TargetEvents¤

Actual against target per shift or per day, and the share of days on target, for any counter signal.

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

Example¤

import numpy as np
import pandas as pd
from ts_shape.events.production.target_tracking import TargetEvents

t = pd.date_range("2024-01-01", periods=7 * 1440, freq="1min")
df = pd.DataFrame({"systime": t, "uuid": "line1:parts", "value_integer": np.arange(len(t))})

tracker = TargetEvents(df)  # shifts shift_1/2/3: 06-14, 14-22, 22-06
by_shift = tracker.compare_to_target(metric_uuid="line1:parts",
                                     targets={"shift_1": 500, "shift_2": 480, "shift_3": 400})
daily = tracker.target_achievement_summary(metric_uuid="line1:parts", daily_target=1400)
result = tracker.target_hit_rate(metric_uuid="line1:parts", daily_target=1400)
print(result[["total_days", "days_on_target", "hit_rate_pct", "avg_achievement_pct"]])

Methods¤

Method	Returns
`compare_to_target(metric_uuid, targets)`	per date and shift: `actual`, `target`, `variance`, `achievement_pct`, `status` (+ `resets`)
`target_achievement_summary(metric_uuid, daily_target)`	per date: same columns (+ `resets`)
`target_hit_rate(metric_uuid, daily_target)`	one row: `total_days`, `days_on_target`, `hit_rate_pct`, `avg_achievement_pct`

Notes¤

status is on_target at 100 % achievement or more, warning from 90 %, else below_target. A day counts as on target at 100 % or more.
Counters are reset-aware by default (handle_resets=True); handle_resets=False uses max(0, last - first) per group.
Without shift_definitions, shifts are shift_1, shift_2, shift_3; targets keys must match. A night shift belongs to its start date (production_day=True).
target_hit_rate(..., as_frame=False) returns the deprecated dict.