dp3.snapshots.snapshooter

Module managing creation of snapshots, enabling data correlation and saving snapshots to DB.

• Snapshots are created periodically (user configurable period)

• When a snapshot is created, several things need to happen:

  • all registered timeseries processing modules must be called
  • this should result in observations or plain datapoints, which will be saved to db and forwarded in processing
  • current value must be computed for all observations
  • load relevant section of observation's history and perform configured history analysis. Result = plain values
  • load plain attributes saved in master collection
  • A record of described plain data makes a profile
  • Profile is additionally extended by related entities
  • Callbacks for data correlation and fusion should happen here
  • Save the complete results into database as snapshots

SnapShooter

SnapShooter(db: EntityDatabase, task_queue_writer: TaskQueueWriter, platform_config: PlatformConfig, scheduler: Scheduler, elog: Optional[EventGroupType] = None)

Class responsible for creating entity snapshots.

Source code in dp3/snapshots/snapshooter.py

def __init__(
    self,
    db: EntityDatabase,
    task_queue_writer: TaskQueueWriter,
    platform_config: PlatformConfig,
    scheduler: Scheduler,
    elog: Optional[EventGroupType] = None,
) -> None:
    self.log = logging.getLogger("SnapShooter")

    self.db = db
    self.task_queue_writer = task_queue_writer
    self.model_spec = platform_config.model_spec
    self.entity_relation_attrs = defaultdict(dict)
    for (entity, attr), _ in self.model_spec.relations.items():
        self.entity_relation_attrs[entity][attr] = True
    for entity in self.model_spec.entities:
        self.entity_relation_attrs[entity]["_id"] = True

    self.worker_index = platform_config.process_index
    self.worker_cnt = platform_config.num_processes
    self.config = SnapShooterConfig.model_validate(platform_config.config.get("snapshots"))

    self.elog = elog or DummyEventGroup()

    self._timeseries_hooks = SnapshotTimeseriesHookContainer(self.log, self.model_spec, elog)
    self._correlation_hooks = SnapshotCorrelationHookContainer(self.log, self.model_spec, elog)
    self._init_hooks: list[Callable[[], list[DataPointTask]]] = []
    self._finalize_hooks: list[Callable[[], list[DataPointTask]]] = []

    queue = f"{platform_config.app_name}-worker-{platform_config.process_index}-snapshots"
    self.snapshot_queue_reader = TaskQueueReader(
        callback=self.process_snapshot_task,
        parse_task=Snapshot.model_validate_json,
        app_name=platform_config.app_name,
        worker_index=platform_config.process_index,
        rabbit_config=platform_config.config.get("processing_core.msg_broker", {}),
        queue=queue,
        priority_queue=False,
        parent_logger=self.log,
    )
    scheduler.register(self.snapshot_queue_reader.watchdog, second="10,40")

    self.snapshot_entities = [
        entity for entity, spec in self.model_spec.entities.items() if spec.snapshot
    ]
    self.log.info("Snapshots will be created for entities: %s", self.snapshot_entities)
    self.link_mirrors = defaultdict(dict)
    for (entity, attr), spec in self.model_spec.relations.items():
        if spec.is_relation and spec.is_mirrored:
            self.link_mirrors[spec.relation_to][spec.mirror_as] = (entity, attr)
    self.log.info("Link mirrors: %s", self.link_mirrors)

    # Get link cache
    self.cache = self.db.get_module_cache("Link")

    if platform_config.process_index != 0:
        self.log.debug(
            "Snapshot task creation will be disabled in this worker to avoid race conditions."
        )
        self.snapshot_queue_writer = None
        return

    self.snapshot_queue_writer = TaskQueueWriter(
        platform_config.app_name,
        platform_config.num_processes,
        platform_config.config.get("processing_core.msg_broker"),
        f"{platform_config.app_name}-main-snapshot-exchange",
        parent_logger=self.log,
    )

    # Schedule snapshot period
    snapshot_cron = self.config.creation_rate.model_dump(exclude_none=True)
    scheduler.register(self.make_snapshots, **snapshot_cron)

start

start()

Connect to RabbitMQ and start consuming from TaskQueue.

