Timed beliefs

flexmeasures/conftest.py

@@ -23,6 +23,7 @@
 from flexmeasures.data.models.assets import AssetType, Asset, Power
 from flexmeasures.data.models.data_sources import DataSource
 from flexmeasures.data.models.markets import Market, Price
+from flexmeasures.data.models.time_series import Sensor, TimedBelief
 
 
 """
@@ -175,9 +176,31 @@ def setup_assets(db, setup_roles_users, setup_markets):
             db.session.add(p)
 
 
+@pytest.fixture(scope="function")
+def setup_beliefs(db: SQLAlchemy, setup_markets) -> int:
+    """
+    :returns: the number of beliefs set up
+    """
+    sensor = Sensor.query.filter(Sensor.name == "epex_da").one_or_none()
+    data_source = DataSource.query.filter_by(
+        name="Seita", type="demo script"
+    ).one_or_none()
+    beliefs = [
+        TimedBelief(
+            sensor=sensor,
+            source=data_source,
+            event_value=21,
+            event_start="2021-03-28 16:00+01",
+            belief_horizon=timedelta(0),
+        )
+    ]
+    db.session.add_all(beliefs)
+    return len(beliefs)
+
+
 @pytest.fixture(scope="function", autouse=True)
 def add_market_prices(db: SQLAlchemy, setup_assets, setup_markets):
-    """Add one day of market prices for the EPEX day-ahead market."""
+    """Add two days of market prices for the EPEX day-ahead market."""
     epex_da = Market.query.filter(Market.name == "epex_da").one_or_none()
     data_source = DataSource.query.filter_by(
         name="Seita", type="demo script"

flexmeasures/data/migrations/versions/e62ac5f519d7_create_table_for_timed_beliefs.py

@@ -0,0 +1,53 @@
+"""create table for timed beliefs
+
+Revision ID: e62ac5f519d7
+Revises: a528c3c81506
+Create Date: 2021-03-28 16:26:45.025994
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "e62ac5f519d7"
+down_revision = "a528c3c81506"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    op.create_table(
+        "timed_belief",
+        sa.Column(
+            "event_start", sa.DateTime(timezone=True), nullable=False, index=True
+        ),
+        sa.Column("belief_horizon", sa.Interval(), nullable=False),
+        sa.Column("cumulative_probability", sa.Float(), nullable=False, default=0.5),
+        sa.Column("event_value", sa.Float(), nullable=False),
+        sa.Column("sensor_id", sa.Integer(), nullable=False, index=True),
+        sa.Column("source_id", sa.Integer(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["sensor_id"],
+            ["sensor.id"],
+            name=op.f("timed_belief_sensor_id_sensor_fkey"),
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["source_id"],
+            ["data_source.id"],
+            name=op.f("timed_belief_source_id_source_fkey"),
+            ondelete="CASCADE",
+        ),
+        sa.PrimaryKeyConstraint(
+            "event_start",
+            "belief_horizon",
+            "cumulative_probability",
+            "sensor_id",
+            name=op.f("timed_belief_pkey"),
+        ),
+    )
+
+
+def downgrade():
+    op.drop_table("timed_belief")

flexmeasures/data/models/time_series.py

@@ -4,6 +4,7 @@
 from sqlalchemy.ext.declarative import declared_attr
 from sqlalchemy.orm import Query, Session
 import timely_beliefs as tb
+import timely_beliefs.utils as tb_utils
 
 from flexmeasures.data.config import db
 from flexmeasures.data.queries.utils import (
@@ -19,6 +20,112 @@
 class Sensor(db.Model, tb.SensorDBMixin):
     """A sensor measures events. """
 
