flexmeasures.data.models.generic_assets

Functions

flexmeasures.data.models.generic_assets.assets_share_location(assets: list[GenericAsset]) → bool

Return True if all assets in this list are located on the same spot. TODO: In the future, we might soften this to compare if assets are in the same “housing” or “site”.

flexmeasures.data.models.generic_assets.create_generic_asset(generic_asset_type: str, **kwargs) → GenericAsset

Create a GenericAsset and assigns it an id.

Parameters:

  • generic_asset_type – “asset”, “market” or “weather_sensor”

  • kwargs – should have values for keys “name”, and: - “asset_type_name” or “asset_type” when generic_asset_type is “asset” - “market_type_name” or “market_type” when generic_asset_type is “market” - “weather_sensor_type_name” or “weather_sensor_type” when generic_asset_type is “weather_sensor” - alternatively, “sensor_type” is also fine

Returns:

the created GenericAsset

flexmeasures.data.models.generic_assets.get_center_location_of_assets(user: User | None) → tuple[float, float]

Find the center position between all generic assets of the user’s account.

Classes

class flexmeasures.data.models.generic_assets.GenericAsset(**kwargs)

An asset is something that has economic value.

Examples of tangible assets: a house, a ship, a weather station. Examples of intangible assets: a market, a country, a copyright.

__init__(**kwargs)

A simple constructor that allows initialization from kwargs.

Sets attributes on the constructed instance using the names and values in kwargs.

Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.

add_annotations(df: DataFrame, annotation_type: str, commit_transaction: bool = False)

Add a data frame describing annotations to the database, and assign the annotations to this asset.

property asset_type: GenericAssetType

This property prepares for dropping the “generic” prefix later

chart(chart_type: str = 'chart_for_multiple_sensors', event_starts_after: datetime | None = None, event_ends_before: datetime | None = None, beliefs_after: datetime | None = None, beliefs_before: datetime | None = None, source: DataSource | list[DataSource] | int | list[int] | str | list[str] | None = None, include_data: bool = False, dataset_name: str | None = None, **kwargs) → dict

Create a vega-lite chart showing sensor data.

Parameters:

  • chart_type – currently only “bar_chart” # todo: where can we properly list the available chart types?

  • event_starts_after – only return beliefs about events that start after this datetime (inclusive)

  • event_ends_before – only return beliefs about events that end before this datetime (inclusive)

  • beliefs_after – only return beliefs formed after this datetime (inclusive)

  • beliefs_before – only return beliefs formed before this datetime (inclusive)

  • source – search only beliefs by this source (pass the DataSource, or its name or id) or list of sources

  • include_data – if True, include data in the chart, or if False, exclude data

  • dataset_name – optionally name the dataset used in the chart (the default name is sensor_<id>)

Returns:

JSON string defining vega-lite chart specs

count_annotations(annotation_starts_after: datetime | None = None, annotations_after: datetime | None = None, annotation_ends_before: datetime | None = None, annotations_before: datetime | None = None, source: DataSource | list[DataSource] | int | list[int] | str | list[str] | None = None, annotation_type: str = None) → int

Count the number of annotations assigned to this asset.

classmethod get_timerange(sensors: list[Sensor]) → dict[str, datetime]

Time range for which sensor data exists.

Parameters:

sensors – sensors to check

Returns:

dictionary with start and end, for example: {

’start’: datetime.datetime(2020, 12, 3, 14, 0, tzinfo=pytz.utc), ‘end’: datetime.datetime(2020, 12, 3, 14, 30, tzinfo=pytz.utc)

}

great_circle_distance(**kwargs)

Query great circle distance (unclear if in km or in miles).

Can be called with an object that has latitude and longitude properties, for example:

great_circle_distance(object=asset)

Can also be called with latitude and longitude parameters, for example:

great_circle_distance(latitude=32, longitude=54) great_circle_distance(lat=32, lng=54)

Requires the following Postgres extensions: earthdistance and cube.

property has_energy_sensors: bool

True if at least one energy sensor is attached

property has_power_sensors: bool

True if at least one power sensor is attached

property offspring: list[GenericAsset]

Returns a flattened list of all offspring, which is looked up recursively.

query: t.ClassVar[Query]

A SQLAlchemy query for a model. Equivalent to db.session.query(Model). Can be customized per-model by overriding query_class.

