Exploration in CDF Notebook¶

Prerequisites

• Access to a CDF Project.
• Some very basic Python understanding or feel up for a challenge.

If you have ZERO Python knowledge and are up for a challenge¶

• You can copy the cells below and past them into notebook.
• You execute cells by pressing shift+enter
• Auto complete with tab.
• You can check available commands by first type a variable and press shift+tab. For example, after running the line pygen = generate_sdk_notebook(data_model_id, client) you can in subsequent cells write pygen. and press shift+tab to see that there are options such as wind_turbine and metmast available .

Installing pygen in the notebook environment.¶

In [1]:

%pip install cognite-pygen

%pip install cognite-pygen

In case you have issues with the installation, check out the troubleshooting in the installation guide.

Generating SDK for Your Data Model¶

In [2]:

from cognite.client import CogniteClient

from cognite.pygen import __version__, generate_sdk_notebook

from cognite.client import CogniteClient from cognite.pygen import __version__, generate_sdk_notebook

In [3]:

# Show version, if you have an older you can run
# the installation cell above with:
# %pip install cognite-pygen==0.32.5
__version__

# Show version, if you have an older you can run # the installation cell above with: # %pip install cognite-pygen==0.32.5 __version__

Out[3]:

'0.99.49'

In [4]:

client = CogniteClient()

client = CogniteClient()

In [5]:

space = "sp_pygen_power"
data_model_external_id = "WindTurbine"
data_model_version = "1"
data_model_id = (space, data_model_external_id, data_model_version)

space = "sp_pygen_power" data_model_external_id = "WindTurbine" data_model_version = "1" data_model_id = (space, data_model_external_id, data_model_version)

In [6]:

pygen = generate_sdk_notebook(data_model_id, client)

pygen = generate_sdk_notebook(data_model_id, 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 /tmp/pygen/sp_pygen_power_WindTurbine_1/wind_turbine
Done!
Added /tmp/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`

Using the Generated SDK¶

In [7]:

pygen.wind_turbine.list(limit=5)

pygen.wind_turbine.list(limit=5)

Out[7]:

	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...

In [8]:

pygen.metmast.list(limit=2)

pygen.metmast.list(limit=2)

Out[8]:

	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...

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.

In [9]:

from cognite.client import CogniteClient

from cognite.pygen import demo

from cognite.client import CogniteClient from cognite.pygen import demo

In [10]:

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

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

In [11]:

%%time
farm = solar.create(client)

%%time farm = solar.create(client)

Created space pygen

Deployed data model DataModelId(space='pygen', external_id='SolarFarmAPM', version='1')

✅  Data Model Ready!

Created 4 TimeSeries

Created 3 FileMetadata

Created 17 nodes

Created 21 edges

✅  Population Complete!

Writing SDK to /tmp/pygen

Done!

/tmp/pygen already in sys.path

Imported solar_farm_apm.client

✅  SDK Generated!

CPU times: user 0 ns, sys: 0 ns, total: 0 ns

Wall time: 5.32 s

Use Generated SDK¶

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

In [12]:

farm.assets.list()

farm.assets.list()

Out[12]:

	external_id	area_id	category_id	created_date	description	documents	is_active	is_critical_line	linked_assets	metrics	name	parent	source_db	tag	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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+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 10:41:56.033000+00:00	2023-08-20 10:41:56.033000+00:00	None

In [13]:

farm.work_orders.retrieve("planned:1001").dict()

farm.work_orders.retrieve("planned:1001").dict()

Out[13]:

{'external_id': 'planned:1001',
 'version': 2,
 'last_updated_time': datetime.datetime(2023, 8, 20, 10, 41, 56, 33000, tzinfo=datetime.timezone.utc),
 'created_time': datetime.datetime(2023, 8, 20, 10, 41, 56, 33000, tzinfo=datetime.timezone.utc),
 'deleted_time': None,
 'actual_hours': 3,
 'created_date': datetime.datetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc),
 'description': 'Inspect solar panels',
 'due_date': datetime.datetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc),
 'duration_hours': 5,
 'end_time': datetime.datetime(2023, 1, 2, 0, 0, tzinfo=datetime.timezone.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=datetime.timezone.utc),
 'priority_description': 'Medium Planned Priority',
 'program_number': '1',
 'start_time': datetime.datetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc),
 'status': 'Done',
 'title': 'Planned Inspection',
 'work_items': ['work:inspection:1001', 'work:cleaning:1001'],
 'work_order_number': '1',
 'work_package_number': '1'}

Cleaning Up¶

Once you are finished with testing and want to remove the data model and data, you can use the .clean() method.

In [14]:

solar.clean(client, auto_confirm=True)

solar.clean(client, auto_confirm=True)

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='WorkOrder'), ContainerId(space='pygen', external_id='Asset'), ContainerId(space='pygen', external_id='WorkItem')]

Deleted space pygen

In [ ]: