ProcessingStep

Record of a single data processing operation applied to time series.

This class documents individual steps in a data processing pipeline, capturing
the type of processing performed, when it was executed, the function used,
and the parameters applied. Each step maintains a complete audit trail of
data transformations.

Processing steps are chained together to form a complete processing history,
enabling full traceability from raw data to final processed results. The
step_distance field tracks temporal shifts introduced by operations like
forecasting or lag analysis.

Field Definitions

Field	Type	Required	Default	Description
type	ProcessingType	✓	—	Category of processing operation performed
description	str	✓	—	Human-readable description of what this processing step accomplished
run_datetime	datetime	✓	—	Timestamp when this processing step was executed
requires_calibration	bool	✓	—	Whether this processing step requires calibration data or parameters
function_info	FunctionInfo	✓	—	Information about the function used for processing
parameters	None	✗	None	Parameters passed to the processing function
step_distance	int	✗	0	Number of time steps shifted (positive for future predictions, negative for lag operations)
suffix	str	✓	—	Short identifier appended to time series names (e.g., 'SMOOTH', 'FILT', 'PRED')
input_series_names	list	✗	Empty list ([])	Names of input time series used in this processing step

Detailed Field Descriptions

type

Type: ProcessingType Required: Yes

Category of processing operation performed

description

Type: str Required: Yes

Human-readable description of what this processing step accomplished

run_datetime

Type: datetime Required: Yes

Timestamp when this processing step was executed

requires_calibration

Type: bool Required: Yes

Whether this processing step requires calibration data or parameters

function_info

Type: FunctionInfo Required: Yes

Information about the function used for processing

parameters

Type: None Required: No Default: None

Parameters passed to the processing function

step_distance

Type: int Required: No Default: 0

Number of time steps shifted (positive for future predictions, negative for lag operations)

suffix

Type: str Required: Yes

Short identifier appended to time series names (e.g., 'SMOOTH', 'FILT', 'PRED')

input_series_names

Type: list Required: No Default: Empty list ([])

Names of input time series used in this processing step

Usage Example

from meteaudata.types import ProcessingStep

# Create a ProcessingStep instance
from datetime import datetime

step = ProcessingStep(
    type=ProcessingType.SMOOTHING,
    description="Applied moving average smoothing",
    run_datetime=datetime.now(),
    requires_calibration=False,
    function_info=func_info,
    suffix="SMOOTH",
    input_series_names=["temperature#1_RAW#1"]
)