Exploration Local Notebook

Prerequisites

Access to a CDF Project.
Know how to install and setup Python.
Launch a Python notebook.

Generating SDK for Your Data Model¶

pygen supports generating an SDK on the fly for easy exploration in a notebook

First, instansiate a CogniteClient. This can be done with any of the supported methods from the cognite-sdk:

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from cognite.client import CogniteClient
from cognite.client import CogniteClient

Service Principal in Microsoft Entra

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client = CogniteClient.default_oauth_client_credentials(
    project="<my-cdf-project>",
    cdf_cluster="<my-cdf-cluster>",
    tenant_id="<my_domain>.onmicrosoft.com",
    client_id="<my_service_principal_client_id>",
    client_secret="<my_service_principal_client_secret>",
)
client = CogniteClient.default_oauth_client_credentials(
    project="",
    cdf_cluster="",
    tenant_id=".onmicrosoft.com",
    client_id="",
    client_secret="",
)

Interactive Login Microsoft Entra

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client = CogniteClient.default_oauth_interactive(
    project="<my-cdf-project>",
    cdf_cluster="<my-cdf-cluster>",
    tenant_id="cognitepygen.onmicrosoft.com",
    client_id="<my_service_principal_client_id>",
)
client = CogniteClient.default_oauth_interactive(
    project="",
    cdf_cluster="",
    tenant_id="cognitepygen.onmicrosoft.com",
    client_id="",
)

Device Code

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from cognite.client import ClientConfig
from cognite.client.credentials import OAuthDeviceCode
from cognite.client import ClientConfig
from cognite.client.credentials import OAuthDeviceCode

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credentials = OAuthDeviceCode.default_for_azure_ad(
    tenant_id="cognitepygen.onmicrosoft.com",
    client_id="<device_client_id>",
    cdf_cluster="<my-cdf-cluster>",
)

client = CogniteClient(ClientConfig(client_name="client-name", project="<my-cdf-project>", credentials=credentials))
credentials = OAuthDeviceCode.default_for_azure_ad(
    tenant_id="cognitepygen.onmicrosoft.com",
    client_id="",
    cdf_cluster="",
)

client = CogniteClient(ClientConfig(client_name="client-name", project="", credentials=credentials))

For convenience pygen has a helper method for loading credentials from a toml file

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from cognite.pygen import generate_sdk_notebook, load_cognite_client_from_toml
from cognite.pygen import generate_sdk_notebook, load_cognite_client_from_toml

We have a local .toml file on the following format to create a CogniteClient

[cognite]
project = "<cdf-project>"
tenant_id = "<tenant-id>"
cdf_cluster = "<cdf-cluster>"
client_id = "<client-id>"
client_secret = "<client-secret>"

Then, we use the following function from pygen to create a `CogniteClient

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client = load_cognite_client_from_toml("config.toml")
client = load_cognite_client_from_toml("config.toml")

We can now create a SDK for the following data model.

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pygen = generate_sdk_notebook(
    ("sp_pygen_power", "WindTurbine", "1"),
    client,
)
pygen = generate_sdk_notebook(
    ("sp_pygen_power", "WindTurbine", "1"),
    client,
)

Successfully retrieved data model(s) ('sp_pygen_power', 'WindTurbine', '1')
Name collision detected in ViewId(space='sp_pygen_power', external_id='SensorTimeSeries', version='1'): 'type'. An underscore will be added to the 'type' to avoid name collision.
Writing SDK to C:\Users\ANDERS~1\AppData\Local\Temp\pygen\sp_pygen_power_WindTurbine_1\wind_turbine
Done!
Added C:\Users\ANDERS~1\AppData\Local\Temp\pygen\sp_pygen_power_WindTurbine_1 to sys.path to enable import
Imported wind_turbine
You can now use the generated SDK in the current Python session.
The data classes are available by importing, for example, `from wind_turbine.data_classes import BladeWrite`

The function above does the following:

Downloads the data model.
Generates an SDK.
Puts it in the temporary folder / pygen
Adds that folder to the sys.path
Imports the client
Instantiates then new client

Use Generated SDK¶

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pygen.wind_turbine.list(limit=5)
pygen.wind_turbine.list(limit=5)

