List of tool function signatures
The following is a listing of tool function text signatures, including argument names and default values, for the purpose of serving as a quick reference. Each function is a member of the WbEnvironment class. The tool documentation contains this same information in a less abbreviated form.
def adaptive_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, threshold: float = 2.0) -> Raster: ...
def add_point_coordinates_to_table(self, input: Vector) -> Vector: ...
def aggregate_raster(self, raster: Raster, aggregation_factor: int = 2, aggregation_type: str = "mean") -> Raster: ...
def and_op(self, input1: Raster, input2: Raster) -> Raster: ...
def anova(self, input_raster: Raster, features_raster: Raster, output_html_file: str) -> None: ...
def ascii_to_las(self, input_ascii_files: List[str], pattern: str, epsg_code: int) -> None: ...
def aspect(self, dem: Raster, z_factor: float = 1.0) -> Raster: ...
def attribute_correlation(self, input: Vector, output_html_file: str) -> None: ...
def attribute_histogram(self, input: Vector, field_name: str, output_html_file: str) -> None: ...
def attribute_scattergram(self, input: Vector, field_name_x: str, field_name_y: str, output_html_file: str, add_trendline: bool = False) -> None: ...
def average_flowpath_slope(self, dem: Raster) -> Raster: ...
def average_normal_vector_angular_deviation(self, dem: Raster, filter_size: int = 11) -> Raster: ...
def average_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def average_upslope_flowpath_length(self, dem: Raster) -> Raster: ...
def balance_contrast_enhancement(self, image: Raster, band_mean: float = 100.0) -> Raster: ...
def basins(self, d8_pntr: Raster, esri_pntr: bool = False) -> Raster: ...
def bilateral_filter(self, raster: Raster, sigma_dist: float = 0.75, sigma_int: float = 1.0) -> Raster: ...
def block_maximum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def block_minimum(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def boundary_shape_complexity(self, raster: Raster) -> Raster: ...
def breach_depressions_least_cost(self, dem: Raster, max_cost: float = float('inf'), max_dist: int = 100, flat_increment: float = float('nan'), fill_deps: bool = False, minimize_dist: bool = False) -> Raster: ...
def breach_single_cell_pits(self, dem: Raster) -> Raster: ...
def buffer_raster(self, input: Raster, buffer_size: float, grid_cells_units: bool = False) -> Raster: ...
def burn_streams_at_roads(self, dem: Raster, streams: Vector, roads: Vector, road_width: float) -> Raster: ...
def centroid_raster(self, input: Raster) -> Tuple[Raster, str]: ...
def centroid_vector(self, input: Vector) -> Vector: ...
def change_vector_analysis(self, date1_rasters: List[Raster], date2_rasters: List[Raster]) -> Tuple[Raster, Raster, str]: ...
def circular_variance_of_aspect(self, dem: Raster, filter_size: int = 11) -> Raster: ...
def classify_buildings_in_lidar(self, in_lidar: Lidar, building_footprints: Vector) -> Lidar: ...
def classify_overlap_points(self, in_lidar: Lidar, resolution: float = 1.0, overlap_criterion: str = "max scan angle", filter: bool = False) -> Lidar: ...
def clean_vector(self, input: Vector) -> Vector: ...
def clip(self, input: Vector, clip_layer: Vector) -> Vector: ...
def clip_lidar_to_polygon(self, input: Lidar, polygons: Vector) -> Lidar: ...
def clip_raster_to_polygon(self, raster: Raster, polygons: Vector, maintain_dimensions: bool = False) -> Raster: ...
def closing(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def clump(self, raster: Raster, diag: bool = False, zero_background: bool = False) -> Raster: ...
def compactness_ratio(self, input: Vector) -> Vector: ...
def conservative_smoothing_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3) -> Raster: ...
def construct_vector_tin(self, input_points: Vector, field_name: str = "FID", use_z: bool = False, max_triangle_edge_length: float = float('inf')) -> Vector: ...
def contours_from_points(self, input: Vector, field_name: str = "", use_z_values: bool = False, max_triangle_edge_length: float = float('inf'), contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9) -> Vector: ...
def contours_from_raster(self, raster_surface: Raster, contour_interval: float = 10.0, base_contour: float = 0.0, smoothing_filter_size: int = 9, deflection_tolerance: float = 10.0) -> Vector: ...
def convert_nodata_to_zero(self, raster: Raster) -> Raster: ...
def corner_detection(self, raster: Raster) -> Raster: ...
def correct_vignetting(self, image: Raster, principal_point: Vector, focal_length: float = 304.8, image_width: float = 228.6, n_param: float = 4.0) -> Raster: ...
def cost_allocation(self, source: Raster, backlink: Raster) -> Raster: ...
def cost_distance(self, source: Raster, cost: Raster) -> Tuple[Raster, Raster]: ...
def cost_pathway(self, destination: Raster, backlink: Raster, zero_background: bool = False) -> Raster: ...
def count_if(self, input_rasters: List[Raster], comparison_value: float) -> Raster: ...
def create_colour_composite(self, red: Raster, green: Raster, blue: Raster, opacity: Raster = None, enhance: bool = False, treat_zeros_as_nodata: bool = False) -> Raster: ...
def create_plane(self, base_file: Raster, gradient: float, aspect: float, constant: float) -> Raster: ...
def crispness_index(self, raster: Raster, output_html_file: str) -> None: ...
def cross_tabulation(self, raster1: Raster, raster2: Raster, output_html_file: str) -> None: ...
def csv_points_to_vector(self, input_file: str, x_field_num: int = 0, y_field_num: int = 1, epsg: int = 0) -> Vector: ...
def cumulative_distribution(self, raster: Raster) -> Raster: ...
