Selecting / Querying¶

We assume that you have generated a SDK for the WindTurbine model and have a client ready to go.

Selecting/Querying is a different way of retrieving data than doing .list(), .retrieve(), search() in that it supports retrieving nested data structures, including filtering anywhere on the retrieved data structure. SDKs generated by pygen have two ways of selecting/querying which we will refer to as Python and GraphQL qurying. In this section, we will go through both of them, and list limitations as well as when to use one over the other.

In [2]:

from wind_turbine import WindTurbineClient

from wind_turbine import WindTurbineClient

In [3]:

pygen = WindTurbineClient.from_toml("config.toml")

pygen = WindTurbineClient.from_toml("config.toml")

Comparison¶

Python Based Querying/Selecting¶

Advantages

• Discovarability through the IDE through autocompletion.
• The returning data classes have required/optional set as in the data model, which are useful for static type checking and IDE auto complete.
• Regular filtering has same syntax as querying along any edge, direct relation, or reverse direct relation.

Limitations

• You can only query along one chain of connections. For example, if we start from WindTurbine above we can either go to blades or metmast, not both.

GraphQL Based Querying¶

Advantages

• Flexiblity in querying. You can retrieve anything you can write up as a graphql query.

Limitations

• Difficult to write the querying, typically you would use the CDF UI to create the query.
• The returning data class has all properties as optional and is thus less structured.
• Can become very verbose when you want to retrieve a lot of properties.

Summary¶

GraphQL based querying is more flexible, but gives you less structure on the returning data classes. In addition, the query will have to be created outside of your IDE which requires context switching.

Python Based Querying¶

Examples¶

Query: All turbines with nacelle:

In [4]:

result = pygen.wind_turbine.select().nacelle.list_full()
result

result = pygen.wind_turbine.select().nacelle.list_full() result

Out[4]:

	space	external_id	capacity	name	blades	datasheets	nacelle	rotor	windfarm	instanceType	data_record
0	sp_wind	hornsea_1_mill_3	7.0	hornsea_1_mill_3	[hornsea_1_mill_3_blade_A, hornsea_1_mill_3_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_3_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
1	sp_wind	hornsea_1_mill_2	7.0	hornsea_1_mill_2	[hornsea_1_mill_2_blade_B, hornsea_1_mill_2_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_2_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
2	sp_wind	hornsea_1_mill_1	7.0	hornsea_1_mill_1	[hornsea_1_mill_1_blade_A, hornsea_1_mill_1_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_1_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
3	sp_wind	hornsea_1_mill_4	7.0	hornsea_1_mill_4	[hornsea_1_mill_4_blade_C, hornsea_1_mill_4_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_4_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
4	sp_wind	hornsea_1_mill_5	7.0	hornsea_1_mill_5	[hornsea_1_mill_5_blade_B, hornsea_1_mill_5_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_5_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...

Query: Get all blades for the windturbine named "hornsea_1_mill_1"

In [5]:

result = pygen.wind_turbine.select().name.equals("hornsea_1_mill_1").blades.list_blade()
result

result = pygen.wind_turbine.select().name.equals("hornsea_1_mill_1").blades.list_blade() result

Out[5]:

	space	external_id	is_damaged	name	instanceType	data_record
0	sp_wind	hornsea_1_mill_1_blade_A	True	A	node	{'version': 1, 'last_updated_time': 2024-11-16...
1	sp_wind	hornsea_1_mill_1_blade_B	False	B	node	{'version': 1, 'last_updated_time': 2024-11-16...
2	sp_wind	hornsea_1_mill_1_blade_C	False	C	node	{'version': 1, 'last_updated_time': 2024-11-16...

