An advanced geospatial data analysis platform
Project description
whitebox
A Python package for advanced geospatial data analysis.
This page is related to the whitebox Python package for geospatial analysis, which is built on a stand-alone executable command-line program called WhiteboxTools.
Authors: Dr. John Lindsay (http://www.uoguelph.ca/~hydrogeo/index.html)
Contributors: Dr. Qiusheng Wu (https://wetlands.io)
GitHub repo: https://github.com/giswqs/whitebox
WhiteboxTools: https://github.com/jblindsay/whitebox-tools
Documentation: https://whitebox.readthedocs.io
Free software: MIT license
Contents
Description
The whitebox Python package is built on WhiteboxTools, an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage; jblindsay) at the University of Guelph’s Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. LiDAR point clouds can be interrogated (LidarInfo, LidarHistogram), segmented, tiled and joined, analyized for outliers, interpolated to rasters (DEMs, intensity images), and ground-points can be classified or filtered. WhiteboxTools is not a cartographic or spatial data visualization package; instead it is meant to serve as an analytical backend for other data visualization software, mainly GIS.
Installation
whitebox supports a variety of platforms, including Microsoft Windows, macOS, and Linux operating systems. Note that you will need to have Python 3.x installed. Python 2.x is not supported. The whitebox Python package can be installed using the following command:
pip install whiteboxIf you have installed whitebox Python package before and want to upgrade to the latest version, you can use the following command:
pip install whitebox -UIt is recommended that you use a Python virtual environment (e.g., conda) to test the whitebox package. Please follow the conda user guide to install conda if necessary. Once you have conda installed, you can use Terminal or an Anaconda Promopt to create a Python virtual environment. Check managing Python environment for more information.
conda create -n py36 python=3.6
source activate py36
pip install whiteboxUsage
Tool names in the whitebox Python package can be called either using the snake_case or CamelCase convention (e.g. lidar_info or LidarInfo). See below for an example Python script (example.py). If you are interested in using the WhiteboxTools command-line program, check WhiteboxTools Usage.
import os
import pkg_resources
import whitebox
wbt = whitebox.WhiteboxTools()
print(wbt.version())
print(wbt.help())
# identify the sample data directory of the package
data_dir = os.path.dirname(pkg_resources.resource_filename("whitebox", 'testdata/'))
wbt.set_working_dir(data_dir)
wbt.verbose = False
wbt.feature_preserving_denoise("DEM.tif", "smoothed.tif", filter=9)
wbt.breach_depressions("smoothed.tif", "breached.tif")
wbt.d_inf_flow_accumulation("breached.tif", "flow_accum.tif")WhiteboxTools also provides a Graphical User Interface (GUI) - WhiteboxTools Runner, which can be invoked using the following Python script:
import whitebox
whitebox.Runner()
Available Tools
The whitebox library currently contains the following 323 tools, which are grouped based on their main function into one of the following categories: Data Tools, GIS Analysis, Hydrological Analysis, Image Analysis, LiDAR Analysis, Mathematical and Statistical Analysis, Stream Network Analysis, and Terrain Analysis. The following is a complete listing of available tools, with brief tool descriptions.
Data Tools
ConvertNodataToZero: Converts nodata values in a raster to zero.
ConvertRasterFormat: Converts raster data from one format to another.
ExportTableToCsv: Exports an attribute table to a CSV text file.
IdwInterpolation: Interpolates vector points into a raster surface using an inverse-distance weighted scheme.
NewRasterFromBase: Creates a new raster using a base image.
PrintGeoTiffTags: Prints the tags within a GeoTIFF.
SetNodataValue: Assign a specified value in an input image to the NoData value.
VectorLinesToRaster: Converts a vector containing polylines into a raster.
VectorPointsToRaster: Converts a vector containing points into a raster.
VectorPolygonsToRaster: Converts a vector containing polygons into a raster.
Geomorphometric Analysis
Aspect: Calculates an aspect raster from an input DEM.
DevFromMeanElev: Calculates deviation from mean elevation.
