helikite.instruments.co2
========================

.. py:module:: helikite.instruments.co2


Attributes
----------

.. autoapisummary::

   helikite.instruments.co2.logger
   helikite.instruments.co2.co2


Classes
-------

.. autoapisummary::

   helikite.instruments.co2.CO2


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

.. py:data:: logger

.. py:class:: CO2(*args, **kwargs)

   Bases: :py:obj:`helikite.instruments.Instrument`


   .. py:method:: __repr__()


   .. py:method:: file_identifier(first_lines_of_csv: list[str]) -> bool


   .. py:method:: data_corrections(df, *args, **kwargs) -> pandas.DataFrame


   .. py:method:: set_time_as_index(df: pandas.DataFrame) -> pandas.DataFrame


   .. py:method:: read_data() -> pandas.DataFrame


   .. py:method:: 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)



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


   .. py:method:: _describe(x: numpy.ndarray) -> dict[str, numbers.Number]
      :staticmethod:



   .. py:method:: _remove_outliers(df: pandas.DataFrame, column: str, min_threshold: numbers.Number, max_threshold: numbers.Number)
      :staticmethod:



.. py:data:: co2