Query: Get all blades for the windmill with external ID "hornsea_1_mill_3" or "hornsea_1_mill_4" with a damaged blade

In [6]:

result = (
    pygen.wind_turbine.select()
    .external_id.in_(["hornsea_1_mill_3", "hornsea_1_mill_4"])
    .blades.is_damaged.equals(True)
    .list_blade()
)
result

result = ( pygen.wind_turbine.select() .external_id.in_(["hornsea_1_mill_3", "hornsea_1_mill_4"]) .blades.is_damaged.equals(True) .list_blade() ) result

Out[6]:

	space	external_id	is_damaged	name	instanceType	data_record
0	sp_wind	hornsea_1_mill_4_blade_C	True	C	node	{'version': 1, 'last_updated_time': 2024-11-16...

Same query as above but return the turbines

In [7]:

result = (
    pygen.wind_turbine.select()
    .external_id.in_(["hornsea_1_mill_3", "hornsea_1_mill_4"])
    .blades.is_damaged.equals(True)
    .list_full()
)
result

result = ( pygen.wind_turbine.select() .external_id.in_(["hornsea_1_mill_3", "hornsea_1_mill_4"]) .blades.is_damaged.equals(True) .list_full() ) result

Out[7]:

	space	external_id	capacity	name	blades	datasheets	nacelle	rotor	windfarm	instanceType	data_record
0	sp_wind	hornsea_1_mill_4	7.0	hornsea_1_mill_4	[{'space': 'sp_wind', 'external_id': 'hornsea_...	[windmill_schematics]	hornsea_1_mill_4_nacelle	hornsea_1_mill_4_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...

We can also inspect the query we are doing:

In [13]:

pygen.wind_turbine.select().nacelle

pygen.wind_turbine.select().nacelle

Out[13]:

Query

Call .list_full() to return a list of Windturbine and .list_nacelle() to return a list of Nacelle.

In the query above, we go from WindTurbine node through the nacelle direct relation to the Nacelle node.

Autocomplete¶

Warning Notebook IDE

The autocomplete shown in the screenshots below works best in a jupyter notebook IDE. For example, VS Code notebook/PyCharm notebooks/Jupyter Lite (CDF Notebook) you might not get the same autocomplete when writing . + tab.

This style of querying is initiated by calling the .select() method on the place we want to start the query. Then, all properties that pygen supports filtering on are available as an attribute on the returning object of query. In addition, all types of connections (edges, direct relations, and reverse direct relations) can be traversed. Illustrated in the screenshot below. To get the list of possible options write . and press tab.

We can select a properties, and depending on the type of property, we will get the available filters up. For example, name is a string property which will make equals, in and prefix filter available.

Ones we have input the filterin values we are back at the source node and can continue to filter on properties of this node or traverse do the next one. In the example below, we traverse to the nacelle:

To make the query more readable it is recommended that you use the paranthesis syntax. This makes the query more readable.

Finally, finish with either .list_full() or .list_<type>() to return the all nodes and edges in the query, or only the last node.

In [14]:

result = (
    pygen.wind_turbine.select()
    .capacity.range(6.0, 8.0)
    .name.prefix("hornsea")
    .blades.is_damaged.equals(True)
    .list_blade()
)
result

result = ( pygen.wind_turbine.select() .capacity.range(6.0, 8.0) .name.prefix("hornsea") .blades.is_damaged.equals(True) .list_blade() ) result

Out[14]:

	space	external_id	is_damaged	name	instanceType	data_record
0	sp_wind	hornsea_1_mill_1_blade_A	True	A	node	{'version': 1, 'last_updated_time': 2024-11-16...
1	sp_wind	hornsea_1_mill_4_blade_C	True	C	node	{'version': 1, 'last_updated_time': 2024-11-16...
2	sp_wind	hornsea_1_mill_2_blade_B	True	B	node	{'version': 1, 'last_updated_time': 2024-11-16...

Sorting¶

You can also sort the results returned by the .select() method. This is done by the .sort_ascending() and .sort_descending() property you are sorting.

