pandassta package allows easy tools to interact with a FROST-Server Sensorthings API, using pandas dataframes. This package was developed within a quality assurance project, which is reflected in some specific functions.

pip install pandassta

Different wrappers are available for some common queries, but custom queries can easily be constructed. The code below builds a query to get the observations per datastream, with the observed properties of thing 1.

obsprop = Entity(Entities.OBSERVEDPROPERTY)
obsprop.selection = [Properties.NAME, Properties.IOT_ID]

obs = Entity(Entities.OBSERVATIONS)
obs.settings = [Settings.COUNT("true"), Settings.TOP(0)]
obs.selection = [Properties.IOT_ID]

ds = Entity(Entities.DATASTREAMS)
ds.settings = [Settings.COUNT("true")]
ds.expand = [obsprop, obs]
ds.selection = [
    Properties.NAME,
    Properties.IOT_ID,
    Properties.DESCRIPTION,
    Properties.UNITOFMEASUREMENT,
    Entities.OBSERVEDPROPERTY,
]
thing = Entity(Entities.THINGS)
thing.id = 1
thing.selection = [Properties.NAME, Properties.IOT_ID, Entities.DATASTREAMS]
thing.expand = [ds]
query = Query(base_url=config.load_sta_url(), root_entity=thing)
query_http = query.build()

Lets assume you want to obtain the air temperature and water temperature measured between 2023-03-10 00:00 and 2023-03-11 10:00.

• Get list of things

  • Imports

    from pandassta.sta_requests import Config, Entity, Entities, Query, Properties
    from pandassta.sta_requests import set_sta_url, get_request, response_datastreams_to_df

  • Config

    config = Config()
    set_sta_url("https://sensors.naturalsciences.be/sta/v1.1")
    
    thing = Entity(Entities.THINGS) #not structly needed in this step, but needed later

  • Get json

    # if `thing` is not defined 
    # query = Query(config.load_sta_url(), root_entity=Entities.THINGS)
    query = Query(config.load_sta_url(), root_entity=thing)
    q_url = query.build() # if needed
    response = get_request(query)

• Get list of datastreams

  • why not datastreams directly?

  • using application https://sensors.naturalsciences.be/sensorthings-data/

  • Using pandassta

    thing.id = 1
    ds = Entity(Entities.DATASTREAMS)
    thing.selection = [ Entities.DATASTREAMS ]
    thing.expand = [ ds ]
    
    response = get_request(query)

• Get the relevant data/observations. In this example, datastreams 7749 and 7767 were selected, but multiple datastreams give the air or water temperature!

  • define the filter

    filter_ds = f"{Properties.IOT_ID} in (7749, 7767)"
    filter_obs = f"overlaps({Properties.PHENOMENONTIME}, 2023-03-10T00:00Z/2023-03-11T10:00Z)"
    ds.filter = filter_ds
    obs = Entity(Entities.OBSERVATIONS)
    
    obs.filter = filter_obs
    
    # # INCLUDING feature of interest! (coordinates)
    # foi = Entity(Entities.FEATUREOFINTEREST)
    # foi.selection = [Properties.COORDINATES, Properties.IOT_ID]
    # obs.expand = [foi]
    
    ds.expand = [obs]
    
    response = get_request(query)

• Data to a pandas dataframe

  • call pandassta method and verify dataframe

    df = response_datastreams_to_df(response[1])
    df.head()

    • output:

      	@iot.selfLink	@iot.id	phenomenonTime	resultTime	result	resultQuality	observation_type	observed_property_id	units	feature_id	long	lat
      0	Link	1155244072	2023-03-10 01:04:00	None	9.8811	2	NaN	None	Degrees Celsius	None	None	None
      1	Link	1155246938	2023-03-10 01:10:00	None	9.8618	2	NaN	None	Degrees Celsius	None	None	None
      2	Link	1155251749	2023-03-10 01:20:03	None	9.7390	2	NaN	None	Degrees Celsius	None	None	None
      3	Link	1155256547	2023-03-10 01:30:06	None	9.7692	2	NaN	None	Degrees Celsius	None	None	None
      4	Link	1155261355	2023-03-10 01:40:08	None	9.7360	2	NaN	None	Degrees Celsius	None	None	None

Reflection of the sensorthings structure.

Classes and function that allow or simplify the construction requests.

Classes and functions to convert observations to a dataframe.