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impulse_query_engine.analyze.metadata.time_series_expression

SeriesType​

class SeriesType(StrEnum)

Determines how a channel's values are interpreted:

CONTINUOUS — the default; the channel is a continuous signal captured by sampling, considered valid within each [tstart, tend) interval. Value v_i was measured at tstart_i and no other value was measured until tend_i; the value between samples can be reconstructed by interpolation.

POINTS_IN_TIME — a time series of discrete events, valid only at their timestamps, with no interpolation or validity in between.

TimeSeriesSelector​

class TimeSeriesSelector(TimeSeriesExpression, RequiresDeserialization)

__init__​

def __init__(expr,
             uses_alias: bool = False,
             series_type: SeriesType = SeriesType.CONTINUOUS,
             value_type: SeriesValueType = SeriesValueType.DOUBLE)

Initialize a TimeSeriesSelector.

Arguments:

  • expr (TagExpression): Tag expression to select.
  • uses_alias (bool): Whether the channel resolves via the channel-alias table.
  • series_type (SeriesType): Which object this selector builds. The default (CONTINUOUS) builds a :class:SampleSeries; POINTS_IN_TIME builds a :class:PointsInTimeSeries. Matching is identical; only the built object and its result dtype differ. This is the plan-time source of truth for the series type.
  • value_type (SeriesValueType): For POINTS_IN_TIME, the declared value data type (DOUBLE / STRING); ignored otherwise. Drives plan-time typing and string-op gating; validated against the silver poi_channels.dtype at solve time.

dtype​

def dtype() -> T.DataType

Returns the Spark data type.

Returns:

pyspark.sql.types.DataType: BinaryType when this selector builds a :class:SampleSeries (a serialized blob). When it builds a :class:PointsInTimeSeries, the value-type-aware PointsInTimeSeries.dtype() (array<array<double>> for numeric, array<struct<tstart,value>> for string).

deserialize​

def deserialize(d)

Deserialize a :class:SampleSeries result after collection/toPandas.

A :class:PointsInTimeSeries result is serialized by get_data() (a plain [[t, v], ...] list) and needs no deserialization, so it is returned as-is; only a :class:SampleSeries (binary) blob is decoded.

Arguments:

  • d (Any): Data to deserialize.

Returns:

SampleSeries or Any: The decoded :class:SampleSeries, else d unchanged.

build​

def build(cache: SeriesCache) -> SampleSeries | PointsInTimeSeries

Instantiate the selected series from cache data.

Resolution is identical regardless of series type — resolve the matching candidates, take the first (container_id, channel_id), and let the cache build the right object. The data is authoritative for the built type: :meth:TimeSeriesCache.load_blob returns a

get_required_tag_exprs​

def get_required_tag_exprs() -> set[TagExpression]

Get required tag expressions.

Returns:

set of TagExpression: Required tag expressions.

required_tags​

def required_tags() -> set[str]

Get required tag keys.

Returns:

set of str: Required tag keys.

get_selector_expr​

def get_selector_expr()

Get selector expression.

Returns:

Any: Selector expression.

with_alias​

def with_alias(*args)

Create an alias selector.

Arguments:

  • *args: Aliases to use.

Returns:

TimeSeriesAliasSelector: Alias selector.

__str__​

def __str__()

String representation.

Returns:

str: String representation.

as_dict​

def as_dict() -> dict[str, Any]

Dictionary representation.

Returns:

dict: Dictionary representation.

from_dict​

def from_dict(obj: dict)

Construct from dictionary.

Arguments:

  • obj (dict): Dictionary containing selector data.

Returns:

TimeSeriesSelector: Selector instance.

TimeSeriesAliasSelector​

class TimeSeriesAliasSelector(TimeSeriesExpression)

__init__​

def __init__(*aliases)

Initialize a TimeSeriesAliasSelector.

Arguments:

  • *aliases (TimeSeriesSelector): Aliases to select.

dtype​

def dtype() -> T.DataType

Returns the Spark data type.

Returns:

pyspark.sql.types.DataType: Data type (BinaryType).

build​

def build(cache: SeriesCache) -> SampleSeries | PointsInTimeSeries

Build the time series from cache.

