ctdam.proc.modules.seabird_functions module

class ctdam.proc.modules.seabird_functions.LoopRemoval

Bases: Module

Flags pressure loops caused by ship heave.

transformation()

Calls the loop removal function and handles the resulting flag values for array truncation.

Return type:

bool

time_dependent_loop_removal(pressure, delta)

Flag samples where pressure does not increase strictly with time. Optionally leaves some room for minor fluctuations.

A sample is flagged when its pressure does not surpass the maximum pressure of every previous measurement (excluding possible minor fluctuations).

Note take time itself is not required as an arugment as each entry is taken at a discrete timestep i.e. relative time can be easily induced

Parameters

delta: float

Value that take minor fluctuations into account

Return type:

ndarray

jens_loop_removal(pressure, sample_interval, precut_period=5, cut_period=10, mean_speed_percent=20, delay=2, filter_order=4)

Flag loops in CTD data caused by ship heave. Credit: Dr. Jens Faber, IOW.

Parameters:

• pressure (ndarray) – Array of vertical axis values

• sample_interval (float) – The interval the data has been sampled with

• precut_period (int) – Cutoff period for the pressure (Default value = 5)

• cut_period (int) – Cutoff period for the main filter (Default value = 10)

• mean_speed_percent (int) – Percentage of filtered velocity to use as a threshold (Default value = 20)

• delay (int) – Delay (Default value = 2)

• filter_order (int) – Order of the Butterworth filter (Default value = 4)

class ctdam.proc.modules.seabird_functions.AlignCTD

Bases: Module

Align the given parameter columns.

transformation()

Return type:

bool

class ctdam.proc.modules.seabird_functions.WFilter

Bases: Module

Apply a signal processing filter to certain data columns.

transformation()

Calls window_filter method and handles argument display.

Return type:

bool

window_filter(data_in, window_type, window_width, sample_interval, half_width=1.0, offset=0.0, flag_value=-9.99e-29)

Filters a dataset by convolving it with an array of weights.

The available window filter types are boxcar, cosine, triangle, gaussian, and median. Refer to the SeaSoft data processing manual version 7.26.8, page 108.

Parameters:

• data_in (ndarray) – Data to be filtered.

• flags (np.ndarray) – Flagged data defined by loop edit.

• window_type (str) – The filter type (boxcar, cosine, triangle, gaussian, or median).

• window_width (int) – Width of the window filter (must be odd).

• sample_interval (float) – Sample interval of the dataset.

• half_width (float) – Width of the Gaussian curve. (Default value = 1.0)

• offset (float) – Shifts the center point of the Gaussian. (Default value = 0.0)

• exclude_flags (bool) – Exclude flagged values from the dataset. (Default value = False)

• flag_value (float) – The flag value in flags. (Default value = -9.99e-29)

Return type:

ndarray

class ctdam.proc.modules.seabird_functions.CellTM

Bases: Module

Fix cell thermal mass errors of the conductivity sensors.

transformation()

Call Sea-Birds cell-termal-mass function and convert unit.

Return type:

bool

class ctdam.proc.modules.seabird_functions.BinAvg

Bases: Module

Bin data points in pressure or time bins.

transformation()

Return type:

bool