def d8_flow_accum(self, raster: Raster, out_type: str = "sca", log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False, esri_pntr: bool = False) -> Raster: ...
def d8_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...
def d8_pointer(self, dem: Raster, esri_pointer: bool = False) -> Raster: ...
def depth_in_sink(self, dem: Raster, zero_background: bool = False) -> Raster: ...
def deviation_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def diff_of_gaussians_filter(self, raster: Raster, sigma1: float = 2.0, sigma2: float = 4.0) -> Raster: ...
def difference(self, input: Vector, overlay: Vector) -> Vector: ...
def difference_from_mean_elevation(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def dinf_flow_accum(self, dem: Raster, out_type: str = "sca", convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False) -> Raster: ...
def dinf_mass_flux(self, dem: Raster, loading: Raster, efficiency: Raster, absorption: Raster) -> Raster: ...
def dinf_pointer(self, dem: Raster) -> Raster: ...
def direct_decorrelation_stretch(self, image: Raster, achromatic_factor: float = 0.5, clip_percent: float = 1.0) -> Raster: ...
def directional_relief(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...
def dissolve(self, input: Vector, dissolve_field: str = "", snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...
def distance_to_outlet(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def diversity_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def downslope_distance_to_stream(self, dem: Raster, streams: Raster, use_dinf: bool = False) -> Raster: ...
def downslope_flowpath_length(self, d8_pointer: Raster, watersheds: Raster, weights: Raster, esri_pntr: bool = False) -> Raster: ...
def downslope_index(self, dem: Raster, vertical_drop: float, output_type: str = "tangent") -> Raster: ...
def edge_contamination(self, dem: Raster, flow_type: str = "mfd", z_factor: float = -1.0) -> Raster: ...
def edge_density(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 5.0, z_factor: float = 1.0) -> Raster: ...
def edge_preserving_mean_filter(self, raster: Raster, filter_size: int = 11, threshold: float = 15.0) -> Raster: ...
def edge_proportion(self, raster: Raster) -> Tuple[Raster, str]: ...
def elev_relative_to_min_max(self, dem: Raster) -> Raster: ...
def elev_relative_to_watershed_min_max(self, dem: Raster, watersheds: Raster) -> Raster: ...
def elevation_above_pit(self, dem: Raster) -> Raster: ...
def elevation_above_stream(self, dem: Raster, streams: Raster) -> Raster: ...
def elevation_above_stream_euclidean(self, dem: Raster, streams: Raster) -> Raster: ...
def elevation_percentile(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...
def eliminate_coincident_points(self, input: Vector, tolerance_dist: float) -> Vector: ...
def elongation_ratio(self, input: Vector) -> Vector: ...
def embankment_mapping(self, dem: Raster, roads_vector: Vector, search_dist: float = 2.5, min_road_width: float = 6.0, typical_embankment_width: float = 30.0, typical_embankment_max_height: float = 2.0, embankment_max_width: float = 60.0, max_upwards_increment: float = 0.05, spillout_slope: float = 4.0, remove_embankments: bool = False) -> Tuple[Raster, Union[Raster, None]]: ...
def emboss_filter(self, raster: Raster, direction: str = "n", clip_amount: float = 0.0) -> Raster: ...
def erase(self, input: Vector, erase_layer: Vector) -> Vector: ...
def erase_polygon_from_lidar(self, input: Lidar, polygons: Vector) -> Lidar: ...
def erase_polygon_from_raster(self, raster: Raster, polygons: Vector) -> Raster: ...
def euclidean_allocation(self, input: Raster) -> Raster: ...
def euclidean_distance(self, input: Raster) -> Raster: ...
def export_table_to_csv(self, input: Vector, output_csv_file: str, headers: bool = True) -> None: ...
def exposure_towards_wind_flux(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf'), z_factor: float = 1.0) -> Raster: ...
def extend_vector_lines(self, input: Vector, distance: float, extend_direction: str = "both") -> Vector: ...
def extract_nodes(self, input: Vector) -> Vector: ...
def extract_raster_values_at_points(self, rasters: List[Raster], points: Vector) -> Tuple[Vector, str]: ...
def extract_streams(self, flow_accumulation: Raster, threshold: float = 0.0, zero_background: bool = False) -> Raster: ...
def extract_valleys(self, dem: Raster, variant: str = "lq", line_thin: bool = False, filter_size: int = 5) -> Raster: ...
def farthest_channel_head(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def fast_almost_gaussian_filter(self, raster: Raster, sigma: float = 1.8) -> Raster: ...
def fd8_flow_accum(self, dem: Raster, out_type: str = "sca", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...
def fd8_pointer(self, dem: Raster) -> Raster: ...
def feature_preserving_smoothing(self, dem: Raster, filter_size: int = 11, normal_diff_threshold: float = 8.0, iterations: int = 3, max_elevation_diff: float = float('inf'), z_factor: float = 1.0) -> Raster: ...
def fetch_analysis(self, dem: Raster, azimuth: float = 0.0, height_increment: float = 0.05) -> Raster: ...
def fill_burn(self, dem: Raster, streams: Vector) -> Raster: ...
def fill_depressions(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan'), max_depth: float = float('inf')) -> Raster: ...
def fill_depressions_planchon_and_darboux(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...
def fill_depressions_wang_and_liu(self, dem: Raster, fix_flats: bool = True, flat_increment: float = float('nan')) -> Raster: ...
def fill_missing_data(self, dem: Raster, filter_size: int = 11, weight: float = 2.0, exclude_edge_nodata: bool = False) -> Raster: ...
def fill_pits(self, dem: Raster) -> Raster: ...
def filter_lidar_classes(self, input: Lidar, exclusion_classes: List[int]) -> Lidar: ...
