Part 3. Mastering technicalities

The topics treated in this part are of a more technical nature than in the preceding chapters. You’ll need the material in these chapters when you’re no longer satisfied with gnuplot’s defaults and want to establish greater control over the appearance of your figures, or if you want to automate and configure the way you work with gnuplot. Chapter 9 explains how to customize many aspects of graph elements, such as their color, the shape of point symbols, and the dash patterns used to draw lines. You’ll also learn how to change the overall appearance of a graph, such as its size and aspect ratio. Chapter 10 describes in detail how you can export graphs to file, using gnuplot’s terminal capabilities. Chapter 11 discusses ways to automate repetitive tasks, either using gnuplot’s own scripting facilities or by using gnuplot in conjunction with scripts or programs written in a separate programming language. Chapter 12 presents options to customize your gnuplot work environment.

Color figure 1. Alpha shading and transparency. When a partially transparent color is added to an existing background, the added (foreground) color tends to prevail; adding it to a background of the same hue just increases the intensity. In the left panel, this effect is used to visualize point density in a dense data set. All data points are drawn with both blue disks and red rims. Because contributions from overlapping points add visually, regions of high point density show up as areas of high color intensity. Regions where the density is high enough for the rims to contribute significantly appear red. The right panel demonstrates what kinds of color mixtures you can expect when several colors are added together. (See chapter 9 for details.)

Color figure 2. Using color gradients to visualize data values. In the left panel, color is used to indicate the order in which particles were added to the cluster. The right panel shows a section of the complex plane, including part of the Mandelbrot set (black) and its fractal boundary. Color is used to visualize the number of iteration steps before the Mandelbrot iteration diverged. (See chapter 9 and appendix D.)

Color figure 3. The three built-in color sequences that can be selected using set colorsequence, and the custom sequence that was used for the color illustrations in this book. (See chapters 9 and 12 for details.)

Color figure 4. Several color gradients (or palettes) for visualization purposes, as discussed in appendix D. Each panel in the figure displays a different palette.

Color figure 5. Using stylesheets to change the appearance of a plot. The plot in this figure has been prepared with a stylesheet that uses relatively bright colors, but thin lines. (Compare color figure 6. Also see listing 12.7 in chapter 12 for details.)

Color figure 6. Using stylesheets to change the appearance of a plot. The plot in this figure has been prepared with a stylesheet that uses soft pastel colors, but relatively thick lines. (Compare color figure 5. Also see listing 12.8 in chapter 12 for details.)

Color figure 7. Using a combination of several graphical techniques to represent a complicated, multivariate data set. The figure shows a parallel-coordinates plot of the entire glass data set. Because of the relatively large number of records, all lines are drawn partially transparent so they don’t obscure each other. A subset of records has been highlighted in a different color: records with a Calcium (Ca) content between 9 and 10 have been selected for highlighting. (See chapter 14 for details.)

Color figure 8. A false-color plot prepared using a color gradient. The color of each square represents the number of defects produced by each machine on each day of the month. Missing squares indicate that the corresponding machine wasn’t used on that day. Many different aspects of the operation are visible in this graph. You should be able to recognize weekends and two clusters of machines that seem more error-prone than the others. This data set contains a handful of outliers (that is, isolated instances with an excessive number of defects), which are drawn in black. (See appendix D for details.)