generic_assets.py

from __future__ import annotations

from datetime import datetime, timedelta
from typing import Any, Dict, Optional, Tuple, List, Union
import json

from flask_security import current_user
import pandas as pd
from sqlalchemy.engine import Row
from sqlalchemy.ext.hybrid import hybrid_method
from sqlalchemy.sql.expression import func
from sqlalchemy.ext.mutable import MutableDict
from sqlalchemy.schema import UniqueConstraint
from timely_beliefs import BeliefsDataFrame, utils as tb_utils

from flexmeasures.data import db
from flexmeasures.data.models.annotations import Annotation, to_annotation_frame
from flexmeasures.data.models.charts import chart_type_to_chart_specs
from flexmeasures.data.models.data_sources import DataSource
from flexmeasures.data.models.parsing_utils import parse_source_arg
from flexmeasures.data.models.user import User
from flexmeasures.data.queries.annotations import query_asset_annotations
from flexmeasures.data.services.timerange import get_timerange
from flexmeasures.auth.policy import AuthModelMixin, EVERY_LOGGED_IN_USER
from flexmeasures.utils import geo_utils
from flexmeasures.utils.coding_utils import flatten_unique
from flexmeasures.utils.time_utils import determine_minimum_resampling_resolution


class GenericAssetType(db.Model):
    """An asset type defines what type an asset belongs to.

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

    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(80), default="", unique=True)
    description = db.Column(db.String(80), nullable=True, unique=False)


class GenericAsset(db.Model, AuthModelMixin):
    """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.
    """

    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(80), default="")
    latitude = db.Column(db.Float, nullable=True)
    longitude = db.Column(db.Float, nullable=True)
    attributes = db.Column(MutableDict.as_mutable(db.JSON), nullable=False, default={})

    generic_asset_type_id = db.Column(
        db.Integer, db.ForeignKey("generic_asset_type.id"), nullable=False
    )
    generic_asset_type = db.relationship(
        "GenericAssetType",
        foreign_keys=[generic_asset_type_id],
        backref=db.backref("generic_assets", lazy=True),
    )
    annotations = db.relationship(
        "Annotation",
        secondary="annotations_assets",
        backref=db.backref("assets", lazy="dynamic"),
    )

    __table_args__ = (
        UniqueConstraint(
            "name",
            "account_id",
            name="generic_asset_name_account_id_key",
        ),
    )

    def __acl__(self):
        """
        All logged-in users can read if the asset is public.
        Within same account, everyone can create, read and update.
        Deletion is left to account admins.
        """
        return {
            "create-children": f"account:{self.account_id}",
            "read": f"account:{self.account_id}"
            if self.account_id is not None
            else EVERY_LOGGED_IN_USER,
            "update": f"account:{self.account_id}",
            "delete": (f"account:{self.account_id}", "role:account-admin"),
        }

    def __repr__(self):
        return "<GenericAsset %s: %r (%s)>" % (
            self.id,
            self.name,
            self.generic_asset_type.name,
        )

    @property
    def asset_type(self) -> GenericAssetType:
        """This property prepares for dropping the "generic" prefix later"""
        return self.generic_asset_type

    account_id = db.Column(
        db.Integer, db.ForeignKey("account.id", ondelete="CASCADE"), nullable=True
    )  # if null, asset is public

    owner = db.relationship(
        "Account",
        backref=db.backref(
            "generic_assets",
            foreign_keys=[account_id],
            lazy=True,
            cascade="all, delete-orphan",
            passive_deletes=True,
        ),
    )

    @property
    def location(self) -> Optional[Tuple[float, float]]:
        location = (self.latitude, self.longitude)
        if None not in location:
            return location

    @hybrid_method
    def great_circle_distance(self, **kwargs):
        """Query great circle distance (in km).

