helikite.processing.post.level1

Functions

create_level1_dataframe(df, output_schema)

rename_columns(df, output_schema, reference_instrument)

Renames columns of the input DataFrame according to predefined rules and instrument-specific rules.

_build_rename_dict(output_schema, reference_instrument)

round_flightnbr_campaign(df, metadata, output_schema, ...)

Round numeric columns of the DataFrame with special handling for 'Lat' and 'Long',

fill_msems_takeoff_landing(df, metadata, ...)

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

flight_profiles(df, reference_instrument, level, ...)

_get_series_bounds(→ tuple[tuple[numbers.Number, ...)

plot_size_distributions(df, level, output_schema, ...)

shade_flagged(shade_config, axes, df, level[, ...])

filter_data(df)

Module Contents

helikite.processing.post.level1.create_level1_dataframe(df: pandas.DataFrame, output_schema: helikite.classes.output_schemas.OutputSchema)
helikite.processing.post.level1.rename_columns(df: pandas.DataFrame, output_schema: helikite.classes.output_schemas.OutputSchema, reference_instrument: helikite.instruments.base.Instrument)

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

Parameters:

  • df (pd.DataFrame) – The DataFrame with columns to be renamed.

  • output_schema (OutputSchema) – The OutputSchema object containing the instruments.

  • reference_instrument (Instrument) – Reference instrument to take pressure values from.

Returns:

DataFrame with renamed columns.

Return type:

pd.DataFrame

helikite.processing.post.level1._build_rename_dict(output_schema: helikite.classes.output_schemas.OutputSchema, reference_instrument: helikite.instruments.base.Instrument)
helikite.processing.post.level1.round_flightnbr_campaign(df: pandas.DataFrame, metadata: pydantic.BaseModel, output_schema: helikite.classes.output_schemas.OutputSchema, decimals: int)

Round numeric columns of the DataFrame with special handling for ‘Lat’ and ‘Long’, and add columns for flight number and campaign.

Parameters:

  • df (pd.DataFrame) – The DataFrame to be rounded and modified.

  • metadata (object) – Metadata object containing the ‘flight’ attribute.

  • output_schema (OutputSchema) – The OutputSchema object containing the campaign name.

  • decimals (int, optional) – The number of decimal places to round to (default is 2).

Returns:

The rounded and modified DataFrame with additional columns.

Return type:

pd.DataFrame

helikite.processing.post.level1.fill_msems_takeoff_landing(df, metadata, time_window_seconds)

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

Parameters:

  • df (pd.DataFrame) – DataFrame with a DateTimeIndex where filling should occur.

  • metadata (object) – An object containing takeoff_time and landing_time attributes.

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

helikite.processing.post.level1.flight_profiles(df: pandas.DataFrame, reference_instrument: helikite.instruments.base.Instrument, level: helikite.classes.output_schemas.Level, output_schema: helikite.classes.output_schemas.OutputSchema, variables: list[helikite.classes.output_schemas.FlightProfileVariable] | None, fig_title=None)
helikite.processing.post.level1._get_series_bounds(x: pandas.Series, default_divider: numbers.Number, default_min: numbers.Number | None, default_max: numbers.Number | None) → tuple[tuple[numbers.Number, numbers.Number], numbers.Number]
helikite.processing.post.level1.plot_size_distributions(df: pandas.DataFrame, level: helikite.classes.output_schemas.Level, output_schema: helikite.classes.output_schemas.OutputSchema, fig_title: str, time_start: datetime.datetime | None, time_end: datetime.datetime | None, freq: str = '1s', min_loc_interval: int = 10)
helikite.processing.post.level1.shade_flagged(shade_config: helikite.classes.output_schemas.FlightProfileVariableShade, axes: list[matplotlib.pyplot.Axes], df: pandas.DataFrame, level: helikite.classes.output_schemas.Level, shade_coord: str = 'y', other_coord_name: str | None = None, timedelta: pandas.Timedelta = pd.Timedelta(seconds=1))
helikite.processing.post.level1.filter_data(df)