Source code in dp3/snapshots/snapshooter.py

def start(self):
    """Connect to RabbitMQ and start consuming from TaskQueue."""
    self.log.info("Connecting to RabbitMQ")
    self.snapshot_queue_reader.connect()
    self.snapshot_queue_reader.check()  # check presence of needed queues
    if self.snapshot_queue_writer is not None:
        self.snapshot_queue_writer.connect()
        self.snapshot_queue_writer.check()  # check presence of needed exchanges

    self.snapshot_queue_reader.start()
    self.log.info("Following used links detected: %s", self._correlation_hooks.used_links)
    self.log.info("SnapShooter started.")

stop

stop()

Stop consuming from TaskQueue, disconnect from RabbitMQ.

Source code in dp3/snapshots/snapshooter.py

def stop(self):
    """Stop consuming from TaskQueue, disconnect from RabbitMQ."""
    self.snapshot_queue_reader.stop()

    if self.snapshot_queue_writer is not None:
        self.snapshot_queue_writer.disconnect()
    self.snapshot_queue_reader.disconnect()

register_timeseries_hook

register_timeseries_hook(hook: Callable[[str, str, list[dict]], list[DataPointTask]], entity_type: str, attr_type: str)

Registers passed timeseries hook to be called during snapshot creation.

Binds hook to specified entity_type and attr_type (though same hook can be bound multiple times).

Parameters:

Name	Type	Description	Default
hook	Callable[[str, str, list[dict]], list[DataPointTask]]	hook callable should expect entity_type, attr_type and attribute history as arguments and return a list of DataPointTask objects.	required
entity_type	str	specifies entity type	required
attr_type	str	specifies attribute type	required

Raises:

Type	Description
ValueError	If entity_type and attr_type do not specify a valid timeseries attribute, a ValueError is raised.

Source code in dp3/snapshots/snapshooter.py

def register_timeseries_hook(
    self,
    hook: Callable[[str, str, list[dict]], list[DataPointTask]],
    entity_type: str,
    attr_type: str,
):
    """
    Registers passed timeseries hook to be called during snapshot creation.

    Binds hook to specified `entity_type` and `attr_type` (though same hook can be bound
    multiple times).

    Args:
        hook: `hook` callable should expect entity_type, attr_type and attribute
            history as arguments and return a list of `DataPointTask` objects.
        entity_type: specifies entity type
        attr_type: specifies attribute type

    Raises:
        ValueError: If entity_type and attr_type do not specify a valid timeseries attribute,
            a ValueError is raised.
    """
    self._timeseries_hooks.register(hook, entity_type, attr_type)

register_correlation_hook

register_correlation_hook(hook: Callable[[str, dict], Union[None, list[DataPointTask]]], entity_type: str, depends_on: list[list[str]], may_change: list[list[str]])

Registers passed hook to be called during snapshot creation.

Binds hook to specified entity_type (though same hook can be bound multiple times).

entity_type and attribute specifications are validated, ValueError is raised on failure.

Parameters:

Name	Type	Description	Default
hook	Callable[[str, dict], Union[None, list[DataPointTask]]]	hook callable should expect entity type as str and its current values, including linked entities, as dict Can optionally return a list of DataPointTask objects to perform.	required
entity_type	str	specifies entity type	required
depends_on	list[list[str]]	each item should specify an attribute that is depended on in the form of a path from the specified entity_type to individual attributes (even on linked entities).	required
may_change	list[list[str]]	each item should specify an attribute that hook may change. specification format is identical to depends_on.	required

Raises:

Type	Description
ValueError	On failure of specification validation.

Source code in dp3/snapshots/snapshooter.py

def register_correlation_hook(
    self,
    hook: Callable[[str, dict], Union[None, list[DataPointTask]]],
    entity_type: str,
    depends_on: list[list[str]],
    may_change: list[list[str]],
):
    """
    Registers passed hook to be called during snapshot creation.

    Binds hook to specified entity_type (though same hook can be bound multiple times).

    `entity_type` and attribute specifications are validated, `ValueError` is raised on failure.

    Args:
        hook: `hook` callable should expect entity type as str
            and its current values, including linked entities, as dict
            Can optionally return a list of DataPointTask objects to perform.
        entity_type: specifies entity type
        depends_on: each item should specify an attribute that is depended on
            in the form of a path from the specified entity_type to individual attributes
            (even on linked entities).
        may_change: each item should specify an attribute that `hook` may change.
            specification format is identical to `depends_on`.

    Raises:
        ValueError: On failure of specification validation.
    """
    self._correlation_hooks.register(hook, entity_type, depends_on, may_change)

register_run_init_hook

register_run_init_hook(hook: Callable[[], list[DataPointTask]])

Registers passed hook to be called before a run of snapshot creation begins.