        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)

        """
        other_location = geo_utils.parse_lat_lng(kwargs)
        if None in other_location:
            return None
        return geo_utils.earth_distance(self.location, other_location)

    @great_circle_distance.expression
    def great_circle_distance(self, **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.
        """
        other_location = geo_utils.parse_lat_lng(kwargs)
        if None in other_location:
            return None
        return func.earth_distance(
            func.ll_to_earth(self.latitude, self.longitude),
            func.ll_to_earth(*other_location),
        )

    def get_attribute(self, attribute: str, default: Any = None):
        if attribute in self.attributes:
            return self.attributes[attribute]
        return default

    def has_attribute(self, attribute: str) -> bool:
        return attribute in self.attributes

    def set_attribute(self, attribute: str, value):
        if self.has_attribute(attribute):
            self.attributes[attribute] = value

    @property
    def has_power_sensors(self) -> bool:
        """True if at least one power sensor is attached"""
        return any([s.measures_power for s in self.sensors])

    @property
    def has_energy_sensors(self) -> bool:
        """True if at least one energy sensor is attached"""
        return any([s.measures_energy for s in self.sensors])

    def add_annotations(
        self,
        df: pd.DataFrame,
        annotation_type: str,
        commit_transaction: bool = False,
    ):
        """Add a data frame describing annotations to the database, and assign the annotations to this asset."""
        annotations = Annotation.add(df, annotation_type=annotation_type)
        self.annotations += annotations
        db.session.add(self)
        if commit_transaction:
            db.session.commit()

    def search_annotations(
        self,
        annotations_after: Optional[datetime] = None,
        annotations_before: Optional[datetime] = None,
        source: Optional[
            Union[DataSource, List[DataSource], int, List[int], str, List[str]]
        ] = None,
        annotation_type: str = None,
        include_account_annotations: bool = False,
        as_frame: bool = False,
    ) -> Union[List[Annotation], pd.DataFrame]:
        """Return annotations assigned to this asset, and optionally, also those assigned to the asset's account.

        :param annotations_after: only return annotations that end after this datetime (exclusive)
        :param annotations_before: only return annotations that start before this datetime (exclusive)
        """
        parsed_sources = parse_source_arg(source)
        annotations = query_asset_annotations(
            asset_id=self.id,
            annotations_after=annotations_after,
            annotations_before=annotations_before,
            sources=parsed_sources,
            annotation_type=annotation_type,
        ).all()
        if include_account_annotations:
            annotations += self.owner.search_annotations(
                annotations_after=annotations_after,
                annotations_before=annotations_before,
                source=source,
            )

        return to_annotation_frame(annotations) if as_frame else annotations

    def count_annotations(
        self,
        annotation_starts_after: Optional[datetime] = None,  # deprecated
        annotations_after: Optional[datetime] = None,
        annotation_ends_before: Optional[datetime] = None,  # deprecated
        annotations_before: Optional[datetime] = None,
        source: Optional[
            Union[DataSource, List[DataSource], int, List[int], str, List[str]]
        ] = None,
        annotation_type: str = None,
    ) -> int:
        """Count the number of annotations assigned to this asset."""

        # todo: deprecate the 'annotation_starts_after' argument in favor of 'annotations_after' (announced v0.11.0)
        annotations_after = tb_utils.replace_deprecated_argument(
            "annotation_starts_after",
            annotation_starts_after,
            "annotations_after",
            annotations_after,
            required_argument=False,
        )

        # todo: deprecate the 'annotation_ends_before' argument in favor of 'annotations_before' (announced v0.11.0)
        annotations_before = tb_utils.replace_deprecated_argument(
            "annotation_ends_before",
            annotation_ends_before,
            "annotations_before",
            annotations_before,
            required_argument=False,
        )

        parsed_sources = parse_source_arg(source)
        return query_asset_annotations(
            asset_id=self.id,
            annotations_after=annotations_after,
            annotations_before=annotations_before,
            sources=parsed_sources,
            annotation_type=annotation_type,
        ).count()

    def chart(
        self,
        chart_type: str = "chart_for_multiple_sensors",
        event_starts_after: Optional[datetime] = None,
        event_ends_before: Optional[datetime] = None,
        beliefs_after: Optional[datetime] = None,
        beliefs_before: Optional[datetime] = None,
        source: Optional[
            Union[DataSource, List[DataSource], int, List[int], str, List[str]]
        ] = None,
        include_data: bool = False,
        dataset_name: Optional[str] = None,
        **kwargs,
    ) -> dict:
        """Create a vega-lite chart showing sensor data.

