pent Parser Tutorial

There is almost always more than one way to construct a pent Parser to capture a given dataset. Sometimes, if the data format is complex or contains irrelevant content interspersed with the data of interest, significant pre- or post-processing may be required. As well, it’s important to inspect your starting data carefully, often by loading it into a Python string, to be sure there aren’t, say, a bunch of unprintable characters floating around and fouling the regex matches.

This tutorial starts by describing the basic structure of the semantic components of pent’s parsing model: tokens, patterns, and Parsers. It then lays out some approaches to constructing Parsers for realistic datasets, with the goal of enabling new users to get quickly up to speed building their own Parsers.

For a formal description of the grammar of the tokens used herein, see the pent Mini-Language Grammar.

• Basic Usage
  • Tokens
  • Patterns
  • Parsers
• Examples
  • Capturing with a Single Parser
  • Capturing with Nested Parsers
  • The Misc Token
  • *Post-Processing of Captured Data
  • *Internal Spaces in One-Or-More Matches
  • The Optional-Line Token
  • Required/Optional/Prohibited Trailing Whitespace
  • *’Any’ Tokens at EOL
  • *Pre-Processing/Data Cleanup Example
  • *Examples of Parser-Generated Regex