helikite.classes.base

Attributes

logger

Classes

BaseProcessor

Abstract base class for processing pipeline stages.

Functions

function_dependencies(required_operations, changes_df, ...)	A decorator to enforce that a method can only run if the required
get_instruments_from_cleaned_data(...)	Reconstruct instrument objects from a dataframe and level 0 metadata.
launch_operations_changing_df(data_processor)	Execute dataframe-modifying operations in dependency order.

Module Contents

helikite.classes.base.logger

helikite.classes.base.function_dependencies(required_operations: list[str | tuple[str, Ellipsis]], changes_df: bool, use_once: bool, complete_with_arg: Any | None = None, complete_req: bool = False)

A decorator to enforce that a method can only run if the required operations have been completed and not rerun.

class helikite.classes.base.BaseProcessor(output_schema: helikite.classes.output_schemas.OutputSchema, instruments: list[helikite.instruments.Instrument], reference_instrument: helikite.instruments.Instrument)

Bases: abc.ABC

Abstract base class for processing pipeline stages.

Provides shared state tracking, dependency management, and utility helpers used by all processing levels.

_output_schema

_instruments: list[helikite.instruments.Instrument]

_reference_instrument: helikite.instruments.Instrument

_completed_operations: list[str] = []

property level: helikite.classes.output_schemas.Level

Abstractmethod:

Processing level identifier.

property output_schema

Return the campaign output format specification.

property instruments

Return instruments present in the current flight

property reference_instrument

Return the instrument used as synchronization reference.

property df: pandas.DataFrame | None

Abstractmethod:

Return the current state of dataframe.

_check_schema_contains_instrument(instrument: helikite.instruments.Instrument) → bool

property _flight_computer: helikite.instruments.flight_computer.FlightComputer | None

abstractmethod _data_state_info() → list[str]

_operations_state_info() → list[str]

state()

Prints the current state of the class in a tabular format

help()

Prints available methods in a clean format

_outliers_file_state_info(outliers_file: str, add_all: bool = False) → list[str]

_print_success_errors(operation: str, success: list[str], errors: list[tuple[str, Any]]) → None

abstractmethod export_data(filepath: str | pathlib.Path | None = None)

Export dataframe in its final state.

helikite.classes.base.get_instruments_from_cleaned_data(df: pandas.DataFrame, metadata: pydantic.BaseModel, flight_computer_version: str | None) → tuple[list[helikite.instruments.Instrument], helikite.instruments.Instrument]

Reconstruct instrument objects from a dataframe and level 0 metadata.

Parameters:

• df – Level 0 dataframe.

• metadata – Metadata model.

• flight_computer_version – Optional flight computer version.

Returns:

Tuple of instruments list and reference instrument.

helikite.classes.base.launch_operations_changing_df(data_processor: BaseProcessor)

Execute dataframe-modifying operations in dependency order.

Parameters:

data_processor – Processor instance.

Returns:

Processor after execution.