Query: Get all damaged blades connected to turbines with capacity between 6.0 and 8.0 and a names that starts with "hornsea". Sort the blades by name

In [15]:

result = (
    pygen.wind_turbine.select()
    .capacity.range(6.0, 8.0)
    .name.prefix("hornsea")
    .blades.is_damaged.equals(True)
    .name.sort_ascending()
    .list_blade()
)
result

result = ( pygen.wind_turbine.select() .capacity.range(6.0, 8.0) .name.prefix("hornsea") .blades.is_damaged.equals(True) .name.sort_ascending() .list_blade() ) result

Out[15]:

	space	external_id	is_damaged	name	instanceType	data_record
0	sp_wind	hornsea_1_mill_1_blade_A	True	A	node	{'version': 1, 'last_updated_time': 2024-11-16...
1	sp_wind	hornsea_1_mill_2_blade_B	True	B	node	{'version': 1, 'last_updated_time': 2024-11-16...
2	sp_wind	hornsea_1_mill_4_blade_C	True	C	node	{'version': 1, 'last_updated_time': 2024-11-16...

In addition, for properties of type TimeStampand Date you can use .latest() and .earliest(). This is a shorthand for calling .sort_descending()/.sort_decending() + setting limit=1.

Query: Get the latest uploaded datasheet for the "hornsea_1_mill_1" turbine

In [16]:

result = pygen.wind_turbine.select().name.equals("hornsea_1_mill_1").datasheets.uploaded_time.latest().list_data_sheet()
result

result = pygen.wind_turbine.select().name.equals("hornsea_1_mill_1").datasheets.uploaded_time.latest().list_data_sheet() result

Out[16]:

	space	external_id	is_uploaded	mime_type	name	uploaded_time	instanceType	data_record
0	sp_wind	windmill_schematics	True	application/pdf	windmill_schematics.pdf	2024-11-16 14:19:28.484000+00:00	node	{'version': 2, 'last_updated_time': 2024-11-16...

Direct Relations¶

We can both traverse and filter on connections that are modeled as a direct relations. pygen distinguish these by appending _filter to the attribute when filtering and nothing when traversing.

Query: Get all wind turbines with nacelles.

In [17]:

result = pygen.wind_turbine.select().nacelle.list_full(limit=5)
result

result = pygen.wind_turbine.select().nacelle.list_full(limit=5) result

Out[17]:

	space	external_id	capacity	name	blades	datasheets	nacelle	rotor	windfarm	instanceType	data_record
0	sp_wind	hornsea_1_mill_3	7.0	hornsea_1_mill_3	[hornsea_1_mill_3_blade_A, hornsea_1_mill_3_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_3_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
1	sp_wind	hornsea_1_mill_2	7.0	hornsea_1_mill_2	[hornsea_1_mill_2_blade_B, hornsea_1_mill_2_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_2_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
2	sp_wind	hornsea_1_mill_1	7.0	hornsea_1_mill_1	[hornsea_1_mill_1_blade_A, hornsea_1_mill_1_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_1_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
3	sp_wind	hornsea_1_mill_4	7.0	hornsea_1_mill_4	[hornsea_1_mill_4_blade_C, hornsea_1_mill_4_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_4_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...
4	sp_wind	hornsea_1_mill_5	7.0	hornsea_1_mill_5	[hornsea_1_mill_5_blade_B, hornsea_1_mill_5_bl...	[windmill_schematics]	{'space': 'sp_wind', 'external_id': 'hornsea_1...	hornsea_1_mill_5_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...

In [18]:

result[0].nacelle

result[0].nacelle

Out[18]:

	value
space	sp_wind
external_id	hornsea_1_mill_3_nacelle
data_record	{'version': 1, 'last_updated_time': 2024-11-16...
node_type	None
acc_from_back_side_x	V52-WindTurbine.Acc1N
acc_from_back_side_y	V52-WindTurbine.Acc2N
acc_from_back_side_z	V52-WindTurbine.Acc3N
gearbox	hornsea_1_mill_3_nacelle_gearbox
generator	hornsea_1_mill_3_nacelle_generator
high_speed_shaft	hornsea_1_mill_3_nacelle_highspeedshaft
main_shaft	hornsea_1_mill_3_nacelle_mainshaft
power_inverter	hornsea_1_mill_3_nacelle_powerinverter
wind_turbine	None
yaw_direction	V52-WindTurbine.yaw
yaw_error	V52-WindTurbine.YawErr
instanceType	node

