# Getting started with the Python SDK

In this article:

Installation
Authenticate
Instantiate a new client
Discover time series
Plot time series
Create an asset hierarchy
Retrieve all events related to an asset subtree
What's next?

# Installation

To install this package:

pip install cognite-sdk

To upgrade the version of this package:

pip install cognite-sdk --upgrade

To install this package without the pandas and NumPy support:

pip install cognite-sdk-core

# Authenticate

The preferred way to authenticate against the Cognite API is by setting the COGNITE_API_KEY environment variable. All examples in this documentation require that the variable has been set.

$ export COGNITE_API_KEY = <your-api-key>

You can also pass your API key directly to the CogniteClient.

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

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# 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 through the COGNITE_CLIENT_NAME environment variable, or by passing it directly to the CogniteClient constructor.

All examples in this documentation assume that COGNITE_CLIENT_NAME has been set.

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

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Read more about the CogniteClient and the functionality it exposes.

# 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 returns the first 25 time series resources.

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

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# Plot time series

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

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

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

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

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

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

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NOTE

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

# Create an asset hierarchy

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

At the top of an asset hierarchy is a root asset (for example, the oil rig). 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)

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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. 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])

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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

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Assets are used to connect related data, even if the data comes from different sources. Time series of data points, events and files are all connected to one or more assets. A pump asset can be connected to a time series measuring pressure within the pump, as well as events recording maintenance operations, and a file with a 3D diagram of the pump.

To retrieve all events related to a given subtree of assets, we first fetch the subtree under an asset using the .subtree() method. This returns an AssetList object, which has a .events() method. This method return events related to any asset in the AssetList.

from cognite.client import CogniteClient
from cognite.client.data_classes import Asset
c = CogniteClient()
subtree_root_asset="some-external-id"
subtree = c.assets.retrieve(external_id=subtree_root_asset).subtree()
related_events = subtree.events()

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You can use the same pattern to retrieve all time series or files related to a set of assets.

related_files = subtree.files()
related_time_series = subtree.time_series()

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# What's next?

To learn more about the Python SDK, see the SDK reference documentation (opens new window).

# Getting started with the Python SDK

# Installation

# Authenticate

# Instantiate a new client

# Discover time series

# Plot time series

# Create an asset hierarchy

# Retrieve all events related to an asset subtree

# What's next?