DiffFromMeanElev: Calculates difference from mean elevation (equivalent to a high-pass filter).
DirectionalRelief: Calculates relief for cells in an input DEM for a specified direction.
DownslopeIndex: Calculates the Hjerdt et al. (2004) downslope index.
ElevAbovePit: Calculate the elevation of each grid cell above the nearest downstream pit cell or grid edge cell.
ElevPercentile: Calculates the elevation percentile raster from a DEM.
ElevRelativeToMinMax: Calculates the elevation of a location relative to the minimum and maximum elevations in a DEM.
ElevRelativeToWatershedMinMax: Calculates the elevation of a location relative to the minimum and maximum elevations in a watershed.
FeaturePreservingDenoise: Reduces short-scale variation in an input DEM using a modified Sun et al. (2007) algorithm.
FetchAnalysis: Performs an analysis of fetch or upwind distance to an obstacle.
FillMissingData: Fills nodata holes in a DEM.
FindRidges: Identifies potential ridge and peak grid cells.
Hillshade: Calculates a hillshade raster from an input DEM.
HypsometricAnalysis: Calculates a hypsometric curve for one or more DEMs.
MaxAnisotropyDev: Calculates the maximum anisotropy (directionality) in elevation deviation over a range of spatial scales.
MaxAnisotropyDevSignature: Calculates the anisotropy in deviation from mean for points over a range of spatial scales.
MaxBranchLength: Lindsay and Seibert’s (2013) branch length index is used to map drainage divides or ridge lines.
MaxDownslopeElevChange: Calculates the maximum downslope change in elevation between a grid cell and its eight downslope neighbors.
MaxElevationDeviation: Calculates the maximum elevation deviation over a range of spatial scales.
MaxElevDevSignature: Calculates the maximum elevation deviation over a range of spatial scales and for a set of points.
MinDownslopeElevChange: Calculates the minimum downslope change in elevation between a grid cell and its eight downslope neighbors.
MultiscaleRoughness: Calculates surface roughness over a range of spatial scales.
MultiscaleRoughnessSignature: Calculates the surface roughness for points over a range of spatial scales.
MultiscaleTopographicPositionImage: Creates a multiscale topographic position image from three DEVmax rasters of differing spatial scale ranges.
HorizonAngle: Calculates horizon angle (maximum upwind slope) for each grid cell in an input DEM.
NumDownslopeNeighbours: Calculates the number of downslope neighbours to each grid cell in a DEM.
NumUpslopeNeighbours: Calculates the number of upslope neighbours to each grid cell in a DEM.
PennockLandformClass: Classifies hillslope zones based on slope, profile curvature, and plan curvature.
PercentElevRange: Calculates percent of elevation range from a DEM.
PlanCurvature: Calculates a plan (contour) curvature raster from an input DEM.
ProfileCurvature: Calculates a profile curvature raster from an input DEM.
Profile: Plots profiles from digital surface models.
RelativeAspect: Calculates relative aspect (relative to a user-specified direction) from an input DEM.
RelativeStreamPowerIndex: Calculates the relative stream power index.
RelativeTopographicPosition: Calculates the relative topographic position index from a DEM.
RuggednessIndex: Calculates the Riley et al.’s (1999) terrain ruggedness index from an input DEM.
RemoveOffTerrainObjects: Removes off-terrain objects from a raster digital elevation model (DEM).
SedimentTransportIndex: Calculates the sediment transport index.
Slope: Calculates a slope raster from an input DEM.
SlopeVsElevationPlot: Creates a slope vs. elevation plot for one or more DEMs.
TangentialCurvature: Calculates a tangential curvature raster from an input DEM.
TotalCurvature: Calculates a total curvature raster from an input DEM.
Viewshed: Identifies the viewshed for a point or set of points.
VisibilityIndex: Estimates the relative visibility of sites in a DEM.
WetnessIndex: Calculates the topographic wetness index, Ln(A / tan(slope)).
GIS Analysis
AggregateRaster: Aggregates a raster to a lower resolution.
