cycles_extractor

cycles_extractor ¤

CycleExtractor(
    dataframe: DataFrame,
    start_uuid: str,
    end_uuid: Optional[str] = None,
    value_change_threshold: float = 0.0,
)

Bases: Base

Class for processing cycles based on different criteria.

Initializes the class with the data and the UUIDs for cycle start and end.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with cycle data	required
`start_uuid`	`str`	UUID for cycle start variable	required
`end_uuid`	`Optional[str]`	UUID for cycle end variable (defaults to start_uuid)	`None`
`value_change_threshold`	`float`	Minimum threshold for considering a value change significant (default: 0.0)	`0.0`

process_persistent_cycle() -> pd.DataFrame

Processes cycles where the value of the variable stays true during the cycle.

process_trigger_cycle() -> pd.DataFrame

Processes cycles where the value of the variable goes from true to false during the cycle.

process_separate_start_end_cycle() -> pd.DataFrame

Processes cycles where different variables indicate cycle start and end.

process_step_sequence(
    start_step: int, end_step: int
) -> pd.DataFrame

Processes cycles based on a step sequence, where specific integer values denote cycle start and end.

process_state_change_cycle() -> pd.DataFrame

Processes cycles where the start of a new cycle is the end of the previous cycle.

process_value_change_cycle() -> pd.DataFrame

Processes cycles where a change in the value indicates a new cycle.

Uses the value_change_threshold to determine if a numeric change is significant.

validate_cycles(
    cycle_df: DataFrame,
    min_duration: str = "1s",
    max_duration: str = "1h",
    warn: bool = True,
) -> pd.DataFrame

Validate cycles based on duration constraints.

Parameters:

Name	Type	Description	Default
`cycle_df`	`DataFrame`	DataFrame with cycle data (output from process_* methods)	required
`min_duration`	`str`	Minimum acceptable cycle duration (default: '1s')	`'1s'`
`max_duration`	`str`	Maximum acceptable cycle duration (default: '1h')	`'1h'`
`warn`	`bool`	Whether to log warnings for invalid cycles (default: True)	`True`

Returns:

Type	Description
`DataFrame`	DataFrame with additional 'is_valid' column and 'validation_issue' column

detect_overlapping_cycles(
    cycle_df: DataFrame, resolve: str = "flag"
) -> pd.DataFrame

Detect and optionally resolve overlapping cycles.

Parameters:

Name	Type	Description	Default
`cycle_df`	`DataFrame`	DataFrame with cycle data	required
`resolve`	`str`	How to handle overlaps - 'flag' (mark only), 'keep_first', 'keep_last', 'keep_longest'	`'flag'`

Returns:

Type	Description
`DataFrame`	DataFrame with 'has_overlap' column and potentially filtered rows

suggest_method() -> Dict[str, Any]

Suggest the best cycle extraction method based on data characteristics.

Analyzes the input DataFrame to recommend appropriate extraction method(s).

Returns:

Type	Description
`Dict[str, Any]`	Dictionary with method suggestions and reasoning

get_extraction_stats() -> Dict[str, Any]

Get statistics about the last cycle extraction.

Returns:

Type	Description
`Dict[str, Any]`	Dictionary with extraction statistics including counts, success rate, and warnings

reset_stats()

Reset extraction statistics.