wordsworth 0.1.6

Wordsworth CW

George Allison K1IG described a methodology "head copy" morse code. High-speed operators who copy in their heads at a speed greater than 40 WPM have learned to process CW by hearing entire words.

The Wordsworth method is a variant of the Farnsworth method, which sends individual letters at high speed. Wordsworth's method sends words at your target speed with long spacing between each word. You reduce the number of spaces as your proficiency increases.

For more information, you can read George's article, published on the QST magazine on his method.

You can also watch George's presentation at QSO Today

Installation

You can install this program from the source located on my github account. If you already have your Python environment set up, the easiest way is by using pip with the following command:

$ pip install wordsworth

If you set the environment variable CALL_SIGN with your call sign, wordworth will use it in the sequences of words generated. Set that variable permanently to your .bashrc or .zshrc file depending on the type of shell you are using.

$ export CALL_SIGN=W6BSD

Usage

If you are running this program on macOS, it will automatically copy the sequence of words into your clipboard buffer. You simply need to paste it into fldigi.

Example:

$ wordworth --repeat 4 --spaces 10 --dataset abbrevs

It is possible to run this program as an fldigi macro. Every time you click on the macro. The CW exercise will automatically appear in your fldigi transmit window.

Example of fldigi macro:

<TX>
<EXEC>/usr/local/bin/wordworth --repeat 4 --spaces 5</EXEC>
<RX>

Datasets

The datasets are:

• "abbrevs" abbreviations used in CW
• "alpha" alphabet [A-Z]
• "combination" most Frequent letters combinations
• "connectives" connective words
• "names" common first names
• "numbers" numbers from 0 - 99
• "pro_codes" ham radio pro-codes , , , , etc
• "punctuation" all the punctuation used in Morse
• "words" most common words

To use a specific dataset use the argument --dataset followed by the names of the dataset you want to learn.

Example

$ wordworth --nb-words 50 --repeat 3 --dataset alpha numbers abbrevs

In this example the program will chose 50 words from the 3 datasets alpha, numbers, abbrevs. Each word will be repeated 3 times.