AverageOverlay: Calculates the average for each grid cell from a group of raster images.
BufferRaster: Maps a distance-based buffer around each non-background (non-zero/non-nodata) grid cell in an input image.
Centroid: Calculates the centroid, or average location, of raster polygon objects.
ClipRasterToPolygon: Clips a raster to a vector polygon.
Clump: Groups cells that form physically discrete areas, assigning them unique identifiers.
CountIf: Counts the number of occurrences of a specified value in a cell-stack of rasters.
CostAllocation: Identifies the source cell to which each grid cell is connected by a least-cost pathway in a cost-distance analysis.
CostDistance: Performs cost-distance accumulation on a cost surface and a group of source cells.
CostPathway: Performs cost-distance pathway analysis using a series of destination grid cells.
CreatePlane: Creates a raster image based on the equation for a simple plane.
EdgeProportion: Calculate the proportion of cells in a raster polygon that are edge cells.
ErasePolygonFromRaster: Erases (cuts out) a vector polygon from a raster.
EuclideanAllocation: Assigns grid cells in the output raster the value of the nearest target cell in the input image, measured by the Shih and Wu (2004) Euclidean distance transform.
EuclideanDistance: Calculates the Shih and Wu (2004) Euclidean distance transform.
FindPatchOrClassEdgeCells: Finds all cells located on the edge of patch or class features.
HighestPosition: Identifies the stack position of the maximum value within a raster stack on a cell-by-cell basis.
LowestPosition: Identifies the stack position of the minimum value within a raster stack on a cell-by-cell basis.
MaxAbsoluteOverlay: Evaluates the maximum absolute value for each grid cell from a stack of input rasters.
MaxOverlay: Evaluates the maximum value for each grid cell from a stack of input rasters.
MinAbsoluteOverlay: Evaluates the minimum absolute value for each grid cell from a stack of input rasters.
MinOverlay: Evaluates the minimum value for each grid cell from a stack of input rasters.
PercentEqualTo: Calculates the percentage of a raster stack that have cell values equal to an input on a cell-by-cell basis.
PercentGreaterThan: Calculates the percentage of a raster stack that have cell values greater than an input on a cell-by-cell basis.
PercentLessThan: Calculates the percentage of a raster stack that have cell values less than an input on a cell-by-cell basis.
PickFromList: Outputs the value from a raster stack specified by a position raster.
RadiusOfGyration: Calculates the distance of cells from their polygon’s centroid.
RasterCellAssignment: Assign row or column number to cells.
Reclass: Reclassifies the values in a raster image.
ReclassEqualInterval: Reclassifies the values in a raster image based on equal-ranges.
ReclassFromFile: Reclassifies the values in a raster image using reclass ranges in a text file.
WeightedOverlay: Performs a weighted sum on multiple input rasters after converting each image to a common scale. The tool performs a multi-criteria evaluation (MCE).
WeightedSum: Performs a weighted-sum overlay on multiple input raster images.
Hydrological Analysis
AverageFlowpathSlope: measures the average length of all upslope flowpaths draining each grid cell.
AverageUpslopeFlowpathLength: Measures the average length of all upslope flowpaths draining each grid cell.
Basins: Identifies drainage basins that drain to the DEM edge.
BreachDepressions: Breaches all of the depressions in a DEM using Lindsay’s (2016) algorithm. This should be preferred over depression filling in most cases.
BreachSingleCellPits: Removes single-cell pits from an input DEM by breaching.
D8FlowAccumulation: Calculates a D8 flow accumulation raster from an input DEM.
D8MassFlux: Performs a D8 mass flux calculation.
D8Pointer: Calculates a D8 flow pointer raster from an input DEM.
DepthInSink: Measures the depth of sinks (depressions) in a DEM.
DInfFlowAccumulation: Calculates a D-infinity flow accumulation raster from an input DEM.
DInfMassFlux: Performs a D-infinity mass flux calculation.
