src.toolbox.steps.custom.variables.salinity#

Classes#

AdjustSalinity

Functions#

running_average_nan(→ numpy.ndarray)

Estimate running average mean

compute_optimal_lag(profile_data, filter_window_size, ...)

Calculate the optimal conductivity time lag relative to temperature to reduce salinity spikes for each glider profile.

Module Contents#

src.toolbox.steps.custom.variables.salinity.running_average_nan(arr: numpy.ndarray, window_size: int) → numpy.ndarray[source]#

Estimate running average mean

src.toolbox.steps.custom.variables.salinity.compute_optimal_lag(profile_data, filter_window_size, time_col)[source]#

Calculate the optimal conductivity time lag relative to temperature to reduce salinity spikes for each glider profile.

class src.toolbox.steps.custom.variables.salinity.AdjustSalinity[source]#

Bases: toolbox.steps.base_step.BaseStep, toolbox.utils.qc_handling.QCHandlingMixin

step_name = 'Salinity Adjustment'[source]#
required_variables = ['TIME', 'PROFILE_NUMBER', 'CNDC', 'TEMP', 'PRES'][source]#
provided_variables = [][source]#
run()[source]#
correct_ct_lag()[source]#
correct_thermal_lag()[source]#

Applies thermal mass correction independently to the downcast, transect, and upcast.

generate_diagnostics()[source]#