Skip to main content

An interactive GUI for whitebox-tools in a Jupyter-based environment

Project description

whiteboxgui

image image image image image image

An interactive GUI for WhiteboxTools in a Jupyter-based environment

Description

The whiteboxgui Python package is a Jupyter frontend for 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.

The WhiteboxTools currently contains 518 tools, which are each 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. For a listing of available tools, complete with documentation and usage details, please see the WhiteboxTools User Manual.

Installation

whiteboxgui is available on PyPI. To install whiteboxgui, run this command in your terminal:

pip install whiteboxgui

whiteboxgui is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can create a conda Python environment to install whiteboxgui:

conda create -n wbt python
conda activate wbt
conda install mamba -c conda-forge
mamba install whiteboxgui -c conda-forge

Usage

The whiteboxgui provides a Graphical User Interface (GUI) for WhiteboxTools in a Jupyter-based environment, which can be invoked using the following Python script. You can also try image

import whiteboxgui
whiteboxgui.show(tree=True)

Imgur

Demo

tutorial

Credits

This package was created with Cookiecutter and the giswqs/pypackage project template.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

whiteboxgui-2.2.0.tar.gz (100.1 kB view details)

Uploaded Source

Built Distribution

whiteboxgui-2.2.0-py2.py3-none-any.whl (99.6 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file whiteboxgui-2.2.0.tar.gz.

File metadata

  • Download URL: whiteboxgui-2.2.0.tar.gz
  • Upload date:
  • Size: 100.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.8

File hashes

Hashes for whiteboxgui-2.2.0.tar.gz
Algorithm Hash digest
SHA256 ade973b4220718e2b82cd29bab4878847b422011a95fe5b1376b8c680155ec55
MD5 2476271464633ad8f732e4415df533fb
BLAKE2b-256 e4969fc31fd37107f74915a88862a233f272bfb9f211af407e1da543f8302c70

See more details on using hashes here.

File details

Details for the file whiteboxgui-2.2.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for whiteboxgui-2.2.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 8269b9828d5b7f39ae01bdd0e98f1563f28e4629ad025cafad3b003d666db047
MD5 a483af2c0c5995913bcc05487c77a752
BLAKE2b-256 f70e02a181991f0a4cd0644f642ab135c7f7a85702e933cce3abebcfeb8261e4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page