DInfPointer: Calculates a D-infinity flow pointer (flow direction) raster from an input DEM.
DownslopeDistanceToStream: Measures distance to the nearest downslope stream cell.
DownslopeFlowpathLength: Calculates the downslope flowpath length from each cell to basin outlet.
ElevationAboveStream: Calculates the elevation of cells above the nearest downslope stream cell.
ElevationAboveStreamEuclidean: Calculates the elevation of cells above the nearest (Euclidean distance) stream cell.
FD8FlowAccumulation: Calculates a FD8 flow accumulation raster from an input DEM.
FD8Pointer: Calculates an FD8 flow pointer raster from an input DEM.
FillBurn: Burns streams into a DEM using the FillBurn (Saunders, 1999) method.
FillDepressions: Fills all of the depressions in a DEM. Depression breaching should be preferred in most cases.
FillSingleCellPits: Raises pit cells to the elevation of their lowest neighbour.
FindNoFlowCells: Finds grid cells with no downslope neighbours.
FindParallelFlow: Finds areas of parallel flow in D8 flow direction rasters.
FlattenLakes: Flattens lake polygons in a raster DEM.
FloodOrder: Assigns each DEM grid cell its order in the sequence of inundations that are encountered during a search starting from the edges, moving inward at increasing elevations.
FlowAccumulationFullWorkflow: Resolves all of the depressions in a DEM, outputting a breached DEM, an aspect-aligned non-divergent flow pointer, a flow accumulation raster.
FlowLengthDiff: Calculates the local maximum absolute difference in downslope flowpath length, useful in mapping drainage divides and ridges.
Hillslopes: Identifies the individual hillslopes draining to each link in a stream network.
ImpoundmentIndex: Calculates the impoundment size resulting from damming a DEM.
Isobasins: Divides a landscape into nearly equal sized drainage basins (i.e. watersheds).
JensonSnapPourPoints: Moves outlet points used to specify points of interest in a watershedding operation to the nearest stream cell.
MaxUpslopeFlowpathLength: Measures the maximum length of all upslope flowpaths draining each grid cell.
NumInflowingNeighbours: Computes the number of inflowing neighbours to each cell in an input DEM based on the D8 algorithm.
RaiseWalls: Raises walls in a DEM along a line or around a polygon, e.g. a watershed.
Rho8Pointer: Calculates a stochastic Rho8 flow pointer raster from an input DEM.
Sink: Identifies the depressions in a DEM, giving each feature a unique identifier.
SnapPourPoints: Moves outlet points used to specify points of interest in a watershedding operation to the cell with the highest flow accumulation in its neighbourhood.
StochasticDepressionAnalysis: Preforms a stochastic analysis of depressions within a DEM.
StrahlerOrderBasins: Identifies Strahler-order basins from an input stream network.
Subbasins: Identifies the catchments, or sub-basin, draining to each link in a stream network.
TraceDownslopeFlowpaths: Traces downslope flowpaths from one or more target sites (i.e. seed points).
UnnestBasins: Extract whole watersheds for a set of outlet points.
Watershed: Identifies the watershed, or drainage basin, draining to a set of target cells.
Image Analysis
AdaptiveFilter: Performs an adaptive filter on an image.
BalanceContrastEnhancement: Performs a balance contrast enhancement on a colour-composite image of multispectral data.
BilateralFilter: A bilateral filter is an edge-preserving smoothing filter introduced by Tomasi and Manduchi (1998).
ChangeVectorAnalysis: Performs a change vector analysis on a two-date multi-spectral dataset.
Closing: A closing is a mathematical morphology operating involving an erosion (min filter) of a dilation (max filter) set.
ConservativeSmoothingFilter: Performs a conservative smoothing filter on an image.
CornerDetection: Identifies corner patterns in boolean images using hit-and-miss pattern mattching.
CorrectVignetting Corrects the darkening of images towards corners.
CreateColourComposite: Creates a colour-composite image from three bands of multispectral imagery.
DirectDecorrelationStretch: Performs a direct decorrelation stretch enhancement on a colour-composite image of multispectral data.