Query: Get the wind turibne with the nacelle hornsea_1_mill_3_nacelle

In [19]:

result = pygen.wind_turbine.select().nacelle_filter.equals("hornsea_1_mill_3_nacelle").list_full(limit=5)
result

result = pygen.wind_turbine.select().nacelle_filter.equals("hornsea_1_mill_3_nacelle").list_full(limit=5) result

Out[19]:

	space	external_id	capacity	name	blades	datasheets	nacelle	rotor	windfarm	instanceType	data_record
0	sp_wind	hornsea_1_mill_3	7.0	hornsea_1_mill_3	[hornsea_1_mill_3_blade_A, hornsea_1_mill_3_bl...	[windmill_schematics]	hornsea_1_mill_3_nacelle	hornsea_1_mill_3_rotor	Hornsea 1	node	{'version': 19, 'last_updated_time': 2025-02-1...

Max Selection Depth (Advanced)¶

When you call .select() pygen constructs a data structure that explores all path starting from the location you called select until a maximum depth is reached. If you try to select a path that is longer than this maximum you will get the following error.

In [22]:

try:
    pygen.wind_turbine.select().nacelle.gearbox.displacement_x.list_sensor_time_series()
except ValueError as e:
    print(e)

try: pygen.wind_turbine.select().nacelle.gearbox.displacement_x.list_sensor_time_series() except ValueError as e: print(e)

Max select depth reached. Cannot query deeper than 3.
You can increase the max_select_depth in the global config.
```
from omni.config import global_config

global_config.max_select_depth = 4
```

As we see in the hint, we can increase this limit by setting the global_config.

Caveat: If you set this to a high value, the data structure pygen creates when calling .select() can become very large. It increases with the number of connections you have in the model, which means for a highly connected model the memory needed will increase exponentially with the depth you set. If you get a MemoryErrorException that is typically as a result of a too high max selection depth.

In [23]:

from wind_turbine.config import global_config

from wind_turbine.config import global_config

In [25]:

global_config.max_select_depth = 4

global_config.max_select_depth = 4

In [26]:

pygen.wind_turbine.select().nacelle.gearbox.displacement_x.list_sensor_time_series()

pygen.wind_turbine.select().nacelle.gearbox.displacement_x.list_sensor_time_series()

Out[26]:

	space	external_id	aliases	concept_id	description	is_step	name	source_unit	standard_name	type_	instanceType	data_record
0	sp_wind	V52-WindTurbine.Gear_D_X	[Gearbox Displacement]	http://purl.org/aspect/gearbox_displacement	Gearbox Displacement X (laser sensor) (not cal...	False	Gear_D_X	V	gearbox_displacement	numeric	node	{'version': 1, 'last_updated_time': 2024-11-16...

GraphQL based Querying¶

When querying with GraphQL we must include __typename of the top level items as this is used by pygen to understand how to pase the object.

The querying method is available on the top level client as this is not particular to any of the data types in your data model

In [15]:

my_query = """{
  listWindTurbine(first:1){
    items{
      __typename
      name
      capacity
      nacelle{
        externalId
      }
      rotor{
        externalId
      }
      blades{
        items{
          name
          is_damaged
        }
      }
    }
  }
}"""

my_query = """{ listWindTurbine(first:1){ items{ __typename name capacity nacelle{ externalId } rotor{ externalId } blades{ items{ name is_damaged } } } } }"""

In [16]:

result = pygen.graphql_query(my_query)

result = pygen.graphql_query(my_query)

In [17]:

result

result

Out[17]:

	blades	capacity	nacelle	name	rotor
0	[{'space': None, 'external_id': None, 'data_re...	7.0	{'space': None, 'external_id': 'hornsea_1_mill...	hornsea_1_mill_3	{'space': None, 'external_id': 'hornsea_1_mill...