+    def __init__(self, name: str, **kwargs):
+        tb.SensorDBMixin.__init__(self, name, **kwargs)
+        tb_utils.remove_class_init_kwargs(tb.SensorDBMixin, kwargs)
+        db.Model.__init__(self, **kwargs)
+
+    def search_beliefs(
+        self,
+        event_time_window: Tuple[Optional[datetime_type], Optional[datetime_type]] = (
+            None,
+            None,
+        ),
+        belief_time_window: Tuple[Optional[datetime_type], Optional[datetime_type]] = (
+            None,
+            None,
+        ),
+        source: Optional[Union[int, List[int], str, List[str]]] = None,
+    ):
+        """Search all beliefs about events for this sensor.
+
+        :param event_time_window: search only events within this time window
+        :param belief_time_window: search only beliefs within this time window
+        :param source: search only beliefs by this source (pass its name or id) or list of sources"""
+        return TimedBelief.search(
+            sensor=self,
+            event_time_window=event_time_window,
+            belief_time_window=belief_time_window,
+            source=source,
+        )
+
+    def __repr__(self) -> str:
+        return f"<Sensor {self.id}: {self.name}>"
+
+
+class TimedBelief(db.Model, tb.TimedBeliefDBMixin):
+    """A timed belief holds a precisely timed record of a belief about an event.
+
+    It also records the source of the belief, and the sensor that the event pertains to.
+    """
+
+    @declared_attr
+    def source_id(cls):
+        return db.Column(db.Integer, db.ForeignKey("data_source.id"), primary_key=True)
+
+    sensor = db.relationship("Sensor", backref=db.backref("beliefs", lazy=True))
+    source = db.relationship("DataSource", backref=db.backref("beliefs", lazy=True))
+
+    def __init__(
+        self,
+        sensor: tb.DBSensor,
+        source: tb.DBBeliefSource,
+        **kwargs,
+    ):
+        tb.TimedBeliefDBMixin.__init__(self, sensor, source, **kwargs)
+        tb_utils.remove_class_init_kwargs(tb.TimedBeliefDBMixin, kwargs)
+        db.Model.__init__(self, **kwargs)
+
+    @classmethod
+    def search(
+        cls,
+        sensor: Sensor,
+        event_time_window: Tuple[Optional[datetime_type], Optional[datetime_type]] = (
+            None,
+            None,
+        ),
+        belief_time_window: Tuple[Optional[datetime_type], Optional[datetime_type]] = (
+            None,
+            None,
+        ),
+        source: Optional[Union[int, List[int], str, List[str]]] = None,
+    ) -> tb.BeliefsDataFrame:
+        """Search all beliefs about events for a given sensor.
+
+        :param sensor: search only this sensor
+        :param event_time_window: search only events within this time window
+        :param belief_time_window: search only beliefs within this time window
+        :param source: search only beliefs by this source (pass its name or id) or list of sources
+        """
+        return cls.search_session(
+            session=db.session,
+            sensor=sensor,
+            event_before=event_time_window[1],
+            event_not_before=event_time_window[0],
+            belief_before=belief_time_window[1],
+            belief_not_before=belief_time_window[0],
+            source=source,
+        )
+
+    @classmethod
+    def add(cls, bdf: tb.BeliefsDataFrame, commit_transaction: bool = True):
+        """Add a BeliefsDataFrame as timed beliefs in the database.
+
+        :param bdf: the BeliefsDataFrame to be persisted
+        :param commit_transaction: if True, the session is committed
+                                   if False, you can still add other data to the session
+                                   and commit it all within an atomic transaction
+        """
+        return cls.add_to_session(
+            session=db.session,
+            beliefs_data_frame=bdf,
+            commit_transaction=commit_transaction,
+        )
+
+    def __repr__(self) -> str:
+        """timely-beliefs representation of timed beliefs."""
+        return tb.TimedBelief.__repr__(self)
+
 
 class TimedValue(object):
     """

flexmeasures/data/scripts/data_gen.py

@@ -17,6 +17,7 @@
 import inflect
 
 from flexmeasures.data.models.markets import MarketType, Market, Price
+from flexmeasures.data.models.time_series import TimedBelief
 from flexmeasures.data.models.assets import AssetType, Asset, Power
 from flexmeasures.data.models.data_sources import DataSource
 from flexmeasures.data.models.weather import WeatherSensorType, WeatherSensor, Weather
@@ -625,5 +626,5 @@ def get_affected_classes(structure: bool = True, data: bool = False):
             DataSource,
         ]
     if data:
-        affected_classes += [Power, Price, Weather]
+        affected_classes += [TimedBelief, Power, Price, Weather]
     return affected_classes

flexmeasures/data/tests/test_queries.py

@@ -7,6 +7,8 @@
 import timely_beliefs as tb
 
 from flexmeasures.data.models.assets import Asset, Power
+from flexmeasures.data.models.data_sources import DataSource
+from flexmeasures.data.models.time_series import Sensor, TimedBelief
 from flexmeasures.data.queries.utils import (
     multiply_dataframe_with_deterministic_beliefs,
     simplify_index,
@@ -215,3 +217,43 @@ def test_simplify_index():
     )
     df = simplify_index(bdf)
     assert df.event_resolution == timedelta(minutes=15)
+
+
+def test_query_beliefs(setup_beliefs):
+    """Check various ways of querying for beliefs."""
+    sensor = Sensor.query.filter_by(name="epex_da").one_or_none()
+    source = DataSource.query.filter_by(name="Seita").one_or_none()
+    bdfs = [
+        TimedBelief.search(sensor, source=source),
+        sensor.search_beliefs(source=source),
+        tb.BeliefsDataFrame(sensor.beliefs),  # doesn't allow filtering
+    ]
+    for bdf in bdfs:
+        assert sensor.event_resolution == timedelta(
+            hours=0
+        )  # todo change to 1 after migrating Markets to Sensors
+        assert bdf.event_resolution == timedelta(
+            hours=0
+        )  # todo change to 1 after migrating Markets to Sensors
+        assert len(bdf) == setup_beliefs
+
+
+def test_persist_beliefs(setup_beliefs):
+    """Check whether persisting beliefs works.
+
+    We load the already set up beliefs, and form new beliefs an hour later.
+    """
+    sensor = Sensor.query.filter_by(name="epex_da").one_or_none()
+    bdf: tb.BeliefsDataFrame = TimedBelief.search(sensor)
+
+    # Form new beliefs
+    df = bdf.reset_index()
+    df["belief_time"] = df["belief_time"] + timedelta(hours=1)
+    df["event_value"] = df["event_value"] * 10
+    bdf = df.set_index(
+        ["event_start", "belief_time", "source", "cumulative_probability"]
+    )
+
+    TimedBelief.add(bdf)
+    bdf: tb.BeliefsDataFrame = TimedBelief.search(sensor)
+    assert len(bdf) == setup_beliefs * 2

requirements/app.in

@@ -32,7 +32,7 @@ netCDF4
 siphon
 tables
 timetomodel>=0.6.8
-timely-beliefs>=1.2.1
+timely-beliefs==1.3.0.dev2664
 python-dotenv
 # a backport, not needed in Python3.8
 importlib_metadata