList

Create Coordinate Reference System <https://docs.cognite.com/api/v1/#operation/createGeospatialCoordinateReferenceSystems>

Parameters:crs – a CoordinateReferenceSystem or a list of CoordinateReferenceSystem
Returns:list of CRSs.
Return type:CoordinateReferenceSystemList

Examples

Create a custom CRS:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> custom_crs = CoordinateReferenceSystem(
...     srid = 121111,
...     wkt=(
...          'PROJCS["NTF (Paris) / Lambert zone II",'
...          ' GEOGCS["NTF (Paris)",'
...          '  DATUM["Nouvelle_Triangulation_Francaise_Paris",'
...          '   SPHEROID["Clarke 1880 (IGN)",6378249.2,293.4660212936265,'
...          '    AUTHORITY["EPSG","7011"]],'
...          '   TOWGS84[-168,-60,320,0,0,0,0],'
...          '   AUTHORITY["EPSG","6807"]],'
...          '  PRIMEM["Paris",2.33722917,'
...          '   AUTHORITY["EPSG","8903"]],'
...          '  UNIT["grad",0.01570796326794897,'
...          '   AUTHORITY["EPSG","9105"]], '
...          '  AUTHORITY["EPSG","4807"]],'
...          ' PROJECTION["Lambert_Conformal_Conic_1SP"],'
...          ' PARAMETER["latitude_of_origin",52],'
...          ' PARAMETER["central_meridian",0],'
...          ' PARAMETER["scale_factor",0.99987742],'
...          ' PARAMETER["false_easting",600000],'
...          ' PARAMETER["false_northing",2200000],'
...          ' UNIT["metre",1,'
...          '  AUTHORITY["EPSG","9001"]],'
...          ' AXIS["X",EAST],'
...          ' AXIS["Y",NORTH],'
...          ' AUTHORITY["EPSG","27572"]]'
...     ),
...     proj_string=(
...          '+proj=lcc +lat_1=46.8 +lat_0=46.8 +lon_0=0 +k_0=0.99987742 '
...          '+x_0=600000 +y_0=2200000 +a=6378249.2 +b=6356515 '
...          '+towgs84=-168,-60,320,0,0,0,0 +pm=paris +units=m +no_defs'
...     )
... )
>>> crs = c.geospatial.create_coordinate_reference_systems(custom_crs)

Put raster data

GeospatialAPI.put_raster(feature_type_external_id: str, feature_external_id: str, raster_property_name: str, raster_format: str, raster_srid: int, file: str, allow_crs_transformation: bool = False, raster_scale_x: Optional[float] = None, raster_scale_y: Optional[float] = None) → cognite.client.data_classes.geospatial.RasterMetadata

Put raster <https://docs.cognite.com/api/v1/#tag/Geospatial/operation/putRaster>

Parameters:
  • feature_type_external_id – Feature type definition for the features to create.
  • feature_external_id – one feature or a list of features to create
  • raster_property_name – the raster property name
  • raster_format – the raster input format
  • raster_srid – the associated SRID for the raster
  • file – the path to the file of the raster
  • allow_crs_transformation – When the parameter is false, requests with rasters in Coordinate Reference System different from the one defined in the feature type will result in bad request response code.
  • raster_scale_x – the X component of the pixel width in units of coordinate reference system
  • raster_scale_y – the Y component of the pixel height in units of coordinate reference system
Returns:

the raster metadata if it was ingested succesfully

Return type:

RasterMetadata

Examples

Put a raster in a feature raster property:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> feature_type = ...
>>> feature = ...
>>> raster_property_name = ...
>>> metadata = c.geospatial.put_raster(feature_type.external_id, feature.external_id,
...         raster_property_name, "XYZ", 3857, file)

Delete raster data

GeospatialAPI.delete_raster(feature_type_external_id: str, feature_external_id: str, raster_property_name: str) → None

Delete raster <https://docs.cognite.com/api/v1/#tag/Geospatial/operation/deleteRaster>

Parameters:
  • feature_type_external_id – Feature type definition for the features to create.
  • feature_external_id – one feature or a list of features to create
  • raster_property_name – the raster property name
Returns:

None

Examples

Delete a raster in a feature raster property:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> feature_type = ...
>>> feature = ...
>>> raster_property_name = ...
>>> c.geospatial.delete_raster(feature_type.external_id, feature.external_id, raster_property_name)

Get raster data

GeospatialAPI.get_raster(feature_type_external_id: str, feature_external_id: str, raster_property_name: str, raster_format: str, raster_options: Dict[str, Any] = None, raster_srid: Optional[int] = None, raster_scale_x: Optional[float] = None, raster_scale_y: Optional[float] = None, allow_crs_transformation: bool = False) → bytes

