airbornegeo.alternating_iterative_line_levelling

alternating_iterative_line_levelling(data, inters, *, data_col, levelled_col, line_column, distance_column, lines_to_level=None, degree=None, filter_kwargs=None, intersection_weight_col=None, crossover_error_interp_method='linear', max_iterations=5, rms_tolerance=None, rms_percent_change_tolerance=10, rms_percent_increase_tolerance=20, plot_convergence=True, plot_dynamic_convergence=False, progressbar=True)

Level a two-group survey by alternating between the groups: each iteration first levels the tie lines (line_type == 1) onto the flight lines (line_type == 0), then levels the flight lines onto the now-levelled ties.

Levelling the ties first follows standard practice (Saul and Pearson, 1998; Seequent’s Oasis montaj Iterative Levelling): the ties are the reference surface the flight lines are levelled onto, but each tie is flown over a long time span and carries its own drift, so it is corrected first against the many flight lines it crosses, where the flight lines’ own (still unlevelled) errors largely average out.

Parameters:

• data (DataFrame)

• inters (DataFrame)

• data_col (str)

• levelled_col (str)

• line_column (str)

• distance_column (str)

• lines_to_level (list[str] | None)

• degree (int | None)

• filter_kwargs (dict[str, Any] | None)

• intersection_weight_col (str | None)

• crossover_error_interp_method (str)

• max_iterations (int)

• rms_tolerance (float | None)

• rms_percent_change_tolerance (float)

• rms_percent_increase_tolerance (float)

• plot_convergence (bool)

• plot_dynamic_convergence (bool)

• progressbar (bool)

Return type:

tuple[DataFrame, DataFrame]