Arguments:

  • cache (SeriesCache): Cache containing time series data.

Returns:

SampleSeries or PointsInTimeSeries: Built series (aliased selectors build a :class:SampleSeries today).

get_required_tag_exprs​

def get_required_tag_exprs() -> set[TagExpression]

Get required tag expressions.

Returns:

set of TagExpression: Required tag expressions.

required_tags​

def required_tags() -> set[str]

Get required tag keys.

Returns:

set of str: Required tag keys.

get_selector_expr​

def get_selector_expr()

Get selector expression.

Returns:

Any: Selector expression.

__str__​

def __str__()

String representation.

Returns:

str: String representation.

TimeSeriesOp​

class TimeSeriesOp(TimeSeriesExpression)

__init__​

def __init__(operation, optype, *args, **kwargs)

Initialize a TimeSeriesOp.

Arguments:

  • operation (callable): The operation to apply.
  • optype (str): Type of operation.
  • *args: Arguments (like (TimeSeriesSelector<TagOp<eq(TagSelector<channel_name>,Vehicle Speed Sensor)>>, 1)) for the operation.
  • **kwargs: Keyword arguments for the operation.

get_required_tag_exprs​

def get_required_tag_exprs() -> set[TagExpression]

Get required tag expressions.

Returns:

set of TagExpression: Required tag expressions.

required_tags​

def required_tags() -> set[str]

Get required tag keys.

Returns:

set of str: Required tag keys.

get_selector_expr​

def get_selector_expr()

Get selector expression.

Returns:

Any: Selector expression.

build​

def build(cache: SeriesCache)

Build the time series from cache.

Arguments:

  • cache (SeriesCache): Cache containing time series data.

Returns:

Any: Built time series object.

__str__​

def __str__()

String representation.

Returns:

str: String representation.

as_dict​

def as_dict() -> dict[str, Any]

Dictionary representation.

Returns:

dict: Dictionary representation.

from_dict​

def from_dict(obj)

Construct from dictionary.

Arguments:

  • obj (dict): Dictionary containing operation data.

Returns:

TimeSeriesOp: Operation instance.

TimeSeriesUDF​

class TimeSeriesUDF(TimeSeriesOp)

__init__​

def __init__(func,
             *args,
             container_tags=None,
             container_metrics=None,
             **kwargs)

Initialize a TimeSeriesUDF.

Arguments:

  • func (callable): The user-defined function to apply.
  • *args: Arguments for the UDF.
  • container_tags (list of str): Container-tag keys to inject into func as a container_tags keyword argument at build time (keyword-only; not treated as an operand).
  • container_metrics (list of str): Container-metric columns to inject into func as a container_metrics keyword argument at build time (keyword-only; not treated as an operand).
  • **kwargs: Keyword arguments for the UDF.

build​

def build(cache: SeriesCache)

Build the time series from cache using the UDF.

When the UDF declared container_tags / container_metrics, the requested values are resolved from cache and passed to func as container_tags / container_metrics keyword arguments (dicts keyed by the declared names; missing values are None).

Arguments:

  • cache (SeriesCache): Cache containing time series data.

Returns:

Any: Result of applying the UDF to the built arguments.

__str__​

def __str__()

Return the string representation of the TimeSeriesUDF.

Returns:

str: String representation.

CallableTimeSeriesExpression​

class CallableTimeSeriesExpression()

__init__​

def __init__(func, container_tags=None, container_metrics=None)

Initialize a CallableTimeSeriesExpression.

Arguments:

  • func (callable): Function to wrap.
  • container_tags (list of str): Container-tag keys forwarded to each :class:TimeSeriesUDF this wrapper builds.
  • container_metrics (list of str): Container-metric columns forwarded to each :class:TimeSeriesUDF this wrapper builds.

__call__​

def __call__(*args, **kwargs)

Create a TimeSeriesUDF with the wrapped function.

Arguments:

  • *args: Arguments for the function.
  • **kwargs: Keyword arguments for the function.

Returns:

TimeSeriesUDF: UDF-wrapped expression.