API Reference

API Reference#

Functions for calculated trajectory or navigation related fields

`along_track_distance`(data, *[, ...])	Calculate the distances along track in meters.
`relative_distance`(data, *[, easting_column, ...])	Calculate distance between successive points in a dataframe.
`ground_speed`(data, *[, time_column, ...])	TODO: do calculation forward for 1st points so they aren't 0 Calculate the ground speed in meters per second.
`vertical_acceleration`(data, *, time_column, ...)	Calculate the 2nd derivative of height change with respect to time for each line.
`track`(data, *, latitude_column, longitude_column)	Calculate the track between each successive row in a dataframe.

Functions for performing geospatial operations.

`reproject`(x, y, input_crs, output_crs)	Convert coordinates from input CRS to output CRS.
`block_reduce`(data, reduction, *, spacing, ...)	Reduce data by line based on the column(s) provided by reduce_by and the reduction function.
`filter_line`(data, *, filter_type, ...[, ...])	Apply a 1D filter to a column of a pandas DataFrame along values of another column.
`filter_grid`(grid[, filter_width, ...])	Apply a spatial filter to a grid.
`sample_grid`(data, grid, coord_names)	Sample grid values at supplied points in dataframe.

Functions for working with and organizing survey data.

`split_into_segments`(data, threshold, column_name)	Split dataframe into segments where there is a gap in the supplied values greater than the threshold.
`unique_line_id`(df[, line_col_name])	Convert supplied lines names into integers.

Functions for automated and manual quality control of airborne geophysical survey data.

inspect_lines(df, *, plot_variable[, interp_on])

Functions for levelling airborne geophysical survey data.

`level_to_grid`(data, *, degree, data_column, ...)	Fit a trend to the misfit between the data_column and grid_column values and subtract the fitted trend from the data to level it to the grid values.
`create_intersection_table`(data, *[, ...])	create a dataframe which contains the intersections between provided flight and tie lines.
`interpolate_intersections`(df, intersections, ...)	_summary_
`inspect_intersections`(data, *, plot_variable)
`plot_line_and_crosses`(df, *, y[, line, x, ...])	plot lines and crosses
`lines_without_intersections`(data, intersections)
`calculate_crossover_errors`(data, ...[, ...])	Calculate mistie values for all intersections.
`update_intersections_with_eq_sources`(data, ...)	At each theoretical intersection point, replace the interpolated field value with a value predected by the fitted equivalent sources for the line, at the x,y coordinate of the intersection point, and the higher of the two lines' elevations.
`line_levelling`(data, inters, *, ...[, ...])	Level lines based on intersection misties values.
`iterative_line_levelling`(data, inters, *, ...)
`alternating_iterative_line_levelling`(data, ...)
`equivalent_source_levelling`(data, ...[, ...])	_summary_
`plot_levelling_convergence`(results, *[, ...])

`plotly_points`(df, *, color_col[, ...])	Create a scatterplot of spatial data.
`plotly_profiles`(data, *, y[, x, y_axes, ...])	plot data profiles with plotly currently only allows 3 separate y axes, set with "y_axes", starting with 1
`inspect_lines`(df, *, plot_variable[, interp_on])

`eq_sources_1d`(data, *, data_column, damping)	Fit a set of equivalent sources along 1 dimension.
`upward_continue_by_line`(data, ...[, ...])	For each light line in a dataframe, fit a set of equivalent sources and then upward continue to data to a specified height and return the upward continued data.
`eotvos_correction_glicken`(latitude, track, ...)	Compute the simple Eötvös correction from latitude, aircraft track and ground speed using the Glicken 1962 simplified equation.
`eotvos_correction_harlan`(latitude, ...[, ...])	Compute the Eötvös correction from latitude, longitude, time, and height from Harlan 1968.
`vertical_acceleration`(data, *, time_column, ...)	Calculate the 2nd derivative of height change with respect to time for each line.