Parameters:

Name	Type	Description	Default
hook	Callable[[], list[DataPointTask]]	hook callable should expect no arguments and return a list of DataPointTask objects to perform.	required

Source code in dp3/snapshots/snapshooter.py

def register_run_init_hook(self, hook: Callable[[], list[DataPointTask]]):
    """
    Registers passed hook to be called before a run of  snapshot creation begins.

    Args:
        hook: `hook` callable should expect no arguments and
            return a list of DataPointTask objects to perform.
    """
    self._init_hooks.append(hook)

register_run_finalize_hook

register_run_finalize_hook(hook: Callable[[], list[DataPointTask]])

Registers passed hook to be called after a run of snapshot creation ends.

Parameters:

Name	Type	Description	Default
hook	Callable[[], list[DataPointTask]]	hook callable should expect no arguments and return a list of DataPointTask objects to perform.	required

Source code in dp3/snapshots/snapshooter.py

def register_run_finalize_hook(self, hook: Callable[[], list[DataPointTask]]):
    """
    Registers passed hook to be called after a run of  snapshot creation ends.

    Args:
        hook: `hook` callable should expect no arguments and
            return a list of DataPointTask objects to perform.
    """
    self._finalize_hooks.append(hook)

make_snapshots

make_snapshots()

Creates snapshots for all entities currently active in database.

Source code in dp3/snapshots/snapshooter.py

def make_snapshots(self):
    """Creates snapshots for all entities currently active in database."""
    time = datetime.utcnow()
    self.db.save_metadata(
        time,
        {
            "task_creation_start": time,
            "entities": 0,
            "components": 0,
            "workers_finished": 0,
            "linked_finished": False,
        },
    )
    # Broadcast run start
    self.snapshot_queue_writer.broadcast_task(
        task=Snapshot(type=SnapshotMessageType.run_start, time=time)
    )

    # distribute list of possibly linked entities to all workers
    cached = self.get_cached_link_entity_ids()
    self.log.debug("Broadcasting %s cached linked entities", len(cached))
    self.snapshot_queue_writer.broadcast_task(
        task=Snapshot(entities=cached, time=time, type=SnapshotMessageType.linked_entities)
    )

    # Load links only for a reduced set of entities
    self.log.debug("Loading linked entities.")

    times = {}
    counts = {"entities": 0, "components": 0}
    try:
        linked_entities = self.get_linked_entities(time, cached)
        times["components_loaded"] = datetime.utcnow()

        for i, linked_entities_component in enumerate(linked_entities):
            counts["entities"] += len(linked_entities_component)
            counts["components"] += 1

            self.snapshot_queue_writer.put_task(
                task=Snapshot(
                    entities=linked_entities_component,
                    time=time,
                    type=SnapshotMessageType.task,
                    final=(i + 1 == len(linked_entities)),
                )
            )

        if len(linked_entities) == 0:
            self.db.update_metadata(time, metadata={"linked_finished": True})
    except pymongo.errors.CursorNotFound as err:
        self.log.exception(err)
    finally:
        times["task_creation_end"] = datetime.utcnow()
        self.db.update_metadata(
            time,
            metadata=times,
            increase=counts,
        )

    # Broadcast run end
    self.snapshot_queue_writer.broadcast_task(
        task=Snapshot(type=SnapshotMessageType.run_end, time=time)
    )

get_linked_entities

get_linked_entities(time: datetime, cached_linked_entities: list[tuple[str, str]])

Get weakly connected components from entity graph.

Source code in dp3/snapshots/snapshooter.py

def get_linked_entities(self, time: datetime, cached_linked_entities: list[tuple[str, str]]):
    """Get weakly connected components from entity graph."""
    visited_entities = set()
    entity_to_component = {}
    linked_components = []
    for etype, eid in cached_linked_entities:
        master_record = self.db.get_master_record(
            etype, eid, projection=self.entity_relation_attrs[etype]
        ) or {"_id": eid}

        if not self.config.keep_empty and len(master_record) == 1:
            continue

        if (etype, master_record["_id"]) not in visited_entities:
            # Get entities linked by current entity
            current_values = self.get_values_at_time(etype, master_record, time)
            linked_entities = self.load_linked_entity_ids(etype, current_values, time)

            # Set linked as visited
            visited_entities.update(linked_entities)

            # Update component, take all connected components into account
            have_component = linked_entities & set(entity_to_component.keys())
            if have_component:
                for entity in have_component:
                    component = entity_to_component[entity]
                    linked_entities.update(component)
            entity_to_component.update({entity: linked_entities for entity in linked_entities})

    # Make a list of unique components
    visited_entities.clear()
    for entity, component in entity_to_component.items():
        if entity in visited_entities:
            continue
        visited_entities.update(component)
        linked_components.append(component)

    return linked_components

process_snapshot_task

process_snapshot_task(msg_id, task: Snapshot)

Acknowledges the received message and makes a snapshot according to the task.

