helikite.instruments.smart_tether

  1. SmartTether -> LOG_20220929_A.csv (has pressure)

The SmartTether is a weather sonde. time res 2 seconds if lon lat recorded. 1 sec if not.

Important variables to keep: Time, Comment, P (mbar), T (deg C), RH (%), Wind (degrees), Wind (m/s), UTC Time, Latitude (deg), Longitude (deg)

!!! Date is not reported in the data, but only in the header (yes, it’s a pity) -> therefore, I wrote a function that to includes the date but it needs to change date if we pass midnight (not implemented yet).

Attributes

logger

smart_tether

Classes

SmartTether

Functions

wind_outlier_removal(df[, col, dir_col, threshold, ...])

Removes outliers from wind speed using a median filter and synchronously removes corresponding wind direction values.

Module Contents

helikite.instruments.smart_tether.logger
class helikite.instruments.smart_tether.SmartTether(*args, **kwargs)

Bases: helikite.instruments.base.Instrument

date_extractor(first_lines_of_csv) → datetime.datetime
file_identifier(first_lines_of_csv) → bool
set_time_as_index(df: pandas.DataFrame) → pandas.DataFrame

Set the DateTime as index of the dataframe and correct if needed

Using values in the time_offset variable, correct DateTime index

As the rows store only a time variable, a rollover at midnight is possible. This function checks for this and corrects the date if needed

data_corrections(df, *args, **kwargs)
read_data() → pandas.DataFrame
helikite.instruments.smart_tether.smart_tether
helikite.instruments.smart_tether.wind_outlier_removal(df, col='smart_tether_Wind (m/s)', dir_col='smart_tether_Wind (degrees)', threshold=0.35, window_size=10)

Removes outliers from wind speed using a median filter and synchronously removes corresponding wind direction values. Plots both original and filtered wind speed and direction vs altitude.

Parameters:

  • df (pd.DataFrame) – Input dataframe with wind speed and direction data.

  • col (str) – Wind speed column name.

  • dir_col (str) – Wind direction column name.

  • threshold (float) – Relative deviation threshold for outlier detection.

  • window_size (int) – Size of sliding window for median filtering.

Returns:

A filtered copy of the input DataFrame with outliers replaced by NaN.

Return type:

pd.DataFrame