batch_tracking

batch_tracking ¤

Batch / recipe production tracking.

Detect batch boundaries from a string signal that carries the current batch ID, compute duration statistics, per-batch yield, and batch transition matrices.

BatchTrackingEvents ¤

BatchTrackingEvents(
    dataframe: DataFrame,
    batch_uuid: str,
    *,
    event_uuid: str = "prod:batch",
    value_column: str = "value_string",
    time_column: str = "systime"
)

Bases: Base

Track batch/recipe production from a batch-ID string signal.

A "batch" is defined as a contiguous period where the batch-ID value remains constant. A new batch begins when the value changes.

Example usage

batches = BatchTrackingEvents(df, batch_uuid='batch_id_signal')

detected = batches.detect_batches() stats = batches.batch_duration_stats() yields = batches.batch_yield('part_counter') transitions = batches.batch_transition_matrix()

Initialize batch tracker.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`batch_uuid`	`str`	UUID of the batch-ID signal.	required
`event_uuid`	`str`	UUID to tag derived events with.	`'prod:batch'`
`value_column`	`str`	Column holding the batch ID string.	`'value_string'`
`time_column`	`str`	Name of timestamp column.	`'systime'`

detect_batches ¤

detect_batches() -> pd.DataFrame

Detect batch start/end from value changes in the batch-ID signal.

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	batch_id: The batch identifier string.
`DataFrame`	start: Timestamp of the first sample in the batch.
`DataFrame`	end: Timestamp of the last sample in the batch.
`DataFrame`	duration_seconds: Duration of the batch.
`DataFrame`	sample_count: Number of data points in the batch.
`DataFrame`	uuid: Event UUID.
`DataFrame`	source_uuid: Source signal UUID.

batch_duration_stats ¤

batch_duration_stats() -> pd.DataFrame

Compute duration statistics grouped by batch type (batch_id).

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	batch_id
`DataFrame`	count: Number of occurrences of this batch type.
`DataFrame`	min_duration_seconds
`DataFrame`	avg_duration_seconds
`DataFrame`	max_duration_seconds
`DataFrame`	total_duration_seconds

batch_yield ¤

batch_yield(
    counter_uuid: str,
    *,
    value_column_counter: str = "value_integer"
) -> pd.DataFrame

Compute production quantity for each detected batch.

Uses a monotonic counter signal. The yield per batch is the counter increase during the batch interval.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	UUID of the monotonic part counter.	required
`value_column_counter`	`str`	Column holding counter values.	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	batch_id
`DataFrame`	start
`DataFrame`	end
`DataFrame`	duration_seconds
`DataFrame`	quantity: Parts produced during the batch.
`DataFrame`	uuid
`DataFrame`	source_uuid

batch_transition_matrix ¤

batch_transition_matrix() -> pd.DataFrame

Build a transition frequency matrix: which batch follows which.

Returns:

Type	Description
`DataFrame`	DataFrame (pivot table) where index = from_batch, columns = to_batch,
`DataFrame`	values = transition count. An extra `total` column is appended.