This function should not be called directly, but set as callback for TaskQueueReader.

Source code in dp3/snapshots/snapshooter.py

def process_snapshot_task(self, msg_id, task: Snapshot):
    """
    Acknowledges the received message and makes a snapshot according to the `task`.

    This function should not be called directly, but set as callback for TaskQueueReader.
    """
    if not self.snapshot_queue_reader.ack(msg_id):
        self.log.warning("Acking message %s failed, will not process.", msg_id)
        return
    if task.type == SnapshotMessageType.task:
        self.make_snapshot(task)
    elif task.type == SnapshotMessageType.linked_entities:
        self.make_snapshots_by_hash(task)
    elif task.type == SnapshotMessageType.run_start:
        self.log.debug("Run start, running init hooks")
        self._run_hooks(self._init_hooks)
    elif task.type == SnapshotMessageType.run_end:
        self.log.debug("Run end, running finalize hooks")
        self._run_hooks(self._finalize_hooks)
    else:
        raise ValueError("Unknown SnapshotMessageType.")

make_snapshots_by_hash

make_snapshots_by_hash(task: Snapshot)

Make snapshots for all entities with routing key belonging to this worker.

Source code in dp3/snapshots/snapshooter.py

def make_snapshots_by_hash(self, task: Snapshot):
    """
    Make snapshots for all entities with routing key belonging to this worker.
    """
    self.log.debug("Creating snapshots for worker portion by hash.")
    have_links = set(task.entities)
    entity_cnt = 0
    for etype in self.snapshot_entities:
        records_cursor = self.db.get_worker_master_records(
            self.worker_index, self.worker_cnt, etype, no_cursor_timeout=True
        )
        for attempt in range(RETRY_COUNT):
            try:
                entity_cnt += self.make_linkless_snapshots(
                    etype, records_cursor, task.time, have_links
                )
            except Exception as err:
                self.log.exception("Uncaught exception while creating snapshots: %s", err)
                if attempt < RETRY_COUNT - 1:
                    self.log.info("Retrying snapshot creation for '%s' due to errors.", etype)
                continue
            finally:
                records_cursor.close()
            break
        else:
            self.log.error(
                "Failed to create snapshots for '%s' after %s attempts.", etype, attempt + 1
            )
    self.db.update_metadata(
        task.time,
        metadata={},
        increase={"entities": entity_cnt, "components": entity_cnt, "workers_finished": 1},
        worker_id=0,
    )
    self.log.debug("Worker snapshot creation done.")

make_linkless_snapshot

make_linkless_snapshot(entity_type: str, master_record: dict, time: datetime)

Make a snapshot for given entity master_record and time.

Runs timeseries and correlation hooks. The resulting snapshot is saved into DB.

Source code in dp3/snapshots/snapshooter.py

def make_linkless_snapshot(self, entity_type: str, master_record: dict, time: datetime):
    """
    Make a snapshot for given entity `master_record` and `time`.

    Runs timeseries and correlation hooks.
    The resulting snapshot is saved into DB.
    """
    self.run_timeseries_processing(entity_type, master_record)
    values = self.get_values_at_time(entity_type, master_record, time)
    self.add_mirrored_links(entity_type, values)
    entity_values = {(entity_type, master_record["_id"]): values}

    tasks = self._correlation_hooks.run(entity_values)
    for task in tasks:
        self.task_queue_writer.put_task(task)

    assert len(entity_values) == 1, "Expected a single entity."
    for record in entity_values.values():
        return record

add_mirrored_links

add_mirrored_links(entity_type: str, values: dict)

This function adds mirrored links to the dict with current values of an entity.

The links are added in the same format as normal links, i.e. as a list of dicts.

Source code in dp3/snapshots/snapshooter.py

def add_mirrored_links(self, entity_type: str, values: dict):
    """
    This function adds mirrored links to the dict with current values of an entity.

