Table of Contents for
Regular Expressions Cookbook, 2nd Edition

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Regular Expressions Cookbook, 2nd Edition by Steven Levithan Published by O'Reilly Media, Inc., 2012
  1. Cover
  2. Regular Expressions Cookbook
  3. Preface
  4. Caught in the Snarls of Different Versions
  5. Intended Audience
  6. Technology Covered
  7. Organization of This Book
  8. Conventions Used in This Book
  9. Using Code Examples
  10. Safari® Books Online
  11. How to Contact Us
  12. Acknowledgments
  13. 1. Introduction to Regular Expressions
  14. Regular Expressions Defined
  15. Search and Replace with Regular Expressions
  16. Tools for Working with Regular Expressions
  17. 2. Basic Regular Expression Skills
  18. 2.1. Match Literal Text
  19. 2.2. Match Nonprintable Characters
  20. 2.3. Match One of Many Characters
  21. 2.4. Match Any Character
  22. 2.5. Match Something at the Start and/or the End of a Line
  23. 2.6. Match Whole Words
  24. 2.7. Unicode Code Points, Categories, Blocks, and Scripts
  25. 2.8. Match One of Several Alternatives
  26. 2.9. Group and Capture Parts of the Match
  27. 2.10. Match Previously Matched Text Again
  28. 2.11. Capture and Name Parts of the Match
  29. 2.12. Repeat Part of the Regex a Certain Number of Times
  30. 2.13. Choose Minimal or Maximal Repetition
  31. 2.14. Eliminate Needless Backtracking
  32. 2.15. Prevent Runaway Repetition
  33. 2.16. Test for a Match Without Adding It to the Overall Match
  34. 2.17. Match One of Two Alternatives Based on a Condition
  35. 2.18. Add Comments to a Regular Expression
  36. 2.19. Insert Literal Text into the Replacement Text
  37. 2.20. Insert the Regex Match into the Replacement Text
  38. 2.21. Insert Part of the Regex Match into the Replacement Text
  39. 2.22. Insert Match Context into the Replacement Text
  40. 3. Programming with Regular Expressions
  41. Programming Languages and Regex Flavors
  42. 3.1. Literal Regular Expressions in Source Code
  43. 3.2. Import the Regular Expression Library
  44. 3.3. Create Regular Expression Objects
  45. 3.4. Set Regular Expression Options
  46. 3.5. Test If a Match Can Be Found Within a Subject String
  47. 3.6. Test Whether a Regex Matches the Subject String Entirely
  48. 3.7. Retrieve the Matched Text
  49. 3.8. Determine the Position and Length of the Match
  50. 3.9. Retrieve Part of the Matched Text
  51. 3.10. Retrieve a List of All Matches
  52. 3.11. Iterate over All Matches
  53. 3.12. Validate Matches in Procedural Code
  54. 3.13. Find a Match Within Another Match
  55. 3.14. Replace All Matches
  56. 3.15. Replace Matches Reusing Parts of the Match
  57. 3.16. Replace Matches with Replacements Generated in Code
  58. 3.17. Replace All Matches Within the Matches of Another Regex
  59. 3.18. Replace All Matches Between the Matches of Another Regex
  60. 3.19. Split a String
  61. 3.20. Split a String, Keeping the Regex Matches
  62. 3.21. Search Line by Line
  63. Construct a Parser
  64. 4. Validation and Formatting
  65. 4.1. Validate Email Addresses
  66. 4.2. Validate and Format North American Phone Numbers
  67. 4.3. Validate International Phone Numbers
  68. 4.4. Validate Traditional Date Formats
  69. 4.5. Validate Traditional Date Formats, Excluding Invalid Dates
  70. 4.6. Validate Traditional Time Formats
  71. 4.7. Validate ISO 8601 Dates and Times
  72. 4.8. Limit Input to Alphanumeric Characters
  73. 4.9. Limit the Length of Text
  74. 4.10. Limit the Number of Lines in Text
  75. 4.11. Validate Affirmative Responses
  76. 4.12. Validate Social Security Numbers
  77. 4.13. Validate ISBNs
  78. 4.14. Validate ZIP Codes
  79. 4.15. Validate Canadian Postal Codes
  80. 4.16. Validate U.K. Postcodes
  81. 4.17. Find Addresses with Post Office Boxes
  82. 4.18. Reformat Names From “FirstName LastName” to “LastName, FirstName”
  83. 4.19. Validate Password Complexity
  84. 4.20. Validate Credit Card Numbers
  85. 4.21. European VAT Numbers
  86. 5. Words, Lines, and Special Characters
  87. 5.1. Find a Specific Word
  88. 5.2. Find Any of Multiple Words
  89. 5.3. Find Similar Words
  90. 5.4. Find All Except a Specific Word
  91. 5.5. Find Any Word Not Followed by a Specific Word
  92. 5.6. Find Any Word Not Preceded by a Specific Word
  93. 5.7. Find Words Near Each Other
  94. 5.8. Find Repeated Words
  95. 5.9. Remove Duplicate Lines
  96. 5.10. Match Complete Lines That Contain a Word
  97. 5.11. Match Complete Lines That Do Not Contain a Word
  98. 5.12. Trim Leading and Trailing Whitespace
  99. 5.13. Replace Repeated Whitespace with a Single Space
  100. 5.14. Escape Regular Expression Metacharacters
  101. 6. Numbers
  102. 6.1. Integer Numbers
  103. 6.2. Hexadecimal Numbers
  104. 6.3. Binary Numbers
  105. 6.4. Octal Numbers
  106. 6.5. Decimal Numbers
  107. 6.6. Strip Leading Zeros
  108. 6.7. Numbers Within a Certain Range
  109. 6.8. Hexadecimal Numbers Within a Certain Range
  110. 6.9. Integer Numbers with Separators
  111. 6.10. Floating-Point Numbers
  112. 6.11. Numbers with Thousand Separators
  113. 6.12. Add Thousand Separators to Numbers
  114. 6.13. Roman Numerals
  115. 7. Source Code and Log Files
  116. Keywords
  117. Identifiers
  118. Numeric Constants
  119. Operators
  120. Single-Line Comments
  121. Multiline Comments
  122. All Comments
  123. Strings
  124. Strings with Escapes
  125. Regex Literals
  126. Here Documents
  127. Common Log Format
  128. Combined Log Format
  129. Broken Links Reported in Web Logs
  130. 8. URLs, Paths, and Internet Addresses
  131. 8.1. Validating URLs
  132. 8.2. Finding URLs Within Full Text
  133. 8.3. Finding Quoted URLs in Full Text
  134. 8.4. Finding URLs with Parentheses in Full Text
  135. 8.5. Turn URLs into Links
  136. 8.6. Validating URNs
  137. 8.7. Validating Generic URLs
  138. 8.8. Extracting the Scheme from a URL
  139. 8.9. Extracting the User from a URL
  140. 8.10. Extracting the Host from a URL
  141. 8.11. Extracting the Port from a URL
  142. 8.12. Extracting the Path from a URL
  143. 8.13. Extracting the Query from a URL
  144. 8.14. Extracting the Fragment from a URL
  145. 8.15. Validating Domain Names
  146. 8.16. Matching IPv4 Addresses
  147. 8.17. Matching IPv6 Addresses
  148. 8.18. Validate Windows Paths
  149. 8.19. Split Windows Paths into Their Parts
  150. 8.20. Extract the Drive Letter from a Windows Path
  151. 8.21. Extract the Server and Share from a UNC Path
  152. 8.22. Extract the Folder from a Windows Path
  153. 8.23. Extract the Filename from a Windows Path
  154. 8.24. Extract the File Extension from a Windows Path
  155. 8.25. Strip Invalid Characters from Filenames
  156. 9. Markup and Data Formats
  157. Processing Markup and Data Formats with Regular Expressions
  158. 9.1. Find XML-Style Tags
  159. 9.2. Replace Tags with
  160. 9.3. Remove All XML-Style Tags Except and
  161. 9.4. Match XML Names
  162. 9.5. Convert Plain Text to HTML by Adding

