cycle_processor

cycle_processor ¤

CycleDataProcessor ¤

CycleDataProcessor(
    cycles_df: DataFrame,
    values_df: DataFrame,
    cycle_uuid_col: str = "cycle_uuid",
    systime_col: str = "systime",
)

Bases: Base

A class to process cycle-based data and values with optimized performance. Uses pandas IntervalIndex for efficient cycle assignment instead of nested loops.

Initializes the CycleDataProcessor with cycles and values DataFrames.

Parameters:

Name	Type	Description	Default
`cycles_df`	`DataFrame`	DataFrame containing columns 'cycle_start', 'cycle_end', and 'cycle_uuid'.	required
`values_df`	`DataFrame`	DataFrame containing the values and timestamps in the 'systime' column.	required
`cycle_uuid_col`	`str`	Name of the column representing cycle UUIDs.	`'cycle_uuid'`
`systime_col`	`str`	Name of the column representing the timestamps for the values.	`'systime'`

split_by_cycle ¤

split_by_cycle() -> Dict[str, pd.DataFrame]

Splits the values DataFrame by cycles defined in the cycles DataFrame. Uses optimized interval-based assignment.

Return

Dictionary where keys are cycle_uuids and values are DataFrames with the corresponding cycle data.

merge_dataframes_by_cycle ¤

merge_dataframes_by_cycle() -> pd.DataFrame

Merges the values DataFrame with the cycles DataFrame based on the cycle time intervals. Uses optimized interval-based assignment instead of nested loops.

Return

DataFrame with an added 'cycle_uuid' column.

group_by_cycle_uuid ¤

group_by_cycle_uuid(
    data: Optional[DataFrame] = None,
) -> List[pd.DataFrame]

Group the DataFrame by the cycle_uuid column, resulting in a list of DataFrames, each containing data for one cycle.

Parameters:

Name	Type	Description	Default
`data`	`Optional[DataFrame]`	DataFrame containing the data to be grouped by cycle_uuid. If None, uses the internal values_df.	`None`

Return

List of DataFrames, each containing data for a unique cycle_uuid.

split_dataframes_by_group ¤

split_dataframes_by_group(
    dfs: List[DataFrame], column: str
) -> List[pd.DataFrame]

Splits a list of DataFrames by groups based on a specified column. This function performs a groupby operation on each DataFrame in the list and then flattens the result.

Parameters:

Name	Type	Description	Default
`dfs`	`List[DataFrame]`	List of DataFrames to be split.	required
`column`	`str`	Column name to group by.	required

Return

List of DataFrames, each corresponding to a group in the original DataFrames.

compute_cycle_statistics ¤

compute_cycle_statistics() -> pd.DataFrame

Compute statistics for each cycle.

Returns:

Type	Description
`DataFrame`	DataFrame with cycle-level statistics including duration, value counts, etc.

compare_cycles ¤

compare_cycles(
    reference_cycle_uuid: str, metric: str = "value_double"
) -> pd.DataFrame

Compare all cycles against a reference cycle.

Parameters:

Name	Type	Description	Default
`reference_cycle_uuid`	`str`	UUID of the reference cycle	required
`metric`	`str`	Column name to use for comparison	`'value_double'`

Returns:

Type	Description
`DataFrame`	DataFrame with comparison metrics for each cycle

identify_golden_cycles ¤

identify_golden_cycles(
    metric: str = "value_double",
    method: str = "low_variability",
    top_n: int = 5,
) -> List[str]

Identify the best performing cycles (golden cycles).

Parameters:

Name	Type	Description	Default
`metric`	`str`	Column name to evaluate	`'value_double'`
`method`	`str`	Method for identification ('low_variability', 'high_mean', 'target_value')	`'low_variability'`
`top_n`	`int`	Number of golden cycles to identify	`5`

Returns:

Type	Description
`List[str]`	List of cycle UUIDs identified as golden cycles