Columns for Data Model

Equipment types

Accessed with

pd.json_normalize(client.get_equipment_types())

id: unique integer associated with the given type/tag

tag_name: this is the equip type tag associated with a class

active: True if this class in the latest version of the ontology

critical_point_types: id numbers of the associated point types that are expected to be observed (look up in client.get_all_point_types())

tags: Haystack tags associated with equipment super-type

Point types

Accessed with

client.get_all_point_types()

id: unique integer associated with the given type/tag

tag_name: human-readable name. This is the class name you will find in the ontology.

active: True if this class in the latest version of the ontology

measurement_id: id of the associated measurement type accessed as documented below

tags: Haystack tags associated with point type

Unit types

Accessed with

pd.DataFrame(client.get_all_units())

id: unique integer associated with the given type/tag

name_long: human-readable unit name (e.g. ‘Kilopascals’)

name_abbr: abbreviated form (e.g. ‘kPa’)

data_type: form of associated data. Can be ‘Binary’, ‘Continuous’, ‘Enum’, ‘None’, or ‘Ordinal’

raw_encoding: for Binary and Enum data types, contains dictionary matching number to interpretation.

display_encoding: for Binary and Enum data types, contains dictionary showing how each reported number will be displayed. E.g., a 0 from an Occupancy sensor will be reported as ‘Unoccupied’.

qudt: url for additional information about unit (e.g. ‘Degrees Celsius’) on qudt.org

unit_type: url for additional information about measurement type (e.g. ‘Temperature’) on qudt.org

Measurement types

Accessed with

pd.DataFrame(client.get_all_measurements())

id: unique integer associated with the given measurement types

name: name of measurement type

default_unit_id: id of default associated unit type in client.get_all_units(). Note, pandas will cast this column as a float, but it can still be used to look up id

units_convertible: True if units of this measurement type can be interchangeably converted (generally True for continuous measurement types)

units: embedded JSON of possible units for given measurement type

qudt_type: url for additional information about measurement type (e.g. ‘rotationalvelocity’) on qudt.org

Tag metadata

Accessed with

pd.DataFrame(client.get_tags())

id: unique integer associated with the given tag metadata

name: name of tag being described

definition: definition of tag

def_source: source of definition (will be dbo)

Columns for Data Extracted from Buildings

Building-Specific Equipment

id: unique integer associated with the given equipment in this building. Will be unique across all equipment in platform.

building_id: unique integer associated with the building. Will be unique across all buildings in platform.

equip_id: Name to identify individual equipment instances. Constructed as equipment name + identifying suffix

suffix: Just the identifying suffix part of the equip_id

equip_type_name: Relevant name in the ontology

equip_type_id: integer id of relevant equipment type

equip_type_abbr: abbreviation of relevant equipment type

equip_type_tag: tag name of relevant equipment type

equip_subtype_name: name of relevant equipment sub-type

equip_subtype_id: integer id of relevant equipment sub-type

equip_subtype_tag: tag name of relevant equipment sub-type

floor_num_physical: 4-digit code (see below) for floor where equipment is located. Can be integer or NaN if not available

1000: basement

1001: rooftop

1002: outside

1003: whole_buildings

1004: ground_floor

1005: penthouse

floor_num_served: 4-digit code for floor that equipment serves. Can be integer or NaN if not available

area_served_desc: Description of area that equipment serves

equip_dis: plain-text description of equipment from building documentation

parent_equip: integer id that links to parent equipment row(s)

child_equip: integer id that links to child equipment row(s)

points: embedded JSON containing associated points

tags: Haystack tags associated with equipment

Building-Specific Points

id: unique integer associated with the given point in this building. Will be unique across all points in platform.

building_id: unique integer associated with the building. Will be unique across all buildings in platform.

last_updated: Unix-formatted timestamp of most recent value reported from point

first_updated: Unix-formatted timestamp of earliest value reported from point

name: raw sensor metadata (from BACnet scan)

description: alternate location for raw sensor metadata (from BACnet scan)

units: Matches to unit abbreviation in units table

raw_unit_id: unit id as it appears when accessing unit types

value: Most recent reported value for point (from BACnet scan)

type: name of point type in the ontology

point_type_id: point type name as it appears when accessing point types

measurement_id: measurement type id as it appears when accessing measurement types

state_text: mapping between each state and text description of state

equip_id: unique integer associated with the associated equipment

Site-Level Data

Accessed with

client.get_all_buildings()

id: Unique ID generated for a new site (primary key for the Site Table)

name: Site name

sq_ft: Total square-footage of the site address

equip_count: Number of equipment instances associated with the building

point_count: Number of points associated with the building

info.org: Site’s main ownership organization

info.floors: Number of floors associated with the site’s square footage

info.m2fend: Site scheduled weekday closing time

info.satend: Site scheduled Saturday closing time

info.sunend: Site scheduled Sunday closing time

info.geoCity: Name of the city where the site is located

info.geoState: Name of the state where the site is located (e.g. New York)

info.m2fstart: Site scheduled weekday opening time

info.satstart: Site scheduled Saturday opening time

info.sunstart: Site scheduled Sunday opening time

info.geoCountry: Name of the country where the site is located

info.weatherRef: The source of weather data