helikite.instruments.co2

Attributes

`logger`
`co2`

Classes

CO2

Module Contents

helikite.instruments.co2.logger

class helikite.instruments.co2.CO2(*args, **kwargs)

Bases: helikite.instruments.Instrument

__repr__()

file_identifier(first_lines_of_csv: list[str]) → bool

data_corrections(df, *args, **kwargs) → pandas.DataFrame

set_time_as_index(df: pandas.DataFrame) → pandas.DataFrame

read_data() → pandas.DataFrame

normalize(df: pandas.DataFrame, reference_instrument: helikite.instruments.Instrument, verbose: bool, min_threshold: numbers.Number, max_threshold: numbers.Number) → pandas.DataFrame

Process CO2 data to convert to STP moist and dry values, apply calibration, and filter out unrealistic values. Only processes if mean CO2 is above threshold.

Parameters:

df: DataFrame with raw data (expects specific column names) min_threshold: Minimum mean CO2 value required to proceed

Returns:

df: Updated DataFrame with ‘co2_CO2_moist’ column (if processed)

plot_raw_and_normalized(df: pandas.DataFrame, verbose: bool = True)

static _describe(x: numpy.ndarray) → dict[str, numbers.Number]

static _remove_outliers(df: pandas.DataFrame, column: str, min_threshold: numbers.Number, max_threshold: numbers.Number)

helikite.instruments.co2.co2