Get raster <https://docs.cognite.com/api/v1/#tag/Geospatial/operation/getRaster>

Parameters:
  • feature_type_external_id – Feature type definition for the features to create.
  • feature_external_id – one feature or a list of features to create
  • raster_property_name – the raster property name
  • raster_format – the raster output format
  • raster_options – GDAL raster creation key-value options
  • raster_srid – the SRID for the output raster
  • raster_scale_x – the X component of the output pixel width in units of coordinate reference system
  • raster_scale_y – the Y component of the output pixel height in units of coordinate reference system
  • allow_crs_transformation – When the parameter is false, requests with output rasters in Coordinate Reference System different from the one defined in the feature type will result in bad request response code.
Returns:

the raster data

Return type:

bytes

Examples

Get a raster from a feature raster property:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> feature_type = ...
>>> feature = ...
>>> raster_property_name = ...
>>> raster_data = c.geospatial.get_raster(feature_type.external_id, feature.external_id,
...    raster_property_name, "XYZ", {"SIGNIFICANT_DIGITS": "4"})

Compute

GeospatialAPI.compute(output: Dict[str, cognite.client.data_classes.geospatial.GeospatialComputeFunction]) → cognite.client.data_classes.geospatial.GeospatialComputedResponse

Compute <https://docs.cognite.com/api/v1/#tag/Geospatial/operation/compute>

Parameters:output – Mapping of keys to compute functions.
Returns:Mapping of keys to computed items.
Return type:dict

Examples

Compute the transformation of an ewkt geometry from one SRID to another:

>>> from cognite.client import CogniteClient
>>> from cognite.client.data_classes.geospatial import GeospatialGeometryTransformComputeFunction, GeospatialGeometryValueComputeFunction
>>> c = CogniteClient()
>>> compute_function = GeospatialGeometryTransformComputeFunction(GeospatialGeometryValueComputeFunction("SRID=4326;POLYGON((0 0,10 0,10 10,0 10,0 0))"), srid=23031)
>>> compute_result = c.geospatial.compute(output = {"output": compute_function})

Data classes

class cognite.client.data_classes.geospatial.CoordinateReferenceSystem(srid: int = None, wkt: str = None, proj_string: str = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a feature in the geospatial api.

class cognite.client.data_classes.geospatial.CoordinateReferenceSystemList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.geospatial.Feature(external_id: str = None, cognite_client: CogniteClient = None, **properties)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a feature in the geospatial api.

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.geospatial.FeatureAggregate(cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A result of aggregating features in geospatial api.

class cognite.client.data_classes.geospatial.FeatureAggregateList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.geospatial.FeatureList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

static from_geopandas(feature_type: cognite.client.data_classes.geospatial.FeatureType, geodataframe: geopandas.GeoDataFrame, external_id_column: str = 'externalId', property_column_mapping: Dict[str, str] = None, data_set_id_column: str = 'dataSetId') → FeatureList

Convert a GeoDataFrame instance into a FeatureList.

Parameters:
  • feature_type (FeatureType) – The feature type the features will conform to
  • geodataframe (GeoDataFrame) – the geodataframe instance to convert into features
  • external_id_column – the geodataframe column to use for the feature external id
  • data_set_id_column – the geodataframe column to use for the feature dataSet id
  • property_column_mapping – provides a mapping from featuretype property names to geodataframe columns
Returns:

The list of features converted from the geodataframe rows.

Return type:

FeatureList

Examples

Create features from a geopandas dataframe:

>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> my_feature_type = ... # some feature type with 'position' and 'temperature' properties
>>> my_geodataframe = ...  # some geodataframe with 'center_xy', 'temp' and 'id' columns
>>> feature_list = FeatureList.from_geopandas(feature_type=my_feature_type, geodataframe=my_geodataframe,
>>>     external_id_column="id", data_set_id_column="dataSetId",
>>>     property_column_mapping={'position': 'center_xy', 'temperature': 'temp'})
>>> created_features = c.geospatial.create_features(my_feature_type.external_id, feature_list)
to_geopandas(geometry: str, camel_case: bool = False) → geopandas.GeoDataFrame

Convert the instance into a GeoPandas GeoDataFrame.

Parameters:
  • geometry (str) – The name of the feature type geometry property to use in the GeoDataFrame
  • camel_case (bool) – Convert column names to camel case (e.g. externalId instead of external_id)
Returns:

The GeoPandas GeoDataFrame.

