BatchTrackingEvents

Batches, their durations and yields from a batch-ID string signal. A batch is a run of constant batch-ID values; a new batch starts when the value changes.

ts_shape.events.production.batch_tracking · Production guide · API reference

Example

import pandas as pd
import ts_shape
from ts_shape.events.production.batch_tracking import BatchTrackingEvents

df = pd.concat([
    ts_shape.make_id_signal("line1:batch_id", ["B-001", "B-002", "B-001"], hold=60, freq="1min"),
    ts_shape.make_timeseries(["line1:part_counter"], n_points=180, freq="1min",
                             baseline=0, noise=0, drift=360, value_column="value_integer"),
], ignore_index=True)

bt = BatchTrackingEvents(df, batch_uuid="line1:batch_id")
print(bt.detect_batches()[["batch_id", "start", "end", "duration_seconds"]])
yields = bt.batch_yield(counter_uuid="line1:part_counter")
matrix = bt.batch_transition_matrix(layout="wide")

Methods

Method	Returns
detect_batches()	one row per batch: batch_id, start (first sample), end (last sample), duration_seconds, sample_count
batch_duration_statistics()	per batch ID: count, min_/avg_/max_/total_duration_seconds
batch_yield(counter_uuid, handle_resets=True)	per batch: batch_id, start, end, duration_seconds, quantity (+ resets)
batch_transition_matrix(layout='long')	from_state, to_state, transition_count; layout='wide' gives the from/to pivot table with a total column

Notes

• batch_yield with handle_resets=True books every reset-aware counter increment to the batch that was running when it was produced: a reading reports the production since the previous reading, so it belongs to the batch whose ID was last seen strictly before it. Production between the last sample of one batch and the first sample of the next counts for the earlier batch, and the batch quantities add up to the counter total.
• Readings before the first or after the last batch-ID sample, or while the batch ID is empty, belong to no batch.
• handle_resets=False keeps max(0, last - first) over each batch's [start, end].
• With fewer than two batches the transition matrix is empty (long layout keeps its columns).