Graphical Analysis and Plotting Tools
Graphical Analysis Books
A selection of books and book chapters that describe the uses and techniques of graphical data analysis:
- Janert, P. K. (2011), Data Analysis with Open Source Tools, is my preferred book for teaching the basic concepts of data analysis. Section I, Chapters 2-6 are focused on graphical methods. Available on-line through the Purdue library.
- Statistical Methods in Water Resources: Chapter 2 Graphical Analysis
Graphical Analysis and Plotting Tools
- Python matplotlib module - see matplotlib section below.
- Python plotly module for developing high-quality, interactive figures for web sites.
- Generic Mapping Tools (GMT)
- Home page: http://gmt.soest.hawaii.edu/
- Generic Mapping Tools (GMT) Utilities
- Tutorial documents
- Home page contains a nice general tutorial, here is the link for GMT 5.4.5.
- Plotting Data with GMT is my own take on the GMT tutorial
- Specific information on using Time and Date axis
- GNUPLOT
- Home page: http://www.gnuplot.info/
- MatLab
- ArcGIS Tools
- ImageMagick - software suite for creating, editing, composing and converting raster image files.
Plotting in Python
Best fundamental plotting package for Python is matplotlib, which is modeled on the plotting functionality of MatLab.
- Python matplotlib module - see Using Python section for basic tutorials.
- Additional tutorials and information:
Other packages that build on matplotlib to provide additional functionality and usability, include:
- Seaborn - statistical data visualization
Developing presentation and publication quality graphics
- Basic help with figure design and color selection
- Ten Simple Rule for Better Figures
- The End of the Rainbow? Color Schemes for Improved Data Graphics
- The colorbrewer2.org site for help in selecting color schemes
- More on colorblindness and appropriate color schemes: http://mkweb.bcgsc.ca/colorblind/
- Tools for drawing, for example creating schematics and flow charts
- Powerpoint - fairly easy to use, but need to recognize that its tools are basic and it provides limited control of output image quality.
- Adobe Illustrator - Lots more control and options, which makes it harder to pick up quickly, but worth the investment. Adobe licensing is a challenge, check out Purdue's licensing agreement at https://www.itap.purdue.edu/shopping/software/product/adobe.html to find out how to use the software. Adobe Illustrator is now part of the Adobe Creative Cloud Suite.
- Xfig - available on many Linux/Unix platforms or as part of their supplemental package system. Cannot confirm continued support for the package as of 22 Apr 2016.
- Inkspace - multi-platform, open-source drawing package.
- Tools for working with photos
- Most OS have basic photo editors embedded, look on-line for information about the best to use with your system.
- Adobe Photoshop - Lots more options and control, which makes it harder to get up and running but worth learning if you want to manipulate photos and similar images. See Adobe Illustrator (above) for links to the Adobe license.
- GIMP (GNU Image Manipulation Program) - available on many Unix/Linux platforms or can be installed on most operating systems now, including OS X and Microsoft Windows. Much of the functionality of Photoshop, but as a free and open source download.
- ImageMagick - available on most Unix/Linux systems, this is a free and open source image manipulation tool that primarily works on the command line. Does anything from simple image format conversions to more complicated command line photo editing. Works extremely well for automating an image processing pipeline, where standard changes are applied to a large number of images.
- Related Tutorials
- Discussion of color scales available with matplotlib and tools for viewing figures with common color perception problems.
- Tutorial for creating custom color scale for GMT.
- Tutorial for converting image formats with ImageMagick.