def filter_lidar_scan_angles(self, in_lidar: Lidar, threshold: int) -> Lidar: ...
def filter_raster_features_by_area(self, input: Raster, threshold: int, zero_background: bool = False) -> Raster: ...
def find_flightline_edge_points(self, in_lidar: Lidar) -> Lidar: ...
def find_lowest_or_highest_points(self, raster: Raster, output_type: str = "lowest") -> Vector: ...
def find_main_stem(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def find_noflow_cells(self, dem: Raster) -> Raster: ...
def find_parallel_flow(self, d8_pntr: Raster, streams: Raster) -> Raster: ...
def find_patch_edge_cells(self, raster: Raster) -> Raster: ...
def find_ridges(self, dem: Raster, line_thin: bool = True) -> Raster: ...
def flatten_lakes(self, dem: Raster, lakes: Vector) -> Raster: ...
def flightline_overlap(self, input_lidar: Lidar, resolution: float = 1.0) -> Raster: ...
def flip_image(self, raster: Raster, direction: str = "v") -> Raster: ...
def flood_order(self, dem: Raster) -> Raster: ...
def flow_accum_full_workflow(self, dem: Raster, out_type: str = "sca", log_transform: bool = False, clip: bool = False, esri_pntr: bool = False) -> Tuple[Raster, Raster, Raster]: ...
def flow_length_diff(self, d8_pointer: Raster, esri_pointer: bool = False, log_transform: bool = False) -> Raster: ...
def gamma_correction(self, raster: Raster, gamma_value: float = 0.5) -> Raster: ...
def gaussian_contrast_stretch(self, raster: Raster, num_tones: int = 256) -> Raster: ...
def gaussian_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def gaussian_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...
def geomorphons(self, dem: Raster, search_distance: int = 1, flatness_threshold: float = 1.0, flatness_distance: int = 0, skip_distance: int = 0, output_forms: bool = True, analyze_residuals: bool = False) -> Raster: ...
def hack_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def heat_map(self, points: Vector, field_name: str, bandwidth: float = 0.0, cell_size: float = 0.0, base_raster: Raster = None, kernel_function: str = "quartic") -> Raster: ...
def height_above_ground(self, input: Lidar) -> Lidar: ...
def hexagonal_grid_from_raster_base(self, base: Raster, width: float, orientation: str = "h") -> Vector: ...
def hexagonal_grid_from_vector_base(self, base: Vector, width: float, orientation: str = "h") -> Vector: ...
def high_pass_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def high_pass_median_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...
def highest_position(self, input_rasters: List[Raster]) -> Raster: ...
def hillshade(self, dem: Raster, azimuth: float = 315.0, altitude: float = 30.0, z_factor: float = 1.0) -> Raster: ...
def hillslopes(self, d8_pntr: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...
def histogram_equalization(self, raster: Raster, num_tones: int = 256) -> Raster: ...
def histogram_matching(self, image: Raster, histogram: List[List[float]], histo_is_cumulative: bool = False) -> Raster: ...
def histogram_matching_two_images(self, image1: Raster, image2: Raster) -> Raster: ...
def hole_proportion(self, input: Vector) -> Vector: ...
def horizon_angle(self, dem: Raster, azimuth: float = 0.0, max_dist: float = float('inf')) -> Raster: ...
def horton_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def hypsometric_analysis(self, dem_rasters: List[Raster], output_html_file: str, watershed_rasters: List[Raster] = None) -> None: ...
def hypsometrically_tinted_hillshade(self, dem: Raster, solar_altitude: float = 45.0, hillshade_weight: float = 0.5, brightness: float = 0.5, atmospheric_effects: float = 0.0, palette: str = "atlas", reverse_palette: bool = False, full_360_mode: bool = False, z_factor: float = 1.0) -> Raster: ...
def idw_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, weight: float = 2.0, radius: float = 0.0, min_points: int = 0, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def ihs_to_rgb(self, intensity: Raster, hue: Raster, saturation: Raster) -> Tuple[Raster, Raster, Raster]: ...
def image_autocorrelation(self, rasters: List[Raster], output_html_file: str, contiguity_type: str = "bishop") -> None: ...
def image_correlation(self, rasters: List[Raster], output_html_file: str) -> None: ...
def image_correlation_neighbourhood_analysis(self, raster1: Raster, raster2: Raster, filter_size: int = 11, correlation_stat: str = "pearson") -> Tuple[Raster, Raster]: ...
def image_regression(self, independent_variable: Raster, dependent_variable: Raster, output_html_file: str, standardize_residuals: bool = False, output_scattergram: bool = False, num_samples: int = 1000) -> Raster: ...
def image_stack_profile(self, images: List[Raster], points: Vector, output_html_file: str) -> None: ...
def impoundment_size_index(self, dem: Raster, max_dam_length: float, output_mean: bool = False, output_max: bool = False, output_volume: bool = False, output_area: bool = False, output_height: bool = False) -> Tuple[Union[Raster, None], Union[Raster, None], Union[Raster, None], Union[Raster, None], Union[Raster, None]]: ...
def insert_dams(self, dem: Raster, dam_points: Vector, dam_length: float) -> Raster: ...
def integral_image_transform(self, raster: Raster) -> Raster: ...
def intersect(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...
def isobasins(self, dem: Raster, target_size: float, connections: bool = False, csv_file: str = "" ) -> Raster: ...
def jenson_snap_pour_points(self, pour_pts: Vector, streams: Raster, snap_dist: float = 0.0) -> Vector: ...
def join_tables(self, primary_vector: Vector, primary_key_field: str, foreign_vector: Vector, foreign_key_field: str, import_field: str = "") -> None: ...
def k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_clusters: int = 5, max_iterations: int = 10, percent_changed_threshold: float = 2.0, initialization_mode: str = "dia", min_class_size: int = 10) -> Raster: ...