DiffOfGaussianFilter: Performs a Difference of Gaussian (DoG) filter on an image.
DiversityFilter: Assigns each cell in the output grid the number of different values in a moving window centred on each grid cell in the input raster.
EdgePreservingMeanFilter: Performs a simple edge-preserving mean filter on an input image.
EmbossFilter: Performs an emboss filter on an image, similar to a hillshade operation.
FastAlmostGaussianFilter: Performs a fast approximate Gaussian filter on an image.
FlipImage: Reflects an image in the vertical or horizontal axis.
GammaCorrection: Performs a sigmoidal contrast stretch on input images.
GaussianContrastStretch: Performs a Gaussian contrast stretch on input images.
GaussianFilter: Performs a Gaussian filter on an image.
HighPassFilter: Performs a high-pass filter on an input image.
HistogramEqualization: Performs a histogram equalization contrast enhancement on an image.
HistogramMatching: Alters the statistical distribution of a raster image matching it to a specified PDF.
HistogramMatchingTwoImages: This tool alters the cumulative distribution function of a raster image to that of another image.
IhsToRgb: Converts intensity, hue, and saturation (IHS) images into red, green, and blue (RGB) images.
ImageStackProfile: Plots an image stack profile (i.e. signature) for a set of points and multispectral images.
IntegralImage: Transforms an input image (summed area table) into its integral image equivalent.
KMeansClustering: Performs a k-means clustering operation on a multi-spectral dataset.
KNearestMeanFilter: A k-nearest mean filter is a type of edge-preserving smoothing filter.
LaplacianFilter: Performs a Laplacian filter on an image.
LaplacianOfGaussianFilter: Performs a Laplacian-of-Gaussian (LoG) filter on an image.
LeeFilter: Performs a Lee (Sigma) smoothing filter on an image.
LineDetectionFilter: Performs a line-detection filter on an image.
LineThinning: Performs line thinning a on Boolean raster image; intended to be used with the RemoveSpurs tool.
MajorityFilter: Assigns each cell in the output grid the most frequently occurring value (mode) in a moving window centred on each grid cell in the input raster.
MaximumFilter: Assigns each cell in the output grid the maximum value in a moving window centred on each grid cell in the input raster.
MeanFilter: Performs a mean filter (low-pass filter) on an input image.
MedianFilter: Performs a median filter on an input image.
MinMaxContrastStretch: Performs a min-max contrast stretch on an input greytone image.
MinimumFilter: Assigns each cell in the output grid the minimum value in a moving window centred on each grid cell in the input raster.
ModifiedKMeansClustering: Performs a modified k-means clustering operation on a multi-spectral dataset.
Mosaic: Mosaics two or more images together.
OlympicFilter: Performs an olympic smoothing filter on an image.
Opening: An opening is a mathematical morphology operating involving a dilation (max filter) of an erosion (min filter) set.
NormalizedDifferenceVegetationIndex: Calculates the normalized difference vegetation index (NDVI) from near-infrared and red imagery.
PanchromaticSharpening: Increases the spatial resolution of image data by combining multispectral bands with panchromatic data.
PercentageContrastStretch: Performs a percentage linear contrast stretch on input images.
PercentileFilter: Performs a percentile filter on an input image.
PrewittFilter: Performs a Prewitt edge-detection filter on an image.
RangeFilter: Assigns each cell in the output grid the range of values in a moving window centred on each grid cell in the input raster.
RemoveSpurs: Removes the spurs (pruning operation) from a Boolean line image.; intended to be used on the output of the LineThinning tool.
Resample: Resamples one or more input images into a destination image.
RgbToIhs: Converts red, green, and blue (RGB) images into intensity, hue, and saturation (IHS) images.
RobertsCrossFilter: Performs a Robert’s cross edge-detection filter on an image.
ScharrFilter: Performs a Scharr edge-detection filter on an image.
SigmoidalContrastStretch: Performs a sigmoidal contrast stretch on input images.
