Book Image

Mastering SVG

By : Rob Larsen
Book Image

Mastering SVG

By: Rob Larsen

Overview of this book

SVG is the most powerful image format in use on the web. In addition to producing resolution-independent images for today's multi-device world, SVG allows you to create animations and visualizations to add to your sites and applications. The simplicity of cross-platform markup, mixed with familiar modern web languages, such as CSS and JavaScript, creates a winning combination for designers and developers alike. In this book, you will learn how to author an SVG document using common SVG features, such as elements and attributes, and serve SVG on the web using simple configuration tips for common web servers. You will also use SVG elements and images in HTML documents. Further, you will use SVG images for a variety of common tasks, such as manipulating SVG elements, adding animations using CSS, mastering the basic JavaScript SVG (API) using Document Object Model (DOM) methods, and interfacing SVG with common libraries and frameworks, such as React, jQuery, and Angular. You will then build an understanding of the Snap.svg and SVG.js APIs, along with the basics of D3, and take a look at how to implement interesting visualizations using the library. By the end of the book, you will have mastered creating animations with SVG.
Table of Contents (17 chapters)
Title Page

Drawing with code

Before you learn about other basic implementations, it's worth taking a look at the previous screenshot in a little more depth. Instead of just being text like the first example (which, after all, you could have just done in HTML), it shows four circles diagonally arranged across the canvas. Let's take a look at the source of that image and learn our first visual element in SVG, the circle element.

The following code sample shows the circle in action. It also shows how simple changes in markup attribute values can create visually interesting patterns. In it there are five circle elements. These all take advantage of four new attributes. cx and cy represent the center x and center y coordinates of the element on a coordinate plane. r represents the radius of the circle. fill defines the color that will fill the circle. fill accepts any valid CSS color value ( In this case, we're using a red,green, blue, alpha (RGBA) value to fill this with variations on pure red. The first few values remain the same while the fourth value, the alpha, doubles every time from .125 to 1 (fully opaque). Similarly, cx, cy, and r double each time. This produces the pattern you saw earlier. This isn't the most elaborate SVG image, but it does show you how easy basic SVG elements are to use and understand: 

<?xml version="1.0" encoding="UTF-8"?>
<svg width="250" height="250" viewBox="0 0 250 250" version="1.1" xmlns="">
       <circle cx="12.5" cy="12.5" r="6.25" fill="rgba(255,0,0,.125)">
       <circle cx="25" cy="25" r="12.5" fill="rgba(255,0,0,.25)">
       <circle cx="50" cy="50" r="25" fill="rgba(255,0,0,.5)"></circle>
       <circle cx="100" cy="100" r="50" fill="rgba(255,0,0,.75)">
       <circle cx="200" cy="200" r="100" fill="rgba(255,0,0,1)">

Scalable + vector graphics

Now that you've seen an example of a drawing created with SVG, it might be useful to take a second to explain the VG in SVG and why that makes the file format scalable

With raster (bitmap) file formats, you're probably familiar with formats such as JPG, PNG, or GIF. You can think of the image data as being stored pixel by pixel, so each point in an image is stored in the file and read out by the browser or graphics program pixel by pixel and row by row. The size and quality of the image is constrained by the size and quality at the time of creation. 




There are optimizations for all the bitmapped file formats that limit the actual amount of data stored. For example, GIFs use the LZ77 algorithm to collapse redundant pixels down to a backpointer and reference pixel. Imagine if your image has 100 pixels of pure black in a row. The algorithm will search through the image for a sequence of same-bytes and when a sequence is encountered, the algorithm will search backwards through the document to find the first instance of that pattern. It will then replace all those pixels with instructions (a backpointer) on how many characters back to search and how many pixels to copy to fill in the number of same-bytes. In this case, it would be 100 (pixels to search) and 1 (pixels to copy).

Vector graphics, on the other hand, are defined by vectors and control points. To simplify significantly, you can think of vector graphics as being a set of numbers that describe the shape of a line. They may be a set of specific points or they may be, as in the case of the circle earlier, a set of instructions on how to create a specific type of object. The circle element doesn't store every pixel that makes up the circle. It stores the arguments used to create the circle.

Why is this cool? One reason is that because it's just a set of instructions defining the shape, which you can scale in or out, and the rendering engine will just calculate new values accordingly. For that reason, vector graphics can scale infinitely without loss of fidelity.

If that's all confusing to you, don't worry about it. The more you work with them, the more familiar you'll be with the way vector graphics work. In the meantime, the following set of examples and figures will help to illustrate the difference. First, look at the following markup. It represents four images, using the exact same SVG image as the source. The image represents the SVG logo. The dimensions are set at the image's natural size and then 2x, 4x, and 8x, the image's natural size:

      <img src="svg-logo-h.svg" width="195" height="82" alt="The SVG 
       logo at natural dimensions">
      <img src="svg-logo-h.svg" width="390" height="164" alt="The SVG 
       logo 2x">
      <img src="svg-logo-h.svg" width="780" height="328" alt="The SVG
       logo 4x">
      <img src="svg-logo-h.svg" width="1560" height="656" alt="The SVG
       logo 8x">

 Rendered in the browser, that markup produces the following. Notice that it's completely crisp all the way up to 8x, the original size:

Now, look at the same markup, this time with PNGs. It follows the same pattern:

      <img src="svg-logo-h.png" width="195" height="82" alt="The SVG
       logo at 'natural' dimensions">
      <img src="svg-logo-h.png" width="390" height="164" alt="The SVG 
       logo 2x">
      <img src="svg-logo-h.png" width="780" height="328" alt="The SVG
       logo 4x">
      <img src="svg-logo-h.png" width="1560" height="656" alt="The SVG
       logo 8x">

But now, see the result. Notice that, at the natural level, there is no difference between the SVG and PNG. The pixels in the PNG are enough to match the vector-defined lines in the SVG Version. Also, notice how the image gets progressively worse as the image gets larger. There is no way for the browser to get more information (more pixels) out of the bitmapped format to fill in the details at the larger size. It simply scales up the pixels that it has, with terribleresults (especially at the 8x level):