def k_nearest_mean_filter(self, raster: Raster, filter_size_x: int = 3, filter_size_y: int = 3, k: int = 5) -> Raster: ...
def kappa_index(self, class_raster: Raster, reference_raster: Raster, output_html_file: str = "") -> None: ...
def ks_normality_test(self, raster: Raster, output_html_file: str, num_samples: int) -> None: ...
def laplacian_filter(self, raster: Raster, variant: str = "3x3(1)", clip_amount: float = 0.0) -> Raster: ...
def laplacian_of_gaussians_filter(self, raster: Raster, sigma: float = 0.75) -> Raster: ...
def las_to_ascii(self, input_lidar: Lidar) -> None: ...
def las_to_shapefile(self, input_lidar: Lidar, output_multipoint: bool = False) -> Vector: ...
def layer_footprint_raster(self, input: Raster) -> Vector: ...
def layer_footprint_vector(self, input: Vector) -> Vector: ...
def lee_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sigma: float = 10.0, m_value: float = 5.0) -> Raster: ...
def length_of_upstream_channels(self, d8_pointer: Raster, streams_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def lidar_block_maximum(self, input_lidar: Lidar, cell_size: float = 1.0) -> Raster: ...
def lidar_block_minimum(self, input_lidar: Lidar, cell_size: float = 1.0) -> Raster: ...
def lidar_classify_subset(self, base_lidar: Lidar, subset_lidar: Lidar, subset_class_value: int, nonsubset_class_value: int) -> Lidar: ...
def lidar_colourize(self, in_lidar: Lidar, in_image: Raster) -> Lidar: ...
def lidar_construct_vector_tin(self, input_lidar: Lidar, returns_included: str = "all", excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Vector: ...
def lidar_digital_surface_model(self, input_lidar: Lidar, cell_size: float = 1.0, search_radius: float = 0.5, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...
def lidar_elevation_slice(self, input: Lidar, minz: float = float('-inf'), maxz: float = float('inf'), classify: bool = False, in_class_value: int = 2, out_class_value: int = 1) -> Lidar: ...
def lidar_ground_point_filter(self, input_lidar: Lidar, search_radius: float = 2.0, min_neighbours: int = 0, slope_threshold: float = 45.0, height_threshold: float = 1.0, classify: bool = False, slope_norm: bool = True, height_above_ground: bool = False) -> Lidar: ...
def lidar_hex_bin(self, input_lidar: Lidar, width: float, orientation: str = "h") -> Vector: ...
def lidar_hillshade(self, input: Lidar, search_radius: float = -1.0, azimuth: float = 315.0, altitude: float = 30.0) -> Lidar: ...
def lidar_histogram(self, input_lidar: Lidar, output_html_file: str, parameter: str = "elevation", clip_percent: float = 1.0) -> None: ...
def lidar_idw_interpolation(self, input_lidar: Lidar, interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, idw_weight: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...
def lidar_info(self, input_lidar: Lidar, output_html_file: str, show_point_density: bool = True, show_vlrs: bool = True, show_geokeys: bool = True) -> None: ...
def lidar_join(self, inputs: List[Lidar]) -> Lidar: ...
def lidar_kappa(self, input_lidar1: Lidar, input_lidar2: Lidar, output_html_file: str, cell_size: float = 1.0, output_class_accuracy: bool = False) -> Raster: ...
def lidar_nearest_neighbour_gridding(self, input_lidar: Lidar, interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...
def lidar_point_density(self, input_lidar: Lidar, returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...
def lidar_point_stats(self, input_lidar: Lidar, cell_size: float = 1.0, num_points: bool = False, num_pulses: bool = False, avg_points_per_pulse: bool = False, z_range: bool = False, intensity_range: bool = False, predominant_class: bool = False) : ...
def lidar_radial_basis_function_interpolation(self, input_lidar: Lidar, interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, num_points: int = 15, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), func_type: str = "thinplatespline", poly_order: str = "none", weight: float = 0.1) -> Raster: ...
def lidar_ransac_planes(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, classify: bool = False, only_last_returns: bool = False) -> Lidar: ...
def lidar_remove_outliers(self, input: Lidar, search_radius: float = 2.0, elev_diff: float = 50.0, use_median: bool = False, classify: bool = False) -> Lidar: ...
def lidar_rooftop_analysis(self, lidar_inputs: List[Lidar], building_footprints: Vector, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, azimuth: float = 180.0, altitude: float = 30.0) -> Vector: ...
def lidar_segmentation(self, in_lidar: Lidar, search_radius: float = 2.0, num_iterations: int = 50, num_samples: int = 10, inlier_threshold: float = 0.15, acceptable_model_size: int = 30, max_planar_slope: float = 75.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classes: bool = False, ground: bool = False) -> Lidar: ...
def lidar_segmentation_based_filter(self, in_lidar: Lidar, search_radius: float = 5.0, norm_diff_threshold: float = 2.0, max_z_diff: float = 1.0, classify_points: bool = False) -> Lidar: ...
def lidar_shift(self, input: Lidar, x_shift: float = 0.0, y_shift: float = 0.0, z_shift: float = 0.0) -> Lidar: ...
def lidar_thin(self, input: Lidar, resolution: float = 1.0, selection_method: str = "first", save_filtered: bool = False) -> Tuple[Lidar, Union[Lidar, None]]: ...
def lidar_thin_high_density(self, input: Lidar, density: float, resolution: float = 1.0, save_filtered: bool = False) -> Tuple[Lidar, Union[Lidar, None]]: ...
def lidar_tile(self, input_lidar: Lidar, tile_width: float = 1000.0, tile_height: float = 1000.0, origin_x: float = 0.0, origin_y: float = 0.0, min_points_in_tile: int = 2, output_laz_format: bool = True) -> None: ...
