Onboard's data model contains both equipment types (e.g. fans, air handling units) and point types (e.g. zone temperature). We can query the full data model within our API.
Data model column definitions for each of the below tables can be found in :ref:`data model columns page<dm-reference-label>`.
First, we query equipment types.
.. tabs::
.. code-tab:: py
>>> from onboard.client import OnboardClient
>>> client = OnboardClient(api_key='')
>>> import pandas as pd
>>> # this query returns a JSON object,
>>> # which we convert to data frame using pd.json_normalize()
>>> equip_type = pd.json_normalize(client.get_equipment_types())
>>> equip_type[["id", "tag_name", "name_long"]]
id tag_name name_long
0 70 ELECTRICAL/ATS ELECTRICAL/ATS
1 71 ELECTRICAL/BATT ELECTRICAL/BATT
2 72 ELECTRICAL/CB ELECTRICAL/CB
3 73 ELECTRICAL/GEN ELECTRICAL/GEN
4 74 ELECTRICAL/PANEL ELECTRICAL/PANEL
.. code-tab:: r R
library(OnboardClient)
library(tidyverse)
api.setup()
get_equip_types() %>% select(id, tag_name, name_long)
id tag_name name_long
1 12 HVAC/AHU HVAC/AHU
6 19 HVAC/BLR HVAC/BLR
Note that not all equipment types have associated sub-types.
Accessing point types is very similar:
.. tabs::
.. code-tab:: py
>>> # Get all point types from the Data Model
>>> point_types = pd.DataFrame(client.get_all_point_types())
id tag_name tags
0 868 ac_voltage_sensor [sensor, volt]
1 869 air_pressure_sensor [pressure, sensor, air]
2 870 air_pressure_status [pressure, air]
3 871 ammonia_leak_detection_alarm [alarm, leakDetector]
4 872 apparent_energy_accumulator [energy, apparent]
.. code-tab:: r R
point_types <- get_point_types()
point_types %>% select(id, point_type, tags) %>% distinct()
# id point_type tags
#1 868 ac_voltage_sensor "sensor", "volt"
#2 869 air_pressure_sensor "pressure", "sensor", "air"
#3 870 air_pressure_status "pressure", "air"
#4 871 ammonia_leak_detection_alarm "alarm", "leakDetector"
point_types is now a dataframe listing all the tags associated with each point type.
Note
In the following, convenience wrapper functions are currently only available on the development version of the R package. For the official version, use each respective api.get() call mentioned in the code.
We can extract the metadata associated with each tag in our data model like so:
.. tabs::
.. code-tab:: py
>>> # Get all tags and their definitions from the Data Model
>>> pd.DataFrame(client.get_tags())
id name definition def_source def_url category
0 499 cogeneration Associated with a cogeneration process. dbo None None
1 500 dehumidification Process of removing moisture from air. dbo None None
2 405 ELECTRICAL/TXMR Tag for transformers, which are devices that t... dbo None None
3 406 ELECTRICAL/UPS Tag for all uninterruptible power supply (UPS)... dbo None None
4 407 GATEWAYS/PASSTHROUGH A device that provides translations for virtua... dbo None None
.. code-tab:: r R
api.get('tags') # official
get_tags() # dev
# id name definition def_source def_url category
#1 120 battery A container that stores chemical energy that can be con... brick https://brickschema.org/ontology/1.1/classes/Battery/ <NA>
#2 191 exhaustVAV A device that regulates the volume of air being exhaust... onboard <NA> <NA>
#3 193 oil A viscous liquid derived from petroleum, especially for... brick https://brickschema.org/ontology/1.2/classes/Oil/ <NA>
#4 114 fumeHood A fume-collection device mounted over a work space, tab... brick https://brickschema.org/ontology/1.1/classes/Fume_Hood/ <NA>
#5 118 limit A parameter that places a lower or upper bound on the r... brick https://brickschema.org/ontology/1.1/classes/Limit/ Point Class
#6 119 reset Indicates a boolean point that reset a flag, property o... brick https://brickschema.org/ontology/1.1/classes/Reset_Command/ <NA>
This returns a dataframe containing definitions for all tags in our data model, with attribution where applicable.
.. tabs::
.. code-tab:: py
>>> # Get all unit types from the Data Model
>>> unit_types = pd.DataFrame(client.get_all_units())
>>> unit_types[['id', 'name_long', 'qudt']].sample(5)
id name_long qudt
29 88 Kilo British Thermal Unit http://qudt.org/vocab/unit/KiloBTU_IT
21 103 Square Meters http://qudt.org/vocab/unit/M2
114 7 Cubic Feet Per Minute http://qudt.org/vocab/unit/FT3-PER-MIN
15 98 Milligravities http://qudt.org/vocab/unit/MilliG
33 78 Megawatt Hours http://qudt.org/vocab/unit/MegaW-HR
.. code-tab:: r R
# Get all unit types from the Data Model
units <- api.get('unit') # official
units <- get_all_units() # dev
units %>% select(id, name_long, qudt)
# id name_long qudt
#1 36 Thousand Pounds per Hour http://qudt.org/vocab/unit/KiloLB-PER-HR
#2 71 Joules Per Kilogram Dry Air http://qudt.org/vocab/unit/J-PER-KiloGM
#3 86 Kilojoules Per Kilogram http://qudt.org/vocab/unit/KiloJ-PER-KiloGM
#4 135 Megajoules Per Kilogram Dry Air http://qudt.org/vocab/unit/MegaJ-PER-KiloGM
.. tabs::
.. code-tab:: py
>>> # Get all measurement types from the Data Model
>>> measurement_types = pd.DataFrame(client.get_all_measurements())
>>> measurement_types[['id', 'name', 'qudt_type']]
id name qudt_type
0 26 Multi-State None
1 33 powerfactor http://qudt.org/vocab/quantitykind/Dimensionless
2 17 rotationalvelocity http://qudt.org/vocab/quantitykind/AngularVelo...
3 28 current http://qudt.org/vocab/quantitykind/ElectricCur...
4 14 energy http://qudt.org/vocab/quantitykind/Energy
.. code-tab:: r R
# Get all measurement types from the Data Model
measurements <- api.get('measurements') # official
measurements <- get_all_measurements() # dev
measurements %>% select(id, name, qudt_type)
# id name qudt_type
#1 26 Multi-State <NA>
#2 33 powerfactor http://qudt.org/vocab/quantitykind/Dimensionless
#3 17 rotationalvelocity http://qudt.org/vocab/quantitykind/AngularVelocity
#4 28 current http://qudt.org/vocab/quantitykind/ElectricCurrent