        :param chart_type: currently only "bar_chart" # todo: where can we properly list the available chart types?
        :param event_starts_after: only return beliefs about events that start after this datetime (inclusive)
        :param event_ends_before: only return beliefs about events that end before this datetime (inclusive)
        :param beliefs_after: only return beliefs formed after this datetime (inclusive)
        :param beliefs_before: only return beliefs formed before this datetime (inclusive)
        :param source: search only beliefs by this source (pass the DataSource, or its name or id) or list of sources
        :param include_data: if True, include data in the chart, or if False, exclude data
        :param dataset_name: optionally name the dataset used in the chart (the default name is sensor_<id>)
        :returns: JSON string defining vega-lite chart specs
        """
        sensors = flatten_unique(self.sensors_to_show)
        for sensor in sensors:
            sensor.sensor_type = sensor.get_attribute("sensor_type", sensor.name)

        # Set up chart specification
        if dataset_name is None:
            dataset_name = "asset_" + str(self.id)
        if event_starts_after:
            kwargs["event_starts_after"] = event_starts_after
        if event_ends_before:
            kwargs["event_ends_before"] = event_ends_before
        chart_specs = chart_type_to_chart_specs(
            chart_type,
            sensors_to_show=self.sensors_to_show,
            dataset_name=dataset_name,
            **kwargs,
        )

        if include_data:
            # Get data
            data = self.search_beliefs(
                sensors=sensors,
                as_json=True,
                event_starts_after=event_starts_after,
                event_ends_before=event_ends_before,
                beliefs_after=beliefs_after,
                beliefs_before=beliefs_before,
                source=source,
            )

            # Combine chart specs and data
            chart_specs["datasets"] = {
                dataset_name: json.loads(data),
            }

        return chart_specs

    def search_beliefs(
        self,
        sensors: Optional[List["Sensor"]] = None,  # noqa F821
        event_starts_after: Optional[datetime] = None,
        event_ends_before: Optional[datetime] = None,
        beliefs_after: Optional[datetime] = None,
        beliefs_before: Optional[datetime] = None,
        horizons_at_least: Optional[timedelta] = None,
        horizons_at_most: Optional[timedelta] = None,
        source: Optional[
            Union[DataSource, List[DataSource], int, List[int], str, List[str]]
        ] = None,
        most_recent_beliefs_only: bool = True,
        most_recent_events_only: bool = False,
        as_json: bool = False,
    ) -> Union[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.

        :param sensors: only return beliefs about events registered by these sensors
        :param event_starts_after: only return beliefs about events that start after this datetime (inclusive)
        :param event_ends_before: only return beliefs about events that end before this datetime (inclusive)
        :param beliefs_after: only return beliefs formed after this datetime (inclusive)
        :param beliefs_before: only return beliefs formed before this datetime (inclusive)
        :param 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)
        :param 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)
        :param source: search only beliefs by this source (pass the DataSource, or its name or id) or list of sources
        :param most_recent_events_only: only return (post knowledge time) beliefs for the most recent event (maximum event start)
        :param 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)
        """
        bdf_dict = {}
        if sensors is None:
            sensors = self.sensors
        for sensor in sensors:
            bdf_dict[sensor] = sensor.search_beliefs(
                event_starts_after=event_starts_after,
                event_ends_before=event_ends_before,
                beliefs_after=beliefs_after,
                beliefs_before=beliefs_before,
                horizons_at_least=horizons_at_least,
                horizons_at_most=horizons_at_most,
                source=source,
                most_recent_beliefs_only=most_recent_beliefs_only,
                most_recent_events_only=most_recent_events_only,
                one_deterministic_belief_per_event_per_source=True,
            )
        if as_json:
            from flexmeasures.data.services.time_series import simplify_index

            if sensors:
                minimum_resampling_resolution = determine_minimum_resampling_resolution(
                    [bdf.event_resolution for bdf in bdf_dict.values()]
                )
                df_dict = {}
                for sensor, bdf in bdf_dict.items():
                    if bdf.event_resolution > timedelta(0):
                        bdf = bdf.resample_events(minimum_resampling_resolution)
                    bdf["belief_horizon"] = bdf.belief_horizons.to_numpy()
                    df = simplify_index(
                        bdf,
                        index_levels_to_columns=["source"]
                        if most_recent_beliefs_only
                        else ["belief_time", "source"],
                    ).set_index(
                        ["source"]
                        if most_recent_beliefs_only
                        else ["belief_time", "source"],
                        append=True,
                    )
                    df["sensor"] = sensor  # or some JSONifiable representation
                    df = df.set_index(["sensor"], append=True)
                    df_dict[sensor.id] = df
                df = pd.concat(df_dict.values())
            else:
                df = simplify_index(
                    BeliefsDataFrame(),
                    index_levels_to_columns=["source"]
                    if most_recent_beliefs_only
                    else ["belief_time", "source"],
                ).set_index(
                    ["source"]
                    if most_recent_beliefs_only
                    else ["belief_time", "source"],
                    append=True,
                )
            df = df.reset_index()
            df["source"] = df["source"].apply(lambda x: x.to_dict())
            df["sensor"] = df["sensor"].apply(lambda x: x.to_dict())
            return df.to_json(orient="records")
        return bdf_dict

