helikite.instruments.mcda_instrument

Mini Cloud Droplet Anlayzer (mCDA) mCDA -> mCDA_output_2025-02-12_A (has pressure)

Important variables to keep:

!!! Raspery Pie of the mCDA has an autonomous timestamp, make sur it has been corrected when creating the output file.

Attributes

`logger`
`MCDA_MIDPOINT_DIAMETER_LIST`
`mcda`

Classes

mCDA

Instrument definition for the mcda sensor system.

Module Contents

helikite.instruments.mcda_instrument.logger

helikite.instruments.mcda_instrument.MCDA_MIDPOINT_DIAMETER_LIST

class helikite.instruments.mcda_instrument.mCDA(*args, **kwargs)

Bases: helikite.instruments.base.Instrument

Instrument definition for the mcda sensor system. Handles timestamp creation and optional corrections.

__repr__()

property has_size_distribution: bool

file_identifier(first_lines_of_csv) → bool: Identify if the file matches the expected mCDA CSV header. Only checks the first few columns for matching names, ignoring the rest.

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

read_data() → pandas.DataFrame

Read data into dataframe

This allows a custom read function to parse the CSV/TXT into a dataframe, for example cleaning dirty data at the end of the file in memory without altering the input file (see flight computer conf).

data_corrections(df: pandas.DataFrame, **kwargs) → pandas.DataFrame: Apply any custom corrections here. For now, this is a pass-through.

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

calculate_derived(df: pandas.DataFrame, verbose: bool, *args, **kwargs) → pandas.DataFrame: Calculate derived data from the raw data, i.e. total concentration

normalize(df: pandas.DataFrame, reference_instrument: helikite.instruments.base.Instrument, verbose: bool, *args, **kwargs) → pandas.DataFrame

Normalize mCDA concentrations to STP conditions and insert the results right after the existing mCDA columns.

Parameters: df (pd.DataFrame): DataFrame containing mCDA measurements and metadata.

Returns: df (pd.DataFrame): Updated DataFrame with STP-normalized columns inserted.

plot_raw_and_normalized(df: pandas.DataFrame, verbose: bool, *args, **kwargs): Plots mCDA total concentration vs Altitude

plot_distribution(df: pandas.DataFrame, verbose: bool, time_start: datetime.datetime, time_end: datetime.datetime, subplot: tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes] | None = None)

Plot mCDA size distribution and total concentration.

Parameters: - df (pd.DataFrame): DataFrame containing mCDA size distribution and total concentration. - Midpoint_diameter_list (list or np.array): List of particle diameters corresponding to the diameter bin midpoints.

plot_vertical_distribution(df: pandas.DataFrame, verbose: bool, *args, **kwargs)

Plots the vertical distribution of mCDA particle size distribution versus altitude.

Parameters: - df: pandas DataFrame containing particle size distribution and altitude data. - midpoint_diameter_list: 1D NumPy array of midpoint diameters corresponding to bins.

helikite.instruments.mcda_instrument.mcda