time_windowed_features

time_windowed_features

TimeWindowedFeatureTable

TimeWindowedFeatureTable(
    dataframe: DataFrame, column_name: str = "systime"
)

Bases: Base

Build ML-ready feature tables from segmented timeseries data.

Takes the output of SegmentProcessor.apply_ranges and computes statistical metrics per UUID within fixed-size time windows (e.g. every 1 minute).

Methods: - compute_long: One row per (time_window, uuid, segment). Long format. - compute: One row per time_window with columns {uuid}__{metric}. Wide format.

compute_long classmethod

compute_long(
    dataframe: DataFrame,
    freq: str = "1min",
    time_column: str = "systime",
    uuid_column: str = "uuid",
    value_column: str = "value_double",
    segment_column: Optional[str] = "segment_value",
    metrics: Optional[List[str]] = None,
) -> pd.DataFrame

Compute statistical metrics per UUID per time window (long format).

Groups the input data into fixed-size time windows using freq and computes numeric statistics for each (time_window, uuid, segment) group.

Parameters:

Name	Type	Description	Default
dataframe	DataFrame	Input DataFrame (typically output of SegmentProcessor.apply_ranges).	required
freq	str	Pandas frequency string for the time window size (e.g. '1min', '30s', '5min').	'1min'
time_column	str	Column containing timestamps.	'systime'
uuid_column	str	Column identifying each timeseries signal.	'uuid'
value_column	str	Column containing numeric values.	'value_double'
segment_column	Optional[str]	Column to sub-group by (e.g. 'segment_value'). Set to None to ignore segments entirely.	'segment_value'
metrics	Optional[List[str]]	Subset of metric names to compute. None uses all 19.	None

Returns:

Type	Description
DataFrame	DataFrame with columns [time_window, uuid, segment_value?, sample_count, metric_1, ...].

compute classmethod

compute(
    dataframe: DataFrame,
    freq: str = "1min",
    time_column: str = "systime",
    uuid_column: str = "uuid",
    value_column: str = "value_double",
    segment_column: Optional[str] = "segment_value",
    metrics: Optional[List[str]] = None,
    column_separator: str = "__",
) -> pd.DataFrame

Compute a wide-format feature table with one row per time window.

Each column is named {uuid}{separator}{metric} (e.g. temperature__mean). Windows where a UUID has insufficient data are filled with NaN.

Parameters:

Name	Type	Description	Default
dataframe	DataFrame	Input DataFrame (typically output of SegmentProcessor.apply_ranges).	required
freq	str	Pandas frequency string for the time window size.	'1min'
time_column	str	Column containing timestamps.	'systime'
uuid_column	str	Column identifying each timeseries signal.	'uuid'
value_column	str	Column containing numeric values.	'value_double'
segment_column	Optional[str]	Column to sub-group by. Set to None to ignore segments.	'segment_value'
metrics	Optional[List[str]]	Subset of metric names to compute. None uses all 19.	None
column_separator	str	Separator between uuid and metric in wide column names.	'__'

Returns:

Type	Description
DataFrame	DataFrame with columns [time_window, segment_value?, {uuid}__{metric}, ...].