SobelFilter: Performs a Sobel edge-detection filter on an image.
SplitColourComposite: This tool splits an RGB colour composite image into seperate multispectral images.
StandardDeviationContrastStretch: Performs a standard-deviation contrast stretch on input images.
StandardDeviationFilter: Assigns each cell in the output grid the standard deviation of values in a moving window centred on each grid cell in the input raster.
ThickenRasterLine: Thickens single-cell wide lines within a raster image.
TophatTransform: Performs either a white or black top-hat transform on an input image
TotalFilter: Performs a total filter on an input image.
UnsharpMasking: An image sharpening technique that enhances edges.
UserDefinedWeightsFilter: Performs a user-defined weights filter on an image.
WriteFunctionMemoryInsertion: Performs a write function memory insertion for single-band multi-date change detection.
LiDAR Analysis
BlockMaximum: Creates a block-maximum raster from an input LAS file.
BlockMinimum: Creates a block-minimum raster from an input LAS file.
ClassifyOverlapPoints: Classifies or filters LAS point in regions of overlapping flight lines.
ClipLidarToPolygon: Clips a LiDAR point cloud to a vector polygon or polygons.
ErasePolygonFromLidar: Erases (cuts out) a vector polygon or polygons from a LiDAR point cloud.
FilterLidarScanAngles: Removes points in a LAS file with scan angles greater than a threshold.
FindFlightlineEdgePoints: Identifies points along a flightline’s edge in a LAS file.
FlightlineOverlap: Reads a LiDAR (LAS) point file and outputs a raster containing the number of overlapping flight lines in each grid cell.
LidarElevationSlice: Outputs all of the points within a LiDAR (LAS) point file that lie between a specified elevation range.
LasToAscii: Converts one or more LAS files into ASCII text files.
LidarColourize: Adds the red-green-blue colour fields of a LiDAR (LAS) file based on an input image.
LidarGroundPointFilter: Identifies ground points within LiDAR dataset.
LidarIdwInterpolation: Interpolates LAS files using an inverse-distance weighted (IDW) scheme.
LidarHillshade: Calculates a hillshade value for points within a LAS file and stores these data in the RGB field.
LidarHistogram: Creates a histogram from LiDAR data.
LidarInfo: Prints information about a LiDAR (LAS) dataset, including header, point return frequency, and classification data and information about the variable length records (VLRs) and geokeys.
LidarJoin: Joins multiple LiDAR (LAS) files into a single LAS file.
LidarKappaIndex: Performs a kappa index of agreement (KIA) analysis on the classifications of two LAS files.
LidarNearestNeighbourGridding: Grids LAS files using nearest-neighbour scheme.
LidarPointDensity: Calculates the spatial pattern of point density for a LiDAR data set.
LidarPointStats: Creates several rasters summarizing the distribution of LAS point data.
LidarRemoveDuplicates: Removes duplicate points from a LiDAR data set.
LidarRemoveOutliers: Removes outliers (high and low points) in a LiDAR point cloud.
LidarSegmentation: Segments a LiDAR point cloud based on normal vectors.
LidarSegmentationBasedFilter: Identifies ground points within LiDAR point clouds using a segmentation based approach.
LidarThin: Thins a LiDAR point cloud, reducing point density.
LidarTile: Tiles a LiDAR LAS file into multiple LAS files.
LidarTophatTransform: Performs a white top-hat transform on a Lidar dataset; as an estimate of height above ground, this is useful for modelling the vegetation canopy.
NormalVectors: Calculates normal vectors for points within a LAS file and stores these data (XYZ vector components) in the RGB field.
Mathematical and Statistical Analysis
AbsoluteValue: Calculates the absolute value of every cell in a raster.
Add: Performs an addition operation on two rasters or a raster and a constant value.
And: Performs a logical AND operator on two Boolean raster images.
Anova: Performs an analysis of variance (ANOVA) test on a raster dataset.
ArcCos: Returns the inverse cosine (arccos) of each values in a raster.