    and
    Tags

  163. 9.6. Decode XML Entities
  164. 9.7. Find a Specific Attribute in XML-Style Tags
  165. 9.8. Add a cellspacing Attribute to Tags That Do Not Already Include It
  166. 9.9. Remove XML-Style Comments
  167. 9.10. Find Words Within XML-Style Comments
  168. 9.11. Change the Delimiter Used in CSV Files
  169. 9.12. Extract CSV Fields from a Specific Column
  170. 9.13. Match INI Section Headers
  171. 9.14. Match INI Section Blocks
  172. 9.15. Match INI Name-Value Pairs
  173. Index
  174. Index
  175. Index
  176. Index
  177. Index
  178. Index
  179. Index
  180. Index
  181. Index
  182. Index
  183. Index
  184. Index
  185. Index
  186. Index
  187. Index
  188. Index
  189. Index
  190. Index
  191. Index
  192. Index
  193. Index
  194. Index
  195. Index
  196. Index
  197. Index
  198. Index
  199. About the Authors
  200. Colophon
  201. Copyright
  202. 9.6. Decode XML Entities

    Problem

    You want to convert all character entities defined by the XML standard to their corresponding literal characters. The conversion should handle named character references (such as &, <, and ") as well as numeric character references (be they in decimal notation as Σ or Σ, or in hexadecimal notation as Σ, Σ, or Σ).