Out[5]:

	space	external_id	name	capacity	windfarm	rotor	nacelle	blades	datasheets	data_record
0	sp_wind	hornsea_1_mill_3	hornsea_1_mill_3	7.0	Hornsea 1	{'space': 'sp_wind', 'external_id': 'hornsea_1...	{'space': 'sp_wind', 'external_id': 'hornsea_1...	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[{'space': 'sp_wind', 'external_id': 'windmill...	{'version': 4, 'last_updated_time': 2024-11-16...
1	sp_wind	hornsea_1_mill_2	hornsea_1_mill_2	7.0	Hornsea 1	{'space': 'sp_wind', 'external_id': 'hornsea_1...	{'space': 'sp_wind', 'external_id': 'hornsea_1...	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[{'space': 'sp_wind', 'external_id': 'windmill...	{'version': 4, 'last_updated_time': 2024-11-16...
2	sp_wind	hornsea_1_mill_1	hornsea_1_mill_1	7.0	Hornsea 1	{'space': 'sp_wind', 'external_id': 'hornsea_1...	{'space': 'sp_wind', 'external_id': 'hornsea_1...	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[{'space': 'sp_wind', 'external_id': 'windmill...	{'version': 4, 'last_updated_time': 2024-11-16...
3	sp_wind	hornsea_1_mill_4	hornsea_1_mill_4	7.0	Hornsea 1	{'space': 'sp_wind', 'external_id': 'hornsea_1...	{'space': 'sp_wind', 'external_id': 'hornsea_1...	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[{'space': 'sp_wind', 'external_id': 'windmill...	{'version': 4, 'last_updated_time': 2024-11-16...
4	sp_wind	hornsea_1_mill_5	hornsea_1_mill_5	7.0	Hornsea 1	{'space': 'sp_wind', 'external_id': 'hornsea_1...	{'space': 'sp_wind', 'external_id': 'hornsea_1...	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[{'space': 'sp_wind', 'external_id': 'windmill...	{'version': 4, 'last_updated_time': 2024-11-16...

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pygen.metmast.list()
pygen.metmast.list()

Out[7]:

	space	external_id	position	wind_speed	tilt_angle	temperature	data_record
0	sp_wind	utsira_station	62.0	utsira_station_wind_speed	utsira_station_tilt_angle	utsira_station_temperature	{'version': 1, 'last_updated_time': 2024-11-16...
1	sp_wind	hitra_station	63.0	hitra_station_wind_speed	hitra_station_tilt_angle	hitra_station_temperature	{'version': 1, 'last_updated_time': 2024-11-16...
2	sp_wind	smola_station	64.0	smola_station_wind_speed	smola_station_tilt_angle	smola_station_temperature	{'version': 1, 'last_updated_time': 2024-11-16...

To see a more complete example of functionality for the generated client see the Usage section in the documentation.

Generating SDK using Demo Data Model¶

In case you want to try out pygen, but do not have a data model with data available, pygen comes with a demo data model included in the package.

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from cognite.pygen import demo, load_cognite_client_from_toml
from cognite.pygen import demo, load_cognite_client_from_toml

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solar = demo.SolarFarmAPM()
client = load_cognite_client_from_toml()
solar = demo.SolarFarmAPM()
client = load_cognite_client_from_toml()

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%%time
pygen = solar.create(client)
%%time
pygen = solar.create(client)

Data model DataModelId(space='pygen', external_id='SolarFarmAPM', version='1') already exists, skipping deployment
✅  Data Model Ready!
Skipping TimeSeries creation, all 4 TimeSeries already exist
Skipping FileMetadata creation, all 3 FileMetadata already exist
All 17 nodes already exists
All 21 edges already exists
✅  Population Complete!
Writing SDK to C:\Users\ANDERS~1\AppData\Local\Temp\pygen
Done!
Added C:\Users\ANDERS~1\AppData\Local\Temp\pygen to sys.path to enable import
Imported solar_farm_apm.client
✅  SDK Generated!
CPU times: total: 469 ms
Wall time: 3.41 s

Use Generated SDK¶

We can now inspect the data model using our newly generated SDK.

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pygen.assets.list()
pygen.assets.list()