def lidar_tile_footprint(self, input_lidar: Lidar, output_hulls: bool = False) -> Vector: ...
def lidar_tin_gridding(self, input_lidar: Lidar, interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf'), max_triangle_edge_length: float = float('inf')) -> Raster: ...
def lidar_tophat_transform(self, input: Lidar, search_radius: float) -> Lidar: ...
def line_detection_filter(self, raster: Raster, variant: str = "v", abs_values: bool = False, clip_tails: float = 0.0) -> Raster: ...
def line_intersections(self, input1: Vector, input2: Vector) -> Vector: ...
def line_thinning(self, raster: Raster) -> Raster: ...
def linearity_index(self, input: Vector) -> Vector: ...
def lines_to_polygons(self, input: Vector) -> Vector: ...
def list_unique_values(self, input: Vector, field_name: str) -> Tuple[str, int]: ...
def long_profile(self, d8_pointer: Raster, streams_raster: Raster, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...
def long_profile_from_points(self, d8_pointer: Raster, points: Vector, dem: Raster, output_html_file: str, esri_pointer: bool = False) -> None: ...
def longest_flowpath(self, dem: Raster, basins: Raster) -> Vector: ...
def lowest_position(self, input_rasters: List[Raster]) -> Raster: ...
def majority_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def map_off_terrain_objects(self, dem: Raster, max_slope: float = float('inf'), min_feature_size: int = 0) -> Raster: ...
def max_absolute_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def max_anisotropy_dev(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...
def max_anisotropy_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...
def max_branch_length(self, dem: Raster, log_transform: bool = False) -> Raster: ...
def max_difference_from_mean(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...
def max_downslope_elev_change(self, raster: Raster) -> Raster: ...
def max_elevation_dev_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...
def max_elevation_deviation(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...
def max_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def max_upslope_elev_change(self, raster: Raster) -> Raster: ...
def max_upslope_flowpath_length(self, dem: Raster) -> Raster: ...
def max_upslope_value(self, dem: Raster, values_raster: Raster) -> Raster: ...
def maximal_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def maximum_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def mdinf_flow_accum(self, dem: Raster, out_type: str = "sca", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...
def mean_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def mean_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def median_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...
def medoid(self, input: Vector) -> Vector: ...
def merge_line_segments(self, input: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...
def merge_table_with_csv(self, primary_vector: Vector, primary_key_field: str, foreign_csv_filename: str, foreign_key_field: str, import_field: str = "") -> None: ...
def merge_vectors(self, input_vectors: List[Vector]) -> Vector: ...
def min_absolute_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def min_downslope_elev_change(self, raster: Raster) -> Raster: ...
def min_max_contrast_stretch(self, raster: Raster, min_val: float, max_val: float, num_tones: int = 256) -> Raster: ...
def min_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def minimal_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def minimum_bounding_box(self, input: Vector, min_criteria: str = "area", individual_feature_hulls: bool = True) -> Vector: ...
def minimum_bounding_circle(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...
def minimum_bounding_envelope(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...
def minimum_convex_hull(self, input: Vector, individual_feature_hulls: bool = True) -> Vector: ...
def minimum_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def modified_k_means_clustering(self, input_rasters: List[Raster], output_html_file: str = "", num_start_clusters: int = 1000, merge_distance: float = 1.0, max_iterations: int = 10, percent_changed_threshold: float = 2.0) -> Raster: ...
def modify_nodata_value(self, input: Raster, new_value: float = -32768.0) : ...
def mosaic(self, images: List[Raster], resampling_method: str = "cc") -> Raster: ...
def mosaic_with_feathering(self, image1: Raster, image2: Raster, resampling_method: str = "cc", distance_weight: float = 4.0) -> Raster: ...
def multidirectional_hillshade(self, dem: Raster, altitude: float = 30.0, z_factor: float = 1.0, full_360_mode: bool = False) -> Raster: ...
def multipart_to_singlepart(self, input: Vector, exclude_holes: bool = False) -> Vector: ...
def multiply_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def multiscale_elevation_percentile(self, dem: Raster, num_significant_digits: int = 3, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0) -> Tuple[Raster, Raster]: ...
def multiscale_roughness(self, dem: Raster, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> Tuple[Raster, Raster]: ...
def multiscale_roughness_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 1, max_scale: int = 100, step_size: int = 1) -> None: ...
def multiscale_std_dev_normals(self, dem: Raster, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0, html_signature_file: str = "") -> Tuple[Raster, Raster]: ...
def multiscale_std_dev_normals_signature(self, dem: Raster, points: Vector, output_html_file: str, min_scale: int = 4, step_size: int = 1, num_steps: int = 10, step_nonlinearity: float = 1.0) -> None: ...
def multiscale_topographic_position_image(self, local: Raster, meso: Raster, broad: Raster, lightness: float = 1.2) -> Raster: ...
def narrowness_index(self, raster: Raster) -> Raster: ...
def natural_neighbour_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None, clip_to_hull: bool = True) -> Raster: ...
def nearest_neighbour_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None, max_dist: float = float('inf')) -> Raster: ...
def new_raster_from_base_raster(self, base: Raster, out_val: float = float('nan'), data_type: str = "float") -> Raster: ...
def new_raster_from_base_vector(self, base: Vector, cell_size: float, out_val: float = float('nan'), data_type: str = "float") -> Raster: ...
def normal_vectors(self, input: Lidar, search_radius: float = -1.0) -> Lidar: ...
def normalized_difference_index(self, nir_image: Raster, red_image: Raster, clip_percent: float = 0.0, correction_value: float = 0.0) -> Raster: ...