    @property
    def sensors_to_show(self) -> list["Sensor" | list["Sensor"]]:  # noqa F821
        """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 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]

        """
        if not self.has_attribute("sensors_to_show"):
            return self.sensors[:2]

        from flexmeasures.data.services.sensors import get_sensors

        sensor_ids_to_show = self.get_attribute("sensors_to_show")
        sensor_map = {
            sensor.id: sensor
            for sensor in get_sensors(
                account=self.owner,
                include_public_assets=True,
                sensor_id_allowlist=flatten_unique(sensor_ids_to_show),
            )
        }

        # Return sensors in the order given by the sensors_to_show attribute, and with the same nesting
        sensors_to_show = []
        for s in sensor_ids_to_show:
            if isinstance(s, list):
                sensors_to_show.append([sensor_map[sensor_id] for sensor_id in s])
            else:
                sensors_to_show.append(sensor_map[s])
        return sensors_to_show

    @property
    def timezone(
        self,
    ) -> 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.
        """
        if self.has_attribute("timezone"):
            return self.get_attribute("timezone")
        if self.sensors:
            return self.sensors[0].timezone
        return "UTC"

    @property
    def timerange(self) -> 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)
                  }
        """
        return self.get_timerange(self.sensors)

    @property
    def timerange_of_sensors_to_show(self) -> 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)
                  }
        """
        return self.get_timerange(self.sensors_to_show)

    @classmethod
    def get_timerange(cls, sensors: List["Sensor"]) -> Dict[str, datetime]:  # noqa F821
        """Time range for which sensor data exists.

        :param 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)
                  }
        """
        sensor_ids = [s.id for s in flatten_unique(sensors)]
        start, end = get_timerange(sensor_ids)
        return dict(start=start, end=end)


def create_generic_asset(generic_asset_type: str, **kwargs) -> GenericAsset:
    """Create a GenericAsset and assigns it an id.

    :param generic_asset_type: "asset", "market" or "weather_sensor"
    :param 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
    """
    asset_type_name = kwargs.pop(f"{generic_asset_type}_type_name", None)
    if asset_type_name is None:
        if f"{generic_asset_type}_type" in kwargs:
            asset_type_name = kwargs.pop(f"{generic_asset_type}_type").name
        else:
            asset_type_name = kwargs.pop("sensor_type").name
    generic_asset_type = GenericAssetType.query.filter_by(
        name=asset_type_name
    ).one_or_none()
    if generic_asset_type is None:
        raise ValueError(f"Cannot find GenericAssetType {asset_type_name} in database.")
    new_generic_asset = GenericAsset(
        name=kwargs["name"],
        generic_asset_type_id=generic_asset_type.id,
        attributes=kwargs["attributes"] if "attributes" in kwargs else {},
    )
    for arg in ("latitude", "longitude", "account_id"):
        if arg in kwargs:
            setattr(new_generic_asset, arg, kwargs[arg])
    db.session.add(new_generic_asset)
    db.session.flush()  # generates the pkey for new_generic_asset
    return new_generic_asset


def 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".
    """
    if not assets:
        return True
    return all([a.location == assets[0].location for a in assets])


def get_center_location_of_assets(user: Optional[User]) -> Tuple[float, float]:
    """
    Find the center position between all generic assets of the user's account.
    """
    query = (
        "Select (min(latitude) + max(latitude)) / 2 as latitude,"
        " (min(longitude) + max(longitude)) / 2 as longitude"
        " from generic_asset"
    )
    if user is None:
        user = current_user
    query += f" where generic_asset.account_id = {user.account_id}"
    locations: List[Row] = db.session.execute(query + ";").fetchall()
    if (
        len(locations) == 0
        or locations[0].latitude is None
        or locations[0].longitude is None
    ):
        return 52.366, 4.904  # Amsterdam, NL
    return locations[0].latitude, locations[0].longitude