ArcSin: Returns the inverse sine (arcsin) of each values in a raster.
ArcTan: Returns the inverse tangent (arctan) of each values in a raster.
Atan2: Returns the 2-argument inverse tangent (atan2).
AttributeCorrelation: Performs a correlation analysis on attribute fields from a vector database.
AttributeHistogram: Creates a histogram for the field values of a vector’s attribute table.
AttributeScattergram: Creates a scattergram for two field values of a vector’s attribute table.
Ceil: Returns the smallest (closest to negative infinity) value that is greater than or equal to the values in a raster.
Cos: Returns the cosine (cos) of each values in a raster.
Cosh: Returns the hyperbolic cosine (cosh) of each values in a raster.
CrispnessIndex: Calculates the Crispness Index, which is used to quantify how crisp (or conversely how fuzzy) a probability image is.
CrossTabulation: Performs a cross-tabulation on two categorical images.
CumulativeDistribution: Converts a raster image to its cumulative distribution function.
Decrement: Decreases the values of each grid cell in an input raster by 1.0.
Divide: Performs a division operation on two rasters or a raster and a constant value.
EqualTo: Performs a equal-to comparison operation on two rasters or a raster and a constant value.
Exp: Returns the exponential (base e) of values in a raster.
Exp2: Returns the exponential (base 2) of values in a raster.
ExtractRasterStatistics: Extracts descriptive statistics for a group of patches in a raster.
Floor: Returns the largest (closest to positive infinity) value that is greater than or equal to the values in a raster.
GreaterThan: Performs a greater-than comparison operation on two rasters or a raster and a constant value.
ImageAutocorrelation: Performs Moran’s I analysis on two or more input images.
ImageCorrelation: Performs image correlation on two or more input images.
ImageRegression: Performs image regression analysis on two input images.
Increment: Increases the values of each grid cell in an input raster by 1.0.
InPlaceAdd: Performs an in-place addition operation (input1 += input2).
InPlaceDivide: Performs an in-place division operation (input1 /= input2).
InPlaceMultiply: Performs an in-place multiplication operation (input1 * = input2).
InPlaceSubtract: Performs an in-place subtraction operation (input1 -= input2).
IntegerDivision: Performs an integer division operation on two rasters or a raster and a constant value.
IsNoData: Identifies NoData valued pixels in an image.
KappaIndex: Performs a kappa index of agreement (KIA) analysis on two categorical raster files.
KSTestForNormality: Evaluates whether the values in a raster are normally distributed.
LessThan: Performs a less-than comparison operation on two rasters or a raster and a constant value.
ListUniqueValues: Lists the unique values contained in a field witin a vector’s attribute table.
Log10: Returns the base-10 logarithm of values in a raster.
Log2: Returns the base-2 logarithm of values in a raster.
Ln: Returns the natural logarithm of values in a raster.
Max: Performs a MAX operation on two rasters or a raster and a constant value.
Min: Performs a MIN operation on two rasters or a raster and a constant value.
Modulo: Performs a modulo operation on two rasters or a raster and a constant value.
Multiply: Performs a multiplication operation on two rasters or a raster and a constant value.
Negate: Changes the sign of values in a raster or the 0-1 values of a Boolean raster.
Not: Performs a logical NOT operator on two Boolean raster images.
NotEqualTo: Performs a not-equal-to comparison operation on two rasters or a raster and a constant value.
Or: Performs a logical OR operator on two Boolean raster images.
Power: Raises the values in grid cells of one rasters, or a constant value, by values in another raster or constant value.
PrincipalComponentAnalysis: Performs a principal component analysis (PCA) on a multi-spectral dataset.
Quantiles: Transforms raster values into quantiles.
RandomField: Creates an image containing random values.
RandomSample: Creates an image containing randomly located sample grid cells with unique IDs.
RasterHistogram: Creates a histogram from raster values.
RasterSummaryStats: Measures a rasters average, standard deviation, num. non-nodata cells, and total.
Reciprocal: Returns the reciprocal (i.e. 1 / z) of values in a raster.
