shift_reporting

shift_reporting ¤

Shift-based production reporting.

Simple module for shift summaries: - Production by shift - Shift performance comparison - Shift targets and actuals

ShiftReporting ¤

ShiftReporting(
    dataframe: DataFrame,
    *,
    time_column: str = "systime",
    shift_definitions: Optional[
        Dict[str, Tuple[str, str]]
    ] = None
)

Bases: Base

Simple shift-based production reporting.

Each UUID represents one signal: - counter_uuid: production counter - part_id_uuid: part number (optional)

Example usage

reporter = ShiftReporting(df, shift_definitions={ "day": ("06:00", "14:00"), "afternoon": ("14:00", "22:00"), "night": ("22:00", "06:00"), })

Production per shift¤

shift_prod = reporter.shift_production( counter_uuid='counter_signal', part_id_uuid='part_number_signal' )

Compare shifts¤

comparison = reporter.shift_comparison(counter_uuid='counter_signal')

Initialize shift reporter.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data	required
`time_column`	`str`	Name of timestamp column (default: 'systime')	`'systime'`
`shift_definitions`	`Optional[Dict[str, Tuple[str, str]]]`	Dictionary mapping shift names to (start, end) times Default: 3-shift operation (06:00-14:00, 14:00-22:00, 22:00-06:00)	`None`

Example shift_definitions

{ "shift_1": ("06:00", "14:00"), "shift_2": ("14:00", "22:00"), "shift_3": ("22:00", "06:00"), }

shift_production ¤

shift_production(
    counter_uuid: str,
    part_id_uuid: Optional[str] = None,
    *,
    value_column_counter: str = "value_integer",
    value_column_part: str = "value_string",
    date: Optional[str] = None
) -> pd.DataFrame

Production quantity per shift.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	Production counter UUID	required
`part_id_uuid`	`Optional[str]`	Part number UUID (optional, for part-specific production)	`None`
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`value_column_part`	`str`	Column containing part numbers	`'value_string'`
`date`	`Optional[str]`	Specific date in 'YYYY-MM-DD' format (optional)	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with production by shift:
`DataFrame`	date: Production date
`DataFrame`	shift: Shift name
`DataFrame`	part_number: Part number (if part_id_uuid provided)
`DataFrame`	quantity: Parts produced

Example

shift_production('counter', part_id_uuid='part_id') date shift part_number quantity 0 2024-01-01 shift_1 PART_A 450 1 2024-01-01 shift_2 PART_A 425 2 2024-01-01 shift_3 PART_A 380

shift_comparison ¤

shift_comparison(
    counter_uuid: str,
    *,
    value_column_counter: str = "value_integer",
    days: int = 7
) -> pd.DataFrame

Compare shift performance over recent days.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	Production counter UUID	required
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`days`	`int`	Number of recent days to analyze (default: 7)	`7`

Returns:

Type	Description
`DataFrame`	DataFrame with shift comparison:
`DataFrame`	shift: Shift name
`DataFrame`	avg_quantity: Average production per shift
`DataFrame`	min_quantity: Minimum production
`DataFrame`	max_quantity: Maximum production
`DataFrame`	std_quantity: Standard deviation
`DataFrame`	days_count: Number of days included

Example

shift_comparison('counter', days=7) shift avg_quantity min_quantity max_quantity std_quantity days_count 0 shift_1 445 420 465 15.2 7 1 shift_2 430 405 450 12.8 7 2 shift_3 385 360 410 18.5 7

shift_targets ¤

shift_targets(
    counter_uuid: str,
    targets: Dict[str, float],
    *,
    value_column_counter: str = "value_integer",
    date: Optional[str] = None
) -> pd.DataFrame

Compare actual production to shift targets.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	Production counter UUID	required
`targets`	`Dict[str, float]`	Dictionary mapping shift names to target quantities	required
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`date`	`Optional[str]`	Specific date in 'YYYY-MM-DD' format (optional)	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with target comparison:
`DataFrame`	date: Production date
`DataFrame`	shift: Shift name
`DataFrame`	actual: Actual production
`DataFrame`	target: Target production
`DataFrame`	variance: Difference (actual - target)
`DataFrame`	achievement_pct: Percentage of target achieved

Example

shift_targets('counter', targets={'shift_1': 450, 'shift_2': 450, 'shift_3': 400}) date shift actual target variance achievement_pct 0 2024-01-01 shift_1 445 450 -5 98.9 1 2024-01-01 shift_2 465 450 15 103.3 2 2024-01-01 shift_3 390 400 -10 97.5

best_and_worst_shifts ¤

best_and_worst_shifts(
    counter_uuid: str,
    *,
    value_column_counter: str = "value_integer",
    days: int = 30
) -> Dict[str, pd.DataFrame]

Identify best and worst performing shifts.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	Production counter UUID	required
`value_column_counter`	`str`	Column containing counter values	`'value_integer'`
`days`	`int`	Number of recent days to analyze (default: 30)	`30`

Returns:

Type	Description
`Dict[str, DataFrame]`	Dictionary with:
`Dict[str, DataFrame]`	'best': Top 5 best shifts
`Dict[str, DataFrame]`	'worst': Top 5 worst shifts

Example

results = best_and_worst_shifts('counter') results['best'] date shift quantity 0 2024-01-15 shift_2 495 1 2024-01-18 shift_1 490 2 2024-01-22 shift_2 485