def not_op(self, input1: Raster, input2: Raster) -> Raster: ...
def num_downslope_neighbours(self, dem: Raster) -> Raster: ...
def num_inflowing_neighbours(self, dem: Raster) -> Raster: ...
def olympic_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def opening(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def or_op(self, input1: Raster, input2: Raster) -> Raster: ...
def paired_sample_t_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...
def panchromatic_sharpening(self, pan: Raster, colour_composite: Raster, red: Raster, green: Raster, blue: Raster, fusion_method: str = "brovey") -> Raster: ...
def patch_orientation(self, input: Vector) -> Vector: ...
def pennock_landform_classification(self, dem: Raster, slope_threshold: float = 3.0, prof_curv_threshold: float = 0.1, plan_curv_threshold: float = 0.0, z_factor: float = 1.0) -> Tuple[Raster, str]: ...
def percent_elev_range(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def percent_equal_to(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...
def percent_greater_than(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...
def percent_less_than(self, input_rasters: List[Raster], comparison: Raster) -> Raster: ...
def percentage_contrast_stretch(self, raster: Raster, clip: float = 1.0, tail: str = "both", num_tones: int = 256) -> Raster: ...
def percentile_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, sig_digits: int = 2) -> Raster: ...
def perimeter_area_ratio(self, input: Vector) -> Vector: ...
def pick_from_list(self, input_rasters: List[Raster], pos_input: Raster) -> Raster: ...
def plan_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def polygon_area(self, input: Vector) -> Vector: ...
def polygon_long_axis(self, input: Vector) -> Vector: ...
def polygon_perimeter(self, input: Vector) -> Vector: ...
def polygon_short_axis(self, input: Vector) -> Vector: ...
def polygonize(self, input_layers: List[Vector]) -> Vector: ...
def polygons_to_lines(self, input: Vector) -> Vector: ...
def prewitt_filter(self, raster: Raster, clip_tails: float = 0.0) -> Raster: ...
def principal_component_analysis(self, rasters: List[Raster], output_html_file: str, num_components: int = 2, standardized: bool = False) -> List[Raster]: ...
def print_geotiff_tags(self, file_name: str) : ...
def profile(self, lines_vector: Vector, surface: Raster, output_html_file: str) -> None: ...
def profile_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def qin_flow_accumulation(self, dem: Raster, out_type: str = "sca", exponent: float = 10.0, max_slope: float = 45.0, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...
def quantiles(self, raster: Raster, num_quantiles: int = 5) -> Raster: ...
def quinn_flow_accumulation(self, dem: Raster, out_type: str = "sca", exponent: float = 1.1, convergence_threshold: float = float('inf'), log_transform: bool = False, clip: bool = False) -> Raster: ...
def radial_basis_function_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, radius: float = 0.0, min_points: int = 0, cell_size: float = 0.0, base_raster: Raster = None, func_type: str = "thinplatespline", poly_order: str = "none", weight: float = 0.1) -> Raster: ...
def radius_of_gyration(self, raster: Raster) -> Tuple[Raster, str]: ...
def raise_walls(self, dem: Raster, walls: Vector, breach_lines: Vector, wall_height: float = 100.0) -> Raster: ...
def random_field(self, base_raster: Raster = None) -> Raster: ...
def random_sample(self, base_raster: Raster = None, num_samples: int = 1000) -> Raster: ...
def range_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def raster_area(self, raster: Raster, units: str = "map units", zero_background: bool = False) -> Tuple[Raster, str]: ...
def raster_calculator(self, expression: str, input_rasters: List[Raster]) -> Raster: ...
def raster_cell_assignment(self, raster: Raster, what_to_assign: str = "column") -> Raster: ...
def raster_histogram(self, raster: Raster, output_html_file: str) -> None: ...
def raster_perimeter(self, raster: Raster, units: str = "map units", zero_background: bool = False) -> Tuple[Raster, str]: ...
def raster_streams_to_vector(self, streams: Raster, d8_pointer: Raster, esri_pointer: bool = False) -> Vector: ...
def raster_summary_stats(self, input: Raster) -> str: ...
def raster_to_vector_lines(self, raster: Raster) -> Vector: ...
def raster_to_vector_points(self, raster: Raster) -> Vector: ...
def raster_to_vector_polygons(self, raster: Raster) -> Vector: ...
def rasterize_streams(self, streams: Vector, base_raster: Raster = None, zero_background: bool = False, use_feature_id: bool = False) -> Raster: ...
def reciprocal(self, raster: Raster) -> Raster: ...
def reclass(self, raster: Raster, reclass_values: List[List[float]], assign_mode: bool = False) -> Raster: ...
def reclass_equal_interval(self, raster: Raster, interval_size: float, start_value: float = float('-inf'), end_value: float = float('inf')) -> Raster: ...
def rectangular_grid_from_raster_base(self, base: Raster, width: float, height: float, x_origin: float = 0.0, y_origin: float = 0.0) -> Vector: ...
def rectangular_grid_from_vector_base(self, base: Vector, width: float, height: float, x_origin: float = 0.0, y_origin: float = 0.0) -> Vector: ...
def reinitialize_attribute_table(self, input: Vector) -> None: ...
def related_circumscribing_circle(self, input: Vector) -> Vector: ...
def relative_aspect(self, dem: Raster, azimuth: float = 0.0, z_factor: float = 1.0) -> Raster: ...
def relative_stream_power_index(self, specific_catchment_area: Raster, slope: Raster, exponent: float = 1.0) -> Raster: ...
def relative_topographic_position(self, dem: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def remove_duplicates(self, input: Lidar, include_z: bool = False) -> Lidar: ...
def remove_off_terrain_objects(self, dem: Raster, filter_size: int = 11, slope_threshold: float = 15.0) -> Raster: ...