In [18]:

result[0].model_dump(exclude_none=True)

result[0].model_dump(exclude_none=True)

Out[18]:

{'blades': [{'is_damaged': False, 'name': 'A'},
  {'is_damaged': False, 'name': 'B'},
  {'is_damaged': False, 'name': 'C'}],
 'capacity': 7.0,
 'nacelle': {'external_id': 'hornsea_1_mill_3_nacelle'},
 'name': 'hornsea_1_mill_3',
 'rotor': {'external_id': 'hornsea_1_mill_3_rotor'}}

In [19]:

turbine = result[0]

turbine = result[0]

In [20]:

turbine.nacelle.external_id

turbine.nacelle.external_id

Out[20]:

'hornsea_1_mill_3_nacelle'

Pitfalls¶

If you forget to include __typename on the top level item pygen will raise a Runtime error

In [22]:

my_invalid_query = """
{
  listWindTurbine(first:1){
    items{
      name
  }
 }
}
"""

my_invalid_query = """ { listWindTurbine(first:1){ items{ name } } } """

In [23]:

try:
    pygen.graphql_query(my_invalid_query)
except RuntimeError as e:
    print(e)

try: pygen.graphql_query(my_invalid_query) except RuntimeError as e: print(e)

Missing '__typename' in GraphQL response. Cannot determine the type of the response.

Data Classes¶

When you call .list(), .retrieve() and .search(), pygen returns ther read format of data classes. This read format matches the type/view required/optional properties.

When you do the graphql_query above all properties are optional as pygen cannot know which objects you included in your query, thus pygen uses a special GraphQL format of the data class it is returning

In [24]:

my_query = """{
  listWindTurbine(first:1){
    items{
      name
      __typename
  }
 }
}"""

my_query = """{ listWindTurbine(first:1){ items{ name __typename } } }"""

In [25]:

result = pygen.graphql_query(my_query)

result = pygen.graphql_query(my_query)

In [26]:

type(result)

type(result)

Out[26]:

wind_turbine.data_classes._core.base.GraphQLList

In [27]:

type(result[0])

type(result[0])

Out[27]:

wind_turbine.data_classes._wind_turbine.WindTurbineGraphQL

This data class can be converted to a regular write or read format by calling the as_write and as_read call.

Warning If you have not included all required properties in the your GraphQL query, pygen will raise an ValueError when you do this call.

In [28]:

try:
    result[0].as_read()
except ValueError as e:
    print(e)

try: result[0].as_read() except ValueError as e: print(e)

2 validation errors for WindTurbine
externalId
  Field required [type=missing, input_value={'name': 'hornsea_1_mill_3'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/missing
data_record
  Field required [type=missing, input_value={'name': 'hornsea_1_mill_3'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/missing

In [29]:

my_query = """{
  listWindTurbine(first:1){
    items{
      name
      space
      externalId
      createdTime
      lastUpdatedTime
      __typename
  }
 }
}"""

my_query = """{ listWindTurbine(first:1){ items{ name space externalId createdTime lastUpdatedTime __typename } } }"""

In [30]:

result = pygen.graphql_query(my_query)

result = pygen.graphql_query(my_query)

In [31]:

windmill_read = result[0].as_read()
windmill_read

windmill_read = result[0].as_read() windmill_read

Out[31]:

	value
space	sp_wind
external_id	hornsea_1_mill_3
data_record	{'version': 0, 'last_updated_time': 2024-12-17...
node_type	None
capacity	None
description	None
name	hornsea_1_mill_3
blades	None
datasheets	None
metmast	None
nacelle	None
power_curve	None
rotor	None
windfarm	None

In [32]:

type(windmill_read)

type(windmill_read)

Out[32]:

wind_turbine.data_classes._wind_turbine.WindTurbine

In [33]:

windmill_write = result[0].as_write()
windmill_write

windmill_write = result[0].as_write() windmill_write

Out[33]:

	value
space	sp_wind
external_id	hornsea_1_mill_3
data_record	{'existing_version': 0}
node_type	None
capacity	None
description	None
name	hornsea_1_mill_3
blades	None
datasheets	None
metmast	None
nacelle	None
power_curve	None
rotor	None
windfarm	None