Return type:

geopandas.GeoDataFrame

Examples

Convert a FeatureList into a GeoPandas GeoDataFrame:

>>> from cognite.client.data_classes.geospatial import PropertyAndSearchSpec
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> features = c.geospatial.search_features(...)
>>> gdf = features.to_geopandas(
...     geometry="position",
...     camel_case=False
... )
>>> gdf.head()
class cognite.client.data_classes.geospatial.FeatureType(external_id: str = None, data_set_id: int = None, created_time: int = None, last_updated_time: int = None, properties: Dict[str, Any] = None, search_spec: Dict[str, Any] = None, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of a feature type in the geospatial api.

class cognite.client.data_classes.geospatial.FeatureTypeList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

class cognite.client.data_classes.geospatial.FeatureTypePatch(external_id: Union[str, NoneType] = None, property_patches: Union[cognite.client.data_classes.geospatial.Patches, NoneType] = None, search_spec_patches: Union[cognite.client.data_classes.geospatial.Patches, NoneType] = None)

Bases: object

class cognite.client.data_classes.geospatial.FeatureTypeUpdate(external_id: str = None, add: cognite.client.data_classes.geospatial.PropertyAndSearchSpec = None, remove: cognite.client.data_classes.geospatial.PropertyAndSearchSpec = None, cognite_client: CogniteClient = None)

Bases: object

A representation of a feature type update in the geospatial api.

class cognite.client.data_classes.geospatial.GeospatialComputeFunction

Bases: abc.ABC

A geospatial compute function

to_json_payload() → dict

Convert function to json for request payload

class cognite.client.data_classes.geospatial.GeospatialComputedItem(resource: Dict[str, Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

A representation of an item computed from geospatial.

class cognite.client.data_classes.geospatial.GeospatialComputedItemList(resources: Collection[Any], cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResourceList

A list of items computed from geospatial.

class cognite.client.data_classes.geospatial.GeospatialComputedResponse(computed_item_list: cognite.client.data_classes.geospatial.GeospatialComputedItemList, cognite_client: CogniteClient = None)

Bases: cognite.client.data_classes._base.CogniteResource

The geospatial compute response.

class cognite.client.data_classes.geospatial.GeospatialGeometryComputeFunction

Bases: cognite.client.data_classes.geospatial.GeospatialComputeFunction, abc.ABC

A geospatial geometry compute function

class cognite.client.data_classes.geospatial.GeospatialGeometryTransformComputeFunction(geospatial_geometry_compute_function: cognite.client.data_classes.geospatial.GeospatialComputeFunction, srid: int)

Bases: cognite.client.data_classes.geospatial.GeospatialComputeFunction

A stTransform geospatial compute function

to_json_payload() → dict

Convert function to json for request payload

class cognite.client.data_classes.geospatial.GeospatialGeometryValueComputeFunction(ewkt: str)

Bases: cognite.client.data_classes.geospatial.GeospatialGeometryComputeFunction

A geospatial geometry value compute function. Accepts a well-known text of the geometry prefixed with a spatial reference identifier, see https://docs.geotools.org/stable/javadocs/org/opengis/referencing/doc-files/WKT.html

to_json_payload() → dict

Convert function to json for request payload

class cognite.client.data_classes.geospatial.OrderSpec(property: str, direction: str)

Bases: object

An order specification with respect to an property.

class cognite.client.data_classes.geospatial.Patches(add: Union[Dict[str, Any], NoneType] = None, remove: Union[List[str], NoneType] = None)

Bases: object

class cognite.client.data_classes.geospatial.PropertyAndSearchSpec(properties: Union[Dict[str, Any], List[str]] = None, search_spec: Union[Dict[str, Any], List[str]] = None)

Bases: object

A representation of a feature type property and search spec.

class cognite.client.data_classes.geospatial.RasterMetadata(**properties)

Bases: object

Raster metadata

cognite.client.data_classes.geospatial.nan_to_none(column_value: Any) → Any

Convert NaN value to None.

3D

Models

Retrieve a model by ID

ThreeDModelsAPI.retrieve(id: int) → Optional[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, Sequence[str]]) → Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelList]

Create new 3d models.

Parameters:name (Union[str, Sequence[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, Sequence[Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelUpdate]]]) → Union[cognite.client.data_classes.three_d.ThreeDModel, cognite.client.data_classes.three_d.ThreeDModelList]

Update 3d models.

Parameters:item (Union[ThreeDModel, ThreeDModelUpdate, Sequence[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, Sequence[int]]) → None

Delete 3d models.

Parameters:id (Union[int, Sequence[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) → Optional[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:

Optional[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, Sequence[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, Sequence[Union[cognite.client.data_classes.three_d.ThreeDModelRevision, cognite.client.data_classes.three_d.ThreeDModelRevisionUpdate]]]) → 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, Sequence[int]]) → None

Delete 3d model revisions.

Parameters:
  • model_id (int) – Delete the revision under the model with this id.
  • id (Union[int, Sequence[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, Sequence[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, Sequence[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, Sequence[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:
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, Sequence[cognite.client.data_classes.three_d.ThreeDAssetMapping]]) → None

Delete 3d node asset mappings.

Parameters:
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: CogniteClient = 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: Collection[Any], cognite_client: CogniteClient = None