def remove_polygon_holes(self, input: Vector) -> Vector: ...
def remove_short_streams(self, d8_pntr: Raster, streams_raster: Raster, min_length: float = 0.0, esri_pntr: bool = False) -> Raster: ...
def remove_spurs(self, raster: Raster, max_iterations: int = 10) -> Raster: ...
def resample(self, input_rasters: List[Raster], cell_size: float = 0.0, base_raster: Raster = None, method: str = "cc") -> Raster: ...
def rescale_value_range(self, raster: Raster, out_min_val: float, out_max_val: float, clip_min: float = float('inf'), clip_max: float = float('-inf')) -> Raster: ...
def rgb_to_ihs(self, red: Raster, green: Raster, blue: Raster, composite: Raster) -> Tuple[Raster, Raster, Raster]: ...
def rho8_flow_accum(self, raster: Raster, out_type: str = "sca", log_transform: bool = False, clip: bool = False, input_is_pointer: bool = False, esri_pntr: bool = False) -> Raster: ...
def rho8_pointer(self, dem: Raster, esri_pntr: bool = False) -> Raster: ...
def roberts_cross_filter(self, raster: Raster, clip_amount: float = 0.0) -> Raster: ...
def root_mean_square_error(self, input: Raster, reference: Raster) -> str: ...
def ruggedness_index(self, input: Raster) -> Raster: ...
def scharr_filter(self, raster: Raster, clip_tails: float = 0.0) -> Raster: ...
def sediment_transport_index(self, specific_catchment_area: Raster, slope: Raster, sca_exponent: float = 0.4, slope_exponent: float = 1.3) -> Raster: ...
def select_tiles_by_polygon(self, input_directory: str, output_directory: str, polygons: Vector) -> None: ...
def set_nodata_value(self, raster: Raster, back_value: float = 0.0) -> Raster: ...
def shape_complexity_index_raster(self, raster: Raster) -> Raster: ...
def shape_complexity_index_vector(self, input: Vector) -> Vector: ...
def shreve_stream_magnitude(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def sigmoidal_contrast_stretch(self, raster: Raster, cutoff: float = 0.0, gain: float = 1.0, num_tones: int = 256) -> Raster: ...
def singlepart_to_multipart(self, input: Vector, field_name: str) -> Vector: ...
def sink(self, dem: Raster, zero_background: bool = False) -> Raster: ...
def slope(self, dem: Raster, units: str = "degrees", z_factor: float = 1.0) -> Raster: ...
def slope_vs_elev_plot(self, dem_rasters: List[Raster], output_html_file: str, watershed_rasters: List[Raster]) -> None: ...
def smooth_vectors(self, input: Vector, filter_size: int = 3) -> Vector: ...
def snap_pour_points(self, pour_pts: Vector, flow_accum: Raster, snap_dist: float = 0.0) -> Vector: ...
def sobel_filter(self, raster: Raster, variant: str = "3x3", clip_tails: float = 0.0) -> Raster: ...
def spherical_std_dev_of_normals(self, dem: Raster, filter_size: int = 11) -> Raster: ...
def split_colour_composite(self, composite_image: Raster) -> Tuple[Raster, Raster, Raster]: ...
def split_vector_lines(self, input: Vector, segment_length: float) -> Vector: ...
def split_with_lines(self, input: Vector, split_vector: Vector) -> Vector: ...
def standard_deviation_contrast_stretch(self, raster: Raster, clip: float = 2.0, num_tones: int = 256) -> Raster: ...
def standard_deviation_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def standard_deviation_of_slope(self, dem: Raster, filter_size: int = 11, z_factor: float = 1.0) -> Raster: ...
def stochastic_depression_analysis(self, dem: Raster, rmse: float, range: float, iterations: int = 100) -> Raster: ...
def strahler_order_basins(self, d8_pointer: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...
def strahler_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def stream_link_class(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def stream_link_identifier(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def stream_link_length(self, d8_pointer: Raster, streams_id_raster: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def stream_link_slope(self, d8_pointer: Raster, streams_id_raster: Raster, dem: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def stream_slope_continuous(self, d8_pointer: Raster, streams_raster: Raster, dem: Raster, esri_pointer: bool = False, zero_background: bool = False) -> Raster: ...
def subbasins(self, d8_pntr: Raster, streams: Raster, esri_pntr: bool = False) -> Raster: ...
def sum_overlay(self, input_rasters: List[Raster]) -> Raster: ...
def surface_area_ratio(self, dem: Raster) -> Raster: ...
def symmetrical_difference(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...
def tangential_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def thicken_raster_line(self, raster: Raster) -> Raster: ...
def time_in_daylight(self, dem: Raster, az_fraction: float = 5.0, max_dist: float = float('inf'), latitude: float = 0.0, longitude: float = 0.0, utc_offset_str: str = "UTC+00:00", start_day: int = 1, end_day: int = 365, start_time: str = "sunrise", end_time: str = "sunset") -> Raster: ...
def tin_interpolation(self, points: Vector, field_name: str = "FID", use_z: bool = False, cell_size: float = 0.0, base_raster: Raster = None, max_triangle_edge_length: float = float('inf')) -> Raster: ...
def tophat_transform(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11, variant: str = "white") -> Raster: ...
def topological_stream_order(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def total_curvature(self, dem: Raster, log_transform: bool = False, z_factor: float = 1.0) -> Raster: ...
def total_filter(self, raster: Raster, filter_size_x: int = 11, filter_size_y: int = 11) -> Raster: ...
def trace_downslope_flowpaths(self, seed_points: Vector, d8_pointer: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def travelling_salesman_problem(self, input: Vector, duration: int = 60) -> Vector: ...
def trend_surface(self, raster: Raster, output_html_file: str, polynomial_order: int = 1) -> Raster: ...