    The links are added in the same format as normal links, i.e. as a list of dicts.
    """
    if entity_type in self.link_mirrors:
        for mirror_name, (etype, attr) in self.link_mirrors[entity_type].items():
            link_sources = self.cache.aggregate(
                [
                    {
                        "$match": {
                            "to": f"{entity_type}#{values['eid']}",
                            "using_attr": f"{etype}#{attr}",
                        }
                    },
                    {"$project": {"from": 1}},
                ]
            )
            values[mirror_name] = [
                {"eid": source["from"].split("#", maxsplit=1)[1]} for source in link_sources
            ]

make_snapshot

make_snapshot(task: Snapshot)

Make a snapshot for entities and time specified by task.

Runs timeseries and correlation hooks. The resulting snapshots are saved into DB.

Source code in dp3/snapshots/snapshooter.py

def make_snapshot(self, task: Snapshot):
    """
    Make a snapshot for entities and time specified by `task`.

    Runs timeseries and correlation hooks.
    The resulting snapshots are saved into DB.
    """
    entity_values = {}
    for entity_type, entity_id in task.entities:
        record = self.db.get_master_record(entity_type, entity_id) or {"_id": entity_id}
        if not self.config.keep_empty and len(record) == 1:
            continue

        self.run_timeseries_processing(entity_type, record)
        values = self.get_values_at_time(entity_type, record, task.time)
        self.add_mirrored_links(entity_type, values)
        entity_values[entity_type, entity_id] = values

    self.link_loaded_entities(entity_values)
    created_tasks = self._correlation_hooks.run(entity_values)
    for created_task in created_tasks:
        self.task_queue_writer.put_task(created_task)

    # unlink entities again
    for (rtype, _rid), record in entity_values.items():
        for attr, value in record.items():
            if (rtype, attr) not in self.model_spec.relations:
                continue
            spec = self.model_spec.relations[rtype, attr]
            if spec.t == AttrType.OBSERVATIONS and spec.multi_value:
                for val in value:
                    self._remove_record_from_value(spec, val)
            else:
                self._remove_record_from_value(spec, value)

    for (rtype, _rid), record in entity_values.items():
        if len(record) == 1 and not self.config.keep_empty:
            continue
        self.db.save_snapshot(rtype, record, task.time)

    if task.final:
        self.db.update_metadata(task.time, metadata={"linked_finished": True}, worker_id=0)

run_timeseries_processing

run_timeseries_processing(entity_type, master_record)

• all registered timeseries processing modules must be called
• this should result in observations or plain datapoints, which will be saved to db and forwarded in processing

Source code in dp3/snapshots/snapshooter.py

def run_timeseries_processing(self, entity_type, master_record):
    """
    - all registered timeseries processing modules must be called
      - this should result in `observations` or `plain` datapoints, which will be saved to db
        and forwarded in processing
    """
    tasks = []
    for attr, attr_spec in self.model_spec.entity_attributes[entity_type].items():
        if attr_spec.t == AttrType.TIMESERIES and attr in master_record:
            new_tasks = self._timeseries_hooks.run(entity_type, attr, master_record[attr])
            tasks.extend(new_tasks)

    self.extend_master_record(entity_type, master_record, tasks)
    for task in tasks:
        self.task_queue_writer.put_task(task)

extend_master_record staticmethod

extend_master_record(etype, master_record, new_tasks: list[DataPointTask])

Update existing master record with datapoints from new tasks

Source code in dp3/snapshots/snapshooter.py

@staticmethod
def extend_master_record(etype, master_record, new_tasks: list[DataPointTask]):
    """Update existing master record with datapoints from new tasks"""
    for task in new_tasks:
        for datapoint in task.data_points:
            if datapoint.etype != etype:
                continue
            dp_dict = datapoint.dict(include={"v", "t1", "t2", "c"})
            if datapoint.attr in master_record:
                master_record[datapoint.attr].append()
            else:
                master_record[datapoint.attr] = [dp_dict]

load_linked_entity_ids

load_linked_entity_ids(entity_type: str, current_values: dict, time: datetime)

Loads the subgraph of entities linked to the current entity, returns a list of their types and ids.

