helikite.classes.data_processing_level1_5
=========================================

.. py:module:: helikite.classes.data_processing_level1_5


Classes
-------

.. autoapisummary::

   helikite.classes.data_processing_level1_5.DataProcessorLevel1_5


Module Contents
---------------

.. py:class:: DataProcessorLevel1_5(output_schema: helikite.classes.output_schemas.OutputSchema, df: pandas.DataFrame, metadata: pydantic.BaseModel, flight_computer_version: str | None = None)

   Bases: :py:obj:`helikite.classes.base.BaseProcessor`


   Level 1.5 for detecting flags that indicate environmental or flight conditions, such as hovering,
   pollution exposure, or cloud immersion.


   .. py:property:: level
      :type: helikite.classes.output_schemas.Level


      Processing level identifier.


   .. py:attribute:: _df


   .. py:attribute:: _metadata


   .. py:attribute:: _automatic_flags_files
      :type:  set[str]


   .. py:attribute:: _final_flags_files
      :type:  set[str]


   .. py:method:: _data_state_info() -> list[str]


   .. py:property:: df
      :type: pandas.DataFrame


      Return the current state of dataframe.


   .. py:method:: fill_msems_takeoff_landing(time_window_seconds=90)

      Fill missing values in mSEMS columns at takeoff and landing times using nearby values.

      :param time_window_seconds: Number of seconds before/after to search for replacement values (default: 90).
      :type time_window_seconds: int, optional



   .. py:method:: remove_before_takeoff_and_after_landing()

      Trim dataframe outside flight time window.



   .. py:method:: drop_columns()

      Drop dataframe columns which are not required for the final file.



   .. py:method:: rename_columns()

      Renames columns of the input DataFrame according to predefined rules and instrument-specific rules.
      See `Instrument.rename_dict`



   .. py:method:: round_and_add_flightnbr_campaign(decimals=2)

      Round numeric columns of the DataFrame with special handling for 'WindDir',
      and add columns for flight number and campaign.

      :param decimals: The number of decimal places to round to (default is 2).
      :type decimals: int, optional



   .. py:method:: detect_flag(flag: helikite.classes.output_schemas.Flag = flag_pollution_cpc, auto_path: str | pathlib.Path = 'flag_auto.csv', plot_detection: bool = True)

      Automatically detect and record flag events.



   .. py:method:: choose_flag(flag: helikite.classes.output_schemas.Flag = flag_pollution_cpc, auto_path: str | pathlib.Path | None = None, corr_path: str | pathlib.Path = 'flag_corr.csv')

      Interactively review and correct flag detection.



   .. py:method:: set_flag(flag: helikite.classes.output_schemas.Flag = flag_pollution_cpc, corr_path: str | pathlib.Path = 'flag_corr.csv', mask: pandas.Series | None = None)

      Apply finalized flag to dataframe.



   .. py:method:: plot_flight_profiles(flight_basename: str, save_path: str | pathlib.Path, variables: list[helikite.classes.output_schemas.FlightProfileVariable] | None = None)

      Generate and save flight profile plots.



   .. py:method:: plot_size_distr(flight_basename: str, save_path: str | pathlib.Path, time_start: datetime.datetime | None = None, time_end: datetime.datetime | None = None)

      Generate and save particle size distribution plots combined in a single plot.



   .. py:method:: get_expected_columns(output_schema: helikite.classes.output_schemas.OutputSchema, reference_instrument: helikite.instruments.Instrument, with_dtype: bool) -> list[str] | dict[str, str]
      :classmethod:


      Generate expected dataframe columns at level 0.

      :param output_schema: Schema containing campaign instruments.
      :param with_dtype: Whether to include dtype mapping.

      :returns: List of column names or dict of column-to-dtype.



   .. py:method:: read_data(level1_5_filepath: str | pathlib.Path) -> pandas.DataFrame
      :staticmethod:


      Load Level 1.5 dataframe from CSV.



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

      Export dataframe in its final state.