Warning

The query interface is considered legacy in SQLAlchemy. Prefer using session.execute(select()) instead.

search_annotations(annotations_after: datetime | None = None, annotations_before: datetime | None = None, source: DataSource | list[DataSource] | int | list[int] | str | list[str] | None = None, annotation_type: str = None, include_account_annotations: bool = False, as_frame: bool = False) → list[Annotation] | pd.DataFrame

Return annotations assigned to this asset, and optionally, also those assigned to the asset’s account.

The returned annotations do not include any annotations on public accounts.

Parameters:

  • annotations_after – only return annotations that end after this datetime (exclusive)

  • annotations_before – only return annotations that start before this datetime (exclusive)

search_beliefs(sensors: list['Sensor'] | None = None, event_starts_after: datetime | None = None, event_ends_before: datetime | None = None, beliefs_after: datetime | None = None, beliefs_before: datetime | None = None, horizons_at_least: timedelta | None = None, horizons_at_most: timedelta | None = None, source: DataSource | list[DataSource] | int | list[int] | str | list[str] | None = None, most_recent_beliefs_only: bool = True, most_recent_events_only: bool = False, as_json: bool = False) → BeliefsDataFrame | str

Search all beliefs about events for all sensors of this asset

If you don’t set any filters, you get the most recent beliefs about all events.

Parameters:

  • sensors – only return beliefs about events registered by these sensors

  • event_starts_after – only return beliefs about events that start after this datetime (inclusive)

  • event_ends_before – only return beliefs about events that end before this datetime (inclusive)

  • beliefs_after – only return beliefs formed after this datetime (inclusive)

  • beliefs_before – only return beliefs formed before this datetime (inclusive)

  • horizons_at_least – only return beliefs with a belief horizon equal or greater than this timedelta (for example, use timedelta(0) to get ante knowledge time beliefs)

  • horizons_at_most – only return beliefs with a belief horizon equal or less than this timedelta (for example, use timedelta(0) to get post knowledge time beliefs)

  • source – search only beliefs by this source (pass the DataSource, or its name or id) or list of sources

  • most_recent_events_only – only return (post knowledge time) beliefs for the most recent event (maximum event start)

  • as_json – return beliefs in JSON format (e.g. for use in charts) rather than as BeliefsDataFrame

Returns:

dictionary of BeliefsDataFrames or JSON string (if as_json is True)

property sensors_to_show: list['Sensor' | list['Sensor']]

Sensors to show, as defined by the sensors_to_show attribute.

Sensors to show are defined as a list of sensor ids, which is set by the “sensors_to_show” field of the asset’s “attributes” column. Valid sensors either belong to the asset itself, to other assets in the same account, or to public assets. In play mode, sensors from different accounts can be added. In case the field is missing, defaults to two of the asset’s sensors.

Sensor ids can be nested to denote that sensors should be ‘shown together’, for example, layered rather than vertically concatenated. How to interpret ‘shown together’ is technically left up to the function returning chart specs, as are any restrictions regarding what sensors can be shown together, such as: - whether they should share the same unit - whether they should share the same name - whether they should belong to different assets

For example, this denotes showing sensors 42 and 44 together:

sensors_to_show = [40, 35, 41, [42, 44], 43, 45]

property timerange: dict[str, datetime]

Time range for which sensor data exists.

Returns:

dictionary with start and end, for example: {

’start’: datetime.datetime(2020, 12, 3, 14, 0, tzinfo=pytz.utc), ‘end’: datetime.datetime(2020, 12, 3, 14, 30, tzinfo=pytz.utc)

}

property timerange_of_sensors_to_show: dict[str, datetime]

Time range for which sensor data exists, for sensors to show.

Returns:

dictionary with start and end, for example: {

’start’: datetime.datetime(2020, 12, 3, 14, 0, tzinfo=pytz.utc), ‘end’: datetime.datetime(2020, 12, 3, 14, 30, tzinfo=pytz.utc)

}

property timezone: str

Timezone relevant to the asset.

If a timezone is not given as an attribute of the asset, it is taken from one of its sensors.

class flexmeasures.data.models.generic_assets.GenericAssetType(**kwargs)

An asset type defines what type an asset belongs to.

Examples of asset types: WeatherStation, Market, CP, EVSE, WindTurbine, SolarPanel, Building.

__init__(**kwargs)

A simple constructor that allows initialization from kwargs.

Sets attributes on the constructed instance using the names and values in kwargs.

Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.

query: t.ClassVar[Query]

A SQLAlchemy query for a model. Equivalent to db.session.query(Model). Can be customized per-model by overriding query_class.

Warning

The query interface is considered legacy in SQLAlchemy. Prefer using session.execute(select()) instead.