part_tracking

part_tracking ¤

Production tracking by part number.

Simple, practical module for daily production reporting: - How many parts were produced? - Production by part number and time window - Daily summaries

Counters are assumed to be monotonically increasing during normal operation. Many real-world counters reset back to zero (or to a lower value) - for example at a shift change, a part change, or a controller restart. A naive last - first calculation silently loses all of the production that happened in a window where a reset occurred. Pass handle_resets=True to account for these resets, and use :meth:PartProductionTracking.detect_resets to inspect when they happened.

PartProductionTracking ¤

PartProductionTracking(
    dataframe: DataFrame, *, time_column: str = "systime"
)

Bases: Base

Track production quantities by part number.

Each UUID represents one signal: - part_id_uuid: string signal with current part number - counter_uuid: monotonic counter for production count

The counter is expected to increase as parts are produced. If the counter is reset during operation (back to zero or a lower value), enable handle_resets so the reset is treated as new production instead of being discarded.

Example usage

tracker = PartProductionTracking(df)

Hourly production by part, accounting for counter resets¤

hourly = tracker.production_by_part( part_id_uuid='part_number_signal', counter_uuid='counter_signal', window='1h', handle_resets=True, )

Daily summary¤

daily = tracker.daily_production_summary( part_id_uuid='part_number_signal', counter_uuid='counter_signal', handle_resets=True, )

When did the counter reset?¤

resets = tracker.detect_resets( part_id_uuid='part_number_signal', counter_uuid='counter_signal', )

Initialize part production tracker.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data	required
`time_column`	`str`	Name of timestamp column (default: 'systime')	`'systime'`

production_by_part ¤

production_by_part(
    part_id_uuid: str,
    counter_uuid: str,
    *,
    window: str = "1h",
    value_column_part: str = "value_string",
    value_column_counter: str = "value_integer",
    handle_resets: bool = False
) -> pd.DataFrame

Calculate production quantity per part number.

Parameters:

Name	Type	Description	Default
`part_id_uuid`	`str`	UUID for part number signal	required
`counter_uuid`	`str`	UUID for production counter	required
`window`	`str`	Time window for aggregation (e.g., '1h', '8h', '1d')	`'1h'`
`value_column_part`	`str`	Column containing part numbers	`'value_string'`
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`handle_resets`	`bool`	When True, treat a decreasing counter as a reset and count the production after the reset instead of losing it. The result gains a `resets` column with the number of resets observed in each window. When False (default), the quantity is simply `max(0, last_count - first_count)` and a window containing a reset reports 0.	`False`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	start: Start of time window
`DataFrame`	part_number: Part number/ID
`DataFrame`	quantity: Parts produced in window
`DataFrame`	first_count: Counter value at window start
`DataFrame`	last_count: Counter value at window end
`DataFrame`	resets: Number of counter resets in window (only when `handle_resets=True`)

Example

production_by_part('part_id', 'counter', window='1h') start part_number quantity first_count last_count 0 2024-01-01 08:00:00 PART_A 150 1000 1150 1 2024-01-01 09:00:00 PART_A 145 1150 1295 2 2024-01-01 10:00:00 PART_B 98 1295 1393

With handle_resets=True, a window where the counter drops from 432 to 61 reports a quantity of 61 (and resets=1) instead of 0.

detect_resets ¤

detect_resets(
    part_id_uuid: str,
    counter_uuid: str,
    *,
    value_column_part: str = "value_string",
    value_column_counter: str = "value_integer"
) -> pd.DataFrame

Find the points in time where the counter was reset.

A reset is any reading where the counter value is lower than the previous reading.

Parameters:

Name	Type	Description	Default
`part_id_uuid`	`str`	UUID for part number signal	required
`counter_uuid`	`str`	UUID for production counter	required
`value_column_part`	`str`	Column containing part numbers	`'value_string'`
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`

Returns:

Type	Description
`DataFrame`	DataFrame with one row per reset and columns:
`DataFrame`	: Timestamp of the reading after the reset
`DataFrame`	part_number: Active part number at the reset
`DataFrame`	count_before: Counter value just before the reset
`DataFrame`	count_after: Counter value just after the reset
`DataFrame`	drop: How far the counter fell (count_before - count_after)

Example

detect_resets('part_id', 'counter') systime part_number count_before count_after drop 0 2026-06-15 17:00:00 8842580 432 61 371 1 2026-06-16 19:00:00 9423376 854 0 854

daily_production_summary ¤

daily_production_summary(
    part_id_uuid: str,
    counter_uuid: str,
    *,
    value_column_part: str = "value_string",
    value_column_counter: str = "value_integer",
    handle_resets: bool = False
) -> pd.DataFrame

Daily production summary by part number.

Parameters:

Name	Type	Description	Default
`part_id_uuid`	`str`	UUID for part number signal	required
`counter_uuid`	`str`	UUID for production counter	required
`value_column_part`	`str`	Column containing part numbers	`'value_string'`
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`handle_resets`	`bool`	Account for counter resets (see :meth:`production_by_part`). Adds a `resets` column with the number of resets observed that day.	`False`

Returns:

Type	Description
`DataFrame`	DataFrame with columns:
`DataFrame`	date: Production date
`DataFrame`	part_number: Part number/ID
`DataFrame`	total_quantity: Total parts produced that day
`DataFrame`	hours_active: Number of hours with production
`DataFrame`	resets: Number of counter resets that day (only when `handle_resets=True`)

Example

daily_production_summary('part_id', 'counter') date part_number total_quantity hours_active 0 2024-01-01 PART_A 1200 8 1 2024-01-01 PART_B 850 6 2 2024-01-02 PART_A 1150 8

production_totals ¤

production_totals(
    part_id_uuid: str,
    counter_uuid: str,
    *,
    start_date: str | None = None,
    end_date: str | None = None,
    value_column_part: str = "value_string",
    value_column_counter: str = "value_integer",
    handle_resets: bool = False
) -> pd.DataFrame

Total production by part number for a date range.

Parameters:

Name	Type	Description	Default
`part_id_uuid`	`str`	UUID for part number signal	required
`counter_uuid`	`str`	UUID for production counter	required
`start_date`	`str \| None`	Start date 'YYYY-MM-DD' (optional)	`None`
`end_date`	`str \| None`	End date 'YYYY-MM-DD' (optional)	`None`
`value_column_part`	`str`	Column containing part numbers	`'value_string'`
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`handle_resets`	`bool`	Account for counter resets (see :meth:`production_by_part`). Adds a `resets` column with the total number of resets in the range.	`False`

Returns:

Type	Description
`DataFrame`	DataFrame with total production per part

Example

production_totals('part_id', 'counter', ... start_date='2024-01-01', end_date='2024-01-07') part_number total_quantity days_produced 0 PART_A 8450 5 1 PART_B 6200 4