UserInput.py takes in the controller panel from the GUI and stores the information selected by the user for this run of cBLUE.
UserInput.py stores the variables:
wind_selection : A string name describing the wind selection.
wind_vals : An array holding integer values representing the wind selection.
kd_selection: A string name describing the turbidity selection.
kd_vals: An array holding integer values representing the turbidity selection.
vdatum_region : A string name of the vdatum region.
mcu : A float value for the maximum cumulative error related to the vdatum region.
output_directory : The string file path of the TPU output directory.
cblue_version : The string name of the current cBLUE version.
multiprocess : A string holding "True" or "False", determines if cBLUE is run with multiprocessing.
cpu_process_info : A tuple holding ("multiprocess", num_cores) or ("singleprocess",), depending on if cBLUE is run with multiprocessing.
water_surface_ellipsoid_height : A float holding the water surface ellipsoid height. In meters, positive up.
error_type : A string holding the error type requested by the user.
csv_option : A boolean value, determines if there is a csv output file.
Pass gui_object into tpu = Tpu() [line 643] instead of individually passing variables through in cBlueApp.py.
Refactored Tpu.py to handle the gui_object instead of individual variables.
In Subaqeuou.py replaced pandas.DataFrame.values (not recommended) with pandas.DataFrame.to_numpy recommended [lines 70, 71, 79, and 80]
In Subaqueous.py averaged the TVU and THU observation equation coefficients in model_process() and return the averaged values instead of the unaveraged ones. Allows for equivalent but faster matrix multiplication in fit_lut().
UserInput.py takes in the controller panel from the GUI and stores the information selected by the user for this run of cBLUE.
UserInput.py stores the variables:
Pass gui_object into tpu = Tpu() [line 643] instead of individually passing variables through in cBlueApp.py.
Refactored Tpu.py to handle the gui_object instead of individual variables.
In Subaqeuou.py replaced pandas.DataFrame.values (not recommended) with pandas.DataFrame.to_numpy recommended [lines 70, 71, 79, and 80]
In Subaqueous.py averaged the TVU and THU observation equation coefficients in model_process() and return the averaged values instead of the unaveraged ones. Allows for equivalent but faster matrix multiplication in fit_lut().