In [34]:

type(windmill_write)

type(windmill_write)

Out[34]:

wind_turbine.data_classes._wind_turbine.WindTurbineWrite

Paging¶

If we include a pageInfo in our query this will be available directly on the result returned from the .graphql_query method

In [35]:

my_query = """
{
  listWindTurbine{
    items{
      __typename
      name
  }
	pageInfo{
    hasNextPage
    hasPreviousPage
    startCursor
    endCursor
  }
 }
}"""

my_query = """ { listWindTurbine{ items{ __typename name } pageInfo{ hasNextPage hasPreviousPage startCursor endCursor } } }"""

In [36]:

result = pygen.graphql_query(my_query)

result = pygen.graphql_query(my_query)

In [37]:

result.page_info.has_next_page

result.page_info.has_next_page

Out[37]:

True

In [40]:

result.page_info.end_cursor

result.page_info.end_cursor

Out[40]:

'Z0FBQUFBQm5jcVdlTGd2ZmRFdVNrLW1PUnFvaEl0RV8xaGU4cU95d2o1SWFQdWxFZVVyeTZ4OEhKR09kNjIzMTh4b1BYU09jZHI1bkhRN05rMDkzUWhubnFkRVhVNjB0bUZkd0JFQ3BzS0l6ZVBuRUdIQXRGX0tza2tTVVhlWkV6cUMxVGdyX2t1aEk4S1Bvd0Y0aVJnUDk0NU41d2M1R3pFV2ZnelpOS1J6RDkxTWRiUTY2SHB2Y0ZEc05MVElLbk9WQjNaUERtaE1DRlY4akhWSmhZWmJHZzhVaEFLdm5rQjFHbU9vNFRFQm5BUG90ZXNlNHhPQm5HUEZzajJZSmhJNjFpVFYzcEpkVQ=='

Datapoints¶

You can use the getDatapoints to include datapoints for a referenced timeseries.

In [49]:

my_query = """{
  listMetmast(first: 1){
    items{
      __typename
      temperature{
        externalId
        name
        getDataPoints(granularity: "1d", aggregates: SUM, first: 100){
          items{
            timestamp
            sum
          }
        }
      }
    }
  }
}"""

my_query = """{ listMetmast(first: 1){ items{ __typename temperature{ externalId name getDataPoints(granularity: "1d", aggregates: SUM, first: 100){ items{ timestamp sum } } } } } }"""

In [50]:

result = pygen.graphql_query(my_query)
metmast = result[0]

result = pygen.graphql_query(my_query) metmast = result[0]

In [51]:

metmast.temperature.data.to_pandas()

metmast.temperature.data.to_pandas()

Out[51]:

	utsira_station_temperature|sum
2020-12-31	-9.844667
2021-01-01	-254.494219
2021-01-02	-495.438664
2021-01-03	-588.549775
2021-01-04	-602.327553
...	...
2021-04-05	87.561336
2021-04-06	235.783558
2021-04-07	295.394670
2021-04-08	167.116892
2021-04-09	-0.383108

100 rows × 1 columns

You can use the getLatestDataPoint to include the latest datapoint for a referenced timeseries.

In [52]:

my_query = """{
  listMetmast(first: 1){
    items{
      __typename
      temperature{
        externalId
        name
        getLatestDataPoint{
          items{
            timestamp
            value
          }
        }
      }
    }
  }
}"""

my_query = """{ listMetmast(first: 1){ items{ __typename temperature{ externalId name getLatestDataPoint{ items{ timestamp value } } } } } }"""

In [54]:

result = pygen.graphql_query(my_query)
metmast = result[0]

result = pygen.graphql_query(my_query) metmast = result[0]

In [56]:

metmast.temperature.latest

metmast.temperature.latest

Out[56]:

	value
2024-12-30 13:00:00	-0.122444

In [ ]:

Next section: Creating and Deleting