todo-extract 0.2.5

Extract TODO items from the text file

TODO extractor from text file

Python script for extracting TODO notes from text file.

List can be grouped into sections and can be summarized with stats.

• Requirements
• Usage
• Examples
  • extraction of bare list, no stats
  • extraction of list summarized with stats
  • extraction of list divided into sections/chapters
• License

Installation

$ pip install todo-extract

Requirements

The script requires Python3 installed, no other dependencies.

Usage

$ todo-extract --help

usage: todo-extract [-h] [-s] [-c] file_name

Extract todo items from markdown file

positional arguments:
  file_name       markdown file path

optional arguments:
  -h, --help      show this help message and exit
  -s, --stats     display stats
  -c, --chapters  display items grouped by important/not important and done/not done

Examples

extraction of bare list, no stats

$ todo-extract file.md

output:

- buy a bag of chips
- buy a bag of cookies
- buy apples
- buy oranges
- buy bananas
- buy pears
- buy plums
- buy avocados
- buy water
- [!] buy bread

extraction of list summarized with stats

$ todo-extract --stats file.md

- buy a bag of chips
- buy a bag of cookies
- buy apples
- buy oranges
- buy bananas
- buy pears
- buy plums
- buy avocados
- buy water
- [!] buy bread

stats:
-------
10  all items (done or not done)
3   not done normal items   (42%  of all normal items are not done)
4   done normal items       (57%  of all normal items are done)
2   important items todo    (66%  of all important items are not done)
1   important items done    (33%  of all important items are done)

extraction of list divided into sections/chapters

$ todo-extract --chapters file.md

==== Done normal items: 4 ====

- buy oranges
- buy bananas
- buy pears
- buy plums

==== Important done items: 1 ====

- [!] buy bread

==== Normal todo items: 3 ====

- buy a bag of chips
- buy a bag of cookies
- buy apples

==== Important todo items: 2 ====

- buy avocados
- buy water

License

MIT © Krystian Safjan.