base

base

Base class and architecture guidelines for ts-shape.

ts-shape uses two complementary patterns for data processing. Choose the right one based on the complexity of the operation:

Stateless classmethods — use when:

• The operation is a single, self-contained transformation (filter, calculate, convert, aggregate).
• There is no shared configuration between methods.
• The result depends only on the inputs (pure function).
• Examples: IntegerCalc, StringFilter, NumericStatistics, DateTimeFilter, LambdaProcessor.

Stateful instance classes — use when:

• Multiple related methods share configuration (column names, signal UUIDs, thresholds, tolerances).
• The constructor validates or preprocesses data once for many operations (e.g. sorting, filtering to a specific UUID).
• The domain workflow has a natural setup phase before analysis (e.g. compute control limits, then apply SPC rules).
• Examples: DataHarmonizer, OutlierDetectionEvents, MachineStateEvents, OEECalculator, all event classes.

FeaturePipeline — use when chaining multiple steps:

• Combines both patterns via add_step() (classmethods) and add_instance_step() (instance classes).
• Supports $prev / $input sentinels for multi-DataFrame wiring.
• Provides logging, timing, and intermediate result capture.
• See ts_shape.features.segment_analysis.feature_pipeline.

Base

Base(dataframe: DataFrame, column_name: str = 'systime')

Initializes the Base with a DataFrame, detects time columns, converts them to datetime, and sorts the DataFrame by the specified column (or the detected time column if applicable).

Parameters:

Name	Type	Description	Default
dataframe	DataFrame	The DataFrame to be processed.	required
column_name	str	The column to sort by. Default is 'systime'. If the column is not found or is not a time column, the class will attempt to detect other time columns.	'systime'

Raises:

Type	Description
TypeError	If dataframe is not a pandas DataFrame.

get_dataframe

get_dataframe() -> pd.DataFrame

Returns the processed DataFrame.