RescaleValueRange: Performs a min-max contrast stretch on an input greytone image.
RootMeanSquareError: Calculates the RMSE and other accuracy statistics.
Round: Rounds the values in an input raster to the nearest integer value.
Sin: Returns the sine (sin) of each values in a raster.
Sinh: Returns the hyperbolic sine (sinh) of each values in a raster.
Square: Squares the values in a raster.
SquareRoot: Returns the square root of the values in a raster.
Subtract: Performs a subtraction operation on two rasters or a raster and a constant value.
Tan: Returns the tangent (tan) of each values in a raster.
Tanh: Returns the hyperbolic tangent (tanh) of each values in a raster.
ToDegrees: Converts a raster from radians to degrees.
ToRadians: Converts a raster from degrees to radians.
TrendSurface: Estimates the trend surface of an input raster file.
TrendSurfaceVectorPoints: Estimates a trend surface from vector points.
Truncate: Truncates the values in a raster to the desired number of decimal places.
TurningBandsSimulation: Creates an image containing random values based on a turning-bands simulation.
Xor: Performs a logical XOR operator on two Boolean raster images.
ZScores: Standardizes the values in an input raster by converting to z-scores.
Stream Network Analysis
DistanceToOutlet: Calculates the distance of stream grid cells to the channel network outlet cell.
ExtractStreams: Extracts stream grid cells from a flow accumulation raster.
ExtractValleys: Identifies potential valley bottom grid cells based on local topolography alone.
FarthestChannelHead: Calculates the distance to the furthest upstream channel head for each stream cell.
FindMainStem: Finds the main stem, based on stream lengths, of each stream network.
HackStreamOrder: Assigns the Hack stream order to each link in a stream network.
HortonStreamOrder: Assigns the Horton stream order to each link in a stream network.
LengthOfUpstreamChannels: Calculates the total length of channels upstream.
LongProfile: Plots the stream longitudinal profiles for one or more rivers.
LongProfileFromPoints: Plots the longitudinal profiles from flow-paths initiating from a set of vector points.
RasterizeStreams: Rasterizes vector streams based on Lindsay (2016) method.
RemoveShortStreams: Removes short first-order streams from a stream network.
ShreveStreamMagnitude: Assigns the Shreve stream magnitude to each link in a stream network.
StrahlerStreamOrder: Assigns the Strahler stream order to each link in a stream network.
StreamLinkClass: Identifies the exterior/interior links and nodes in a stream network.
StreamLinkIdentifier: Assigns a unique identifier to each link in a stream network.
StreamLinkLength: Estimates the length of each link (or tributary) in a stream network.
StreamLinkSlope: Estimates the average slope of each link (or tributary) in a stream network.
StreamSlopeContinuous: Estimates the slope of each grid cell in a stream network.
TopologicalStreamOrder: Assigns each link in a stream network its topological order.
TributaryIdentifier: Assigns a unique identifier to each tributary in a stream network.
Supported Data Formats
The WhiteboxTools library currently supports read/writing raster data in Whitebox GAT, GeoTIFF, ESRI (ArcGIS) ASCII and binary (.flt & .hdr), GRASS GIS, Idrisi, SAGA GIS (binary and ASCII), and Surfer 7 data formats. At present, there is limited ability in WhiteboxTools to read vector geospatial data. Support for Shapefile (and other common vector formats) will be enhanced within the library soon.
Contributing
If you would like to contribute to the project as a developer, follow these instructions to get started:
Fork the whitebox project (https://github.com/giswqs/whitebox)
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am ‘Add some feature’)
Push to the branch (git push origin my-new-feature)
Create a new Pull Request
License
The whitebox package is distributed under the MIT license, a permissive open-source (free software) license.
Reporting Bugs
Report bugs at https://github.com/giswqs/whitebox/issues.
If you are reporting a bug, please include:
Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.2.0 (2018-06-08)
0.1.0 (2018-06-06)
First release on PyPI.
Release history Release notifications | RSS feed
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