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	external_id	area_id	category_id	created_date	description	documents	is_active	is_critical_line	linked_assets	metrics	name	parent	source_db	updated_date	version	last_updated_time	created_time	deleted_time
0	solarfarm:fornebu	1	1	2017-01-01 00:00:00+00:00	The most sunny place in Norway	[]	True	True	[]	[]	Fornebu	None	AlbertCerebrum	2023-01-01 00:00:00+00:00	2	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
1	solarpanel:sunny	2	2	2017-01-01 00:00:00+00:00	The panel on the first building	[]	True	False	[inverter:sunny]	[]	Solar Panel Sunny	solarfarm:fornebu	AlbertCerebrum	2022-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
2	solarpanel:cloudy	3	2	2020-01-01 00:00:00+00:00	The panel on the second building	[]	True	True	[inverter:cloudy]	[]	Solar Panel Cloudy	solarfarm:fornebu	AlbertCerebrum	2022-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
3	transformer:sunny	2	3	2017-01-01 00:00:00+00:00	The transformer connected to sunny	[]	True	False	[substation:fornebu]	[]	Transformer Sunny	solarfarm:fornebu	AlbertCerebrum	2022-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
4	transformer:cloudy	3	3	2017-01-01 00:00:00+00:00	The transformer connected to cloudy	[]	True	False	[substation:fornebu]	[]	Transformer Cloudy	solarfarm:fornebu	AlbertCerebrum	2021-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
5	transformer:saver	4	3	2022-01-01 00:00:00+00:00	The transformer connected to battery	[]	True	True	[substation:fornebu]	[]	Transformer Saver	solarfarm:fornebu	AlbertCerebrum	2023-03-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
6	inverter:sunny	2	4	2017-01-01 00:00:00+00:00	The inverter connected to sunny	[]	True	False	[transformer:sunny]	[]	Inverter Sunny	solarfarm:fornebu	AlbertCerebrum	2022-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
7	inverter:cloudy	3	4	2017-01-01 00:00:00+00:00	The inverter connected to cloudy	[]	True	False	[transformer:cloudy]	[]	Inverter Cloudy	solarfarm:fornebu	AlbertCerebrum	2021-01-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
8	inverter:saver	4	4	2022-01-01 00:00:00+00:00	The inverter connected to battery	[]	True	True	[transformer:saver]	[]	Inverter Saver	solarfarm:fornebu	AlbertCerebrum	2023-03-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
9	substation:fornebu	1	4	2017-01-01 00:00:00+00:00	The substation for the solar farm	[]	True	True	[]	[]	Fornebu station	solarfarm:fornebu	AlbertCerebrum	2023-03-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None
10	battery:winter	4	5	2020-01-01 00:00:00+00:00	The battery for the solar farm	[]	True	True	[inverter:saver]	[]	Saver	solarfarm:fornebu	AlbertCerebrum	2020-06-01 00:00:00+00:00	1	2023-08-20 07:48:17.799000+00:00	2023-08-20 07:48:17.799000+00:00	None

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pygen.work_orders.retrieve("planned:1001")
pygen.work_orders.retrieve("planned:1001")

Out[5]:

WorkOrder(external_id='planned:1001', version=2, last_updated_time=datetime.datetime(2023, 8, 20, 7, 48, 17, 799000, tzinfo=TzInfo(UTC)), created_time=datetime.datetime(2023, 8, 20, 7, 48, 17, 799000, tzinfo=TzInfo(UTC)), deleted_time=None, actual_hours=3, created_date=datetime.datetime(2023, 1, 1, 0, 0, tzinfo=TzInfo(UTC)), description='Inspect solar panels', due_date=datetime.datetime(2023, 1, 1, 0, 0, tzinfo=TzInfo(UTC)), duration_hours=5, end_time=datetime.datetime(2023, 1, 2, 0, 0, tzinfo=TzInfo(UTC)), is_active=False, is_cancelled=False, is_completed=True, is_safety_critical=False, linked_assets=['solarpanel:sunny', 'solarpanel:cloudy'], percentage_progress=100, planned_start=datetime.datetime(2023, 1, 1, 0, 0, tzinfo=TzInfo(UTC)), priority_description='Medium Planned Priority', program_number='1', start_time=datetime.datetime(2023, 1, 1, 0, 0, tzinfo=TzInfo(UTC)), status='Done', title='Planned Inspection', work_items=['work:inspection:1001', 'work:cleaning:1001'], work_order_number='1', work_package_number='1')

Once we are finished exploting pygen using the demo data we can remove it with the .clean() method

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solar.clean(client)
solar.clean(client)

About to delete data model DataModelId(space='pygen', external_id='SolarFarmAPM', version='1')
About to delete views [ViewId(space='pygen', external_id='WorkItem', version='e3b9053e1dd01e'), ViewId(space='pygen', external_id='WorkOrder', version='27f589c97cdb01'), ViewId(space='pygen', external_id='Asset', version='a974167b65241a')]
About to delete containers [ContainerId(space='pygen', external_id='Asset'), ContainerId(space='pygen', external_id='WorkOrder'), ContainerId(space='pygen', external_id='WorkItem')] along with all nodes and edges
About to delete space pygen

Deleted 4 TimeSeries
Deleted 3 FileMetadata
Deleted 17 nodes
Deleted 0 edges
Deleted 4 type nodes
Deleted data model DataModelId(space='pygen', external_id='SolarFarmAPM', version='1')
Deleted views [ViewId(space='pygen', external_id='WorkItem', version='e3b9053e1dd01e'), ViewId(space='pygen', external_id='WorkOrder', version='27f589c97cdb01'), ViewId(space='pygen', external_id='Asset', version='a974167b65241a')]
Deleted containers [ContainerId(space='pygen', external_id='Asset'), ContainerId(space='pygen', external_id='WorkOrder'), ContainerId(space='pygen', external_id='WorkItem')]
Deleted space pygen

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