Source code in dp3/snapshots/snapshooter.py

def load_linked_entity_ids(self, entity_type: str, current_values: dict, time: datetime):
    """
    Loads the subgraph of entities linked to the current entity,
    returns a list of their types and ids.
    """
    loaded_entity_ids = {(entity_type, current_values["eid"])}
    linked_entity_ids_to_process = (
        self.get_linked_entity_ids(entity_type, current_values) - loaded_entity_ids
    )

    while linked_entity_ids_to_process:
        entity_identifiers = linked_entity_ids_to_process.pop()
        linked_etype, linked_eid = entity_identifiers
        relevant_attributes = self.entity_relation_attrs[linked_etype]
        record = self.db.get_master_record(
            linked_etype, linked_eid, projection=relevant_attributes
        ) or {"_id": linked_eid}
        linked_values = self.get_values_at_time(linked_etype, record, time)

        linked_entity_ids_to_process.update(
            self.get_linked_entity_ids(entity_type, linked_values) - set(loaded_entity_ids)
        )
        loaded_entity_ids.add((linked_etype, linked_eid))

    return loaded_entity_ids

get_linked_entity_ids

get_linked_entity_ids(entity_type: str, current_values: dict) -> set[tuple[str, str]]

Returns a set of tuples (entity_type, entity_id) identifying entities linked by current_values.

Source code in dp3/snapshots/snapshooter.py

def get_linked_entity_ids(self, entity_type: str, current_values: dict) -> set[tuple[str, str]]:
    """
    Returns a set of tuples (entity_type, entity_id) identifying entities linked by
    `current_values`.
    """
    related_entity_ids = set()
    for attr, val in current_values.items():
        if (entity_type, attr) not in self._correlation_hooks.used_links:
            continue
        attr_spec = self.model_spec.relations[entity_type, attr]
        if attr_spec.t == AttrType.OBSERVATIONS and attr_spec.multi_value:
            for v in val:
                related_entity_ids.update(self._get_link_entity_ids(attr_spec, v))
        else:
            related_entity_ids.update(self._get_link_entity_ids(attr_spec, val))
    return related_entity_ids

get_value_at_time

get_value_at_time(attr_spec: AttrSpecObservations, attr_history, time: datetime) -> tuple[Any, float]

Get current value of an attribute from its history. Assumes multi_value = False.

Source code in dp3/snapshots/snapshooter.py

def get_value_at_time(
    self, attr_spec: AttrSpecObservations, attr_history, time: datetime
) -> tuple[Any, float]:
    """Get current value of an attribute from its history. Assumes `multi_value = False`."""
    return max(
        (
            (point["v"], self.extrapolate_confidence(point, time, attr_spec.history_params))
            for point in attr_history
        ),
        key=lambda val_conf: val_conf[1],
        default=(None, 0.0),
    )

get_multi_value_at_time

get_multi_value_at_time(attr_spec: AttrSpecObservations, attr_history, time: datetime) -> tuple[list, list[float]]

Get current value of a multi_value attribute from its history.

Source code in dp3/snapshots/snapshooter.py

def get_multi_value_at_time(
    self, attr_spec: AttrSpecObservations, attr_history, time: datetime
) -> tuple[list, list[float]]:
    """Get current value of a multi_value attribute from its history."""
    if attr_spec.data_type.hashable:
        values_with_confidence = defaultdict(float)
        for point in attr_history:
            value = point["v"]
            confidence = self.extrapolate_confidence(point, time, attr_spec.history_params)
            if confidence > 0.0 and values_with_confidence[value] < confidence:
                values_with_confidence[value] = confidence
        return list(values_with_confidence.keys()), list(values_with_confidence.values())
    else:
        values = []
        confidence_list = []
        for point in attr_history:
            value = point["v"]
            confidence = self.extrapolate_confidence(point, time, attr_spec.history_params)
            if value in values:
                i = values.index(value)
                confidence_list[i] = max(confidence_list[i], confidence)
            elif confidence > 0.0:
                values.append(value)
                confidence_list.append(confidence)
        return values, confidence_list

extrapolate_confidence staticmethod

extrapolate_confidence(datapoint: dict, time: datetime, history_params: ObservationsHistoryParams) -> float

Get the confidence value at given time.

Source code in dp3/snapshots/snapshooter.py

@staticmethod
def extrapolate_confidence(
    datapoint: dict, time: datetime, history_params: ObservationsHistoryParams
) -> float:
    """Get the confidence value at given time."""
    t1 = datapoint["t1"]
    t2 = datapoint["t2"]
    base_confidence = datapoint["c"]

    if time < t1:
        if time <= t1 - history_params.pre_validity:
            return 0.0
        return base_confidence * (1 - (t1 - time) / history_params.pre_validity)
    if time <= t2:
        return base_confidence  # completely inside the (strict) interval
    if time >= t2 + history_params.post_validity:
        return 0.0
    return base_confidence * (1 - (time - t2) / history_params.post_validity)