airbornegeo.level_to_grid

level_to_grid(data, *, data_column, grid_column, fit_by_column, degree=None, filter_kwargs=None, groupby_column=None)

Level flight lines based on the misfit between the data_column and grid_column values. The levelling correction is determined from the misfit values, by either fitting a trend with a specified order to the misfits, or by spatially or temporally filtering the misfits. This fitting or filtering use the data in fit_by_column as the independent variable. The levelling correction is the subtracted from the line data. The grid values can be sampled into the dataframe with ::func`sample_grid`. If groupby_column is provided, the trend will be fit a line-by-line basis in 1D using the column distance_along_line. If groupby_column is not provided, the trend will be fit to the entire survey in 2D using the cartesian coordinate columns provided by groupby_column.

Parameters:

• data (DataFrame) – dataframe with the data and grid values.

• data_column (str) – the column name for the data to fit.

• grid_column (str) – the column name with the sample grid values.

• fit_by_column (str | tuple[str]) – the column(s) to filter or fit a trend against, for example a column called ‘unixtime’ which contains sequentially second values, if filtering temporally, or a column called ‘distance_along_line’ with sequential distance values in meters, if filtering spatially, or if groupby_column is not used, then you must provide two column names to fit a trend / filter in 2D, for each ‘easting’ and ‘northing’.

• groupby_column (str | None) – Column name to group by, by default None

• degree (int | None) – the degree order to fit to the misfit values.

• filter_kwargs (dict[str, Any] | None) – Alternative to degree: keyword arguments forwarded to airbornegeo.filter_line to low-pass filter the misfit values (besides data_column and filter_by_column, which are set for you). Must include filter_width; anything else you don’t provide falls back to filter_line’s own defaults (e.g. filter_shape=”gaussian”, engine=”scipy”).

Returns:

The levelled data

Return type:

Series