    Solution

    Regular expression

    &(?:#([0-9]+)|#x([0-9a-fA-F]+)|([0-9a-zA-Z]+));
    Regex options: None
    Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby

    This regular expression includes three capturing groups. Only one of the groups participate in any particular match and capture a value. Using three groups like this allows you to easily check which type of entity was matched.

Replace matches with their corresponding literal characters

Use the regular expression just shown, together with the code in Recipe 3.16. The code examples listed there show how to perform a search-and-replace with replacement text generated in code.

When writing your replacement callback function, use backreferences to determine the appropriate replacement character. If group 1 captured a value, backreference 1 holds a numeric character reference in decimal notation, possibly with leading zeros. If group 2 captured a value, backreference 2 holds a numeric character reference in hexadecimal notation, possibly with leading zeros. If group 3 captured a value, backreference 3 holds an entity name. Use a lookup object, dictionary, hash, or whatever data structure is most convenient to map entity names to their corresponding characters by value or character code. You can then quickly identify which character to use as your replacement text.

The next section uses JavaScript to demonstrate how this all ties together.

Example JavaScript solution

// Accepts the match ($0) and backreferences; returns replacement text
function callback($0, $1, $2, $3) {
    var charCode;

    // Name lookup object that maps to decimal character codes
    // Equivalent hexadecimal numbers are listed in comments
    var names = {
        quot: 34, // 0x22
        amp: 38, // 0x26
        apos: 39, // 0x27
        lt: 60, // 0x3C
        gt: 62 // 0x3E
    };

    // Decimal character reference
    if ($1) {
        charCode = parseInt($1, 10);
    // Hexadecimal character reference
    } else if ($2) {
        charCode = parseInt($2, 16);
    // Named entity with a lookup mapping
    } else if ($3 && ($3 in names)) {
        charCode = names[$3];
    // Invalid or unknown entity name
    } else {
        return $0; // Return the match unaltered
    }

    // Return a literal character
    return String.fromCharCode(charCode);
}

// Replace all entities with literal text
subject = subject.replace(
        /&(?:#([0-9]+)|#x([0-9a-fA-F]+)|([0-9a-zA-Z]+));/g,
        callback);

Discussion

The regular expression and example code we’ve shown in this recipe are intended for decoding snippets of XML-style text, rather than entire XML documents. The regex here can be useful when converting XML or (X)HTML content to plain text, but keep in mind that no restrictions are placed on where named or numbered entities can occur within the subject text. For instance, there is no special handling for skipping entities in XML CDATA blocks or HTML script blocks.

The JavaScript example code converts both decimal and hexadecimal numeric references to their corresponding literal characters, and additionally converts the five named entities that are defined in the XML standard: &quot; (“), &amp; (&), &apos; ('), &lt; (<), and &gt; (>). HTML includes many more named entities that aren’t covered here.[22] If you follow the approach used in the example code, however, it should be straightforward to add as many more entity names as you need.

The JavaScript example code converts the following subject string:

"&lt; &bogus; dec &#65;&#0065; &amp;lt; hex &#x41;&#x041; &gt;"

To this:

"< &bogus; dec AA &lt; hex AA >"

JavaScript doesn’t support Unicode code points beyond U+FFFF, so the provided code (or more specifically, the String.fromCharCode() method used within it) works correctly only with numeric character references up to &#xFFFF; hexadecimal and &#65535; decimal. This shouldn’t be a problem in most cases, since characters beyond this range are rare. Numeric character references with numbers above this range are invalid in the first edition of the XML 1.0 standard.

Tip

Some programming languages and XML APIs have built-in functions to perform XML or HTML entity decoding. For instance, in PHP 4.3 and later you can use the function html_entity_decode(). It might still be helpful to implement your own method since such functions vary in which entity names they recognize. In some cases, such as with Ruby’s CGI::unescapeHTML(), even fewer than the standard five XML named entities are recognized.

See Also

Recipe 9.5 explains how to convert plain text to HTML by adding <p> and <br> tags. The first step in the process is HTML-encoding &, <, and > characters using named entities.

Techniques used in the regular expressions in this recipe are discussed in Chapter 2. Recipe 2.3 explains character classes. Recipe 2.9 explains grouping. Recipe 2.12 explains repetition.



[22] HTML 4.01 defines 252 named entities. HTML5 has more than 2,000.