def trend_surface_vector_points(self, input: Vector, cell_size: float, output_html_file: str, field_name: str = "FID", polynomial_order: int = 1) -> Raster: ...
def tributary_identifier(self, d8_pntr: Raster, streams_raster: Raster, esri_pntr: bool = False, zero_background: bool = False) -> Raster: ...
def turning_bands_simulation(self, base_raster: Raster = None, range: float = 1.0, iterations: int = 1000) -> Raster: ...
def two_sample_ks_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...
def union(self, input: Vector, overlay: Vector, snap_tolerance: float = 2.220446049250313e-16) -> Vector: ...
def unnest_basins(self, d8_pointer: Raster, pour_points: Vector, esri_pntr: bool = False) -> List[Raster]: ...
def unsharp_masking(self, raster: Raster, sigma: float = 0.75, amount: float = 100.0, threshold: float = 0.0) -> Raster: ...
def update_nodata_cells(self, input1: Raster, input2: Raster) -> Raster: ...
def upslope_depression_storage(self, dem: Raster) -> Raster: ...
def user_defined_weights_filter(self, raster: Raster, weights: List[List[float]], kernel_center: str = "center", normalize_weights: bool = False) -> Raster: ...
def vector_hex_binning(self, vector_points: Vector, width: float, orientation: str = "h") -> Vector: ...
def vector_lines_to_raster(self, input: Vector, field_name: str = "FID", zero_background: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def vector_points_to_raster(self, input: Vector, field_name: str = "FID", assign_op: str = "last", zero_background: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def vector_polygons_to_raster(self, input: Vector, field_name: str = "FID", zero_background: bool = False, cell_size: float = 0.0, base_raster: Raster = None) -> Raster: ...
def viewshed(self, dem: Raster, station_points: Vector, station_height: float = 2.0) -> Raster: ...
def visibility_index(self, dem: Raster, station_height: float = 2.0, resolution_factor: int = 8) -> Raster: ...
def voronoi_diagram(self, input_points: Vector) -> Vector: ...
def watershed(self, d8_pointer: Raster, pour_points: Vector, esri_pntr: bool = False) -> Raster: ...
def watershed_from_raster_pour_points(self, d8_pointer: Raster, pour_points: Raster, esri_pntr: bool = False) -> Raster: ...
def weighted_overlay(self, factors: List[Raster], weights: List[float], cost: List[Raster] = None, constraints: List[Raster] = None, scale_max: float = 1.0) -> Raster: ...
def weighted_sum(self, input_rasters: List[Raster], weights: List[float]) -> Raster: ...
def wetness_index(self, specific_catchment_area: Raster, slope: Raster) -> Raster: ...
def wilcoxon_signed_rank_test(self, raster1: Raster, raster2: Raster, output_html_file: str, num_samples: int) -> None: ...
def write_function_memory_insertion(self, image1: Raster, image2: Raster, image3: Raster) -> Raster: ...
def xor_op(self, input1: Raster, input2: Raster) -> Raster: ...
def z_scores(self, raster: Raster) -> Raster: ...
def zonal_statistics(self, data_raster: Raster, feature_definitions_raster: Raster, stat_type: str = "mean") -> Tuple[Raster, str]: ...
Raster class tool functions
There are also a large number of functions associated with the Raster class for manipulating raster data sets. These include:
def __add__(self, other: Union[Raster, float]) -> Raster: ...
def __sub__(self, other: Union[Raster, float]) -> Raster: ...
def __mul__(self, other: Union[Raster, float]) -> Raster: ...
def __truediv__(self, other: Union[Raster, float]) -> Raster: ...
def __floordiv__(self, other: Union[Raster, float]) -> Raster: ...
def __mod__(self, other: Union[Raster, float]) -> Raster: ...
def __pow__(self, other: Union[Raster, float], modulo: float = None) -> Raster: ...
def __neg__(self) -> Raster: ...
def __abs__(self) -> Raster: ...
def __iadd__(self, other: Union[Raster, float]) -> None: ...
def __isub__(self, other: Union[Raster, float]) -> None: ...
def __imul__(self, other: Union[Raster, float]) -> None: ...
def __idiv__(self, other: Union[Raster, float]) -> None: ...
def __getitem__(self, row: int, column: int) -> float: ...
def __setitem__(self, row: int, column: int, value: float) -> None: ...
def acos(self) -> Raster: ...
def acosh(self) -> Raster: ...
def asin(self) -> Raster: ...
def asinh(self) -> Raster: ...
def atan(self) -> Raster: ...
def atan2(self, other: Union[Raster, float]) -> Raster: ...
def atanh(self) -> Raster: ...
def ceil(self) -> Raster: ...
def con(self, con_statement: str, true_raster_or_float: Union[Raster, float, str], false_raster_or_float: Union[Raster, float, str]) -> Raster: ...
def cos(self) -> Raster: ...
def cosh(self) -> Raster: ...
def exp(self) -> Raster: ...
def exp2(self) -> Raster: ...
def floor(self) -> Raster: ...
def is_nodata(self) -> Raster: ...
def ln(self) -> Raster: ...
def log2(self) -> Raster: ...
def log10(self) -> Raster: ...
def max(self, other: Union[Raster, float]) -> Raster: ...
def min(self, other: Union[Raster, float]) -> Raster: ...
def signum(self) -> Raster: ...
def sin(self) -> Raster: ...
def sinh(self) -> Raster: ...
def sqrt(self) -> Raster: ...
def square(self) -> Raster: ...
def tan(self) -> Raster: ...
def tanh(self) -> Raster: ...
def to_degrees(self) -> Raster: ...
def to_radians(self) -> Raster: ...
def trunc(self) -> Raster: ...