Creating Mythic-Style Charts

image made by a human

filed under soloing and mechanics on 18 Sep 2016
tagged solo, mythic, python, tool, mod, and script

Inspired by the great Mythic-style charts on the Lone Wolf Roleplaying google+ community over the last few days, I wrote a simple script that uses nltk TextBlob and python to take a text file, sort it by word type, and output each type as a separate, numbered chart.

It’s not perfect; the resulting chart needs quite a bit of curation, but it’s a lot easier than doing it manually!

The Goal

Extremely brief research (I looked at the pdfs for a couple of seconds) and foggy grammar skills suggest that the Mythic pattern is “verb (or adjective) noun”. Location Crafter (as near as I can tell) uses “verb noun” for actions and “adverb adjective” for descriptions.

Verbs tend to be in the concrete, present tense (“coerce”, “demand”, “mend”). Subjects can be any noun. Adverbs tend to end in “ly” but you’ll probably need to convert some of the verbs manually to make up more than 50 or so suitable ones. And 100 elements seems pretty standard per list.

Using Seed Parser

If you have Python installed, it’s pretty straightforward to use the seedparser. First, install nltk. Install TextBlob, which will install what you need from nltk automatically. Open a terminal and type:

$ pip install -U textblob
$ python -m textblob.download_corpora

Source text files can be weighted lists (run through, say, wordclouds.com) or raw text. It doesn’t really matter as long as it is plain text, though older files or ones with a lot of underscores and odd formatting might choke the script. Very long files (over ~165K words) take a while though. Note that it ignores word order, so if weighting is important to you, remove any low ranked words from the source before running the script.

Run the script in the same directory as your word file; it will output a .csv file and a .py file for each of the four required parts of speech (noun, adjective, adverb, verb). The .py is in python dictionary/list format and the csv has a comma-separated, numbered chart that should be straightforward to import into Google spreadsheets or a similar program.

For standard 100 element lists:

python seedparser.py <filename>

Flags:

  • -x, –max, number, The max number of elements in each final list or chart. Should be 10% to 25% higher than your intended final total to give you spares to replace miscategorized or boring words. Default is 110.
  • -f, –fill, True or False, Keep trying until the list is at the max number or a maximum number of tries is exceeded. Default is True.
  • -c, –case, u[pper], l[ower], Set the initial letter of each element of each list to uppercase or lowercase. Defaults to lowercase.
  • -m, –min, number, Minimum length of words to include; default is 4, to eliminate things like “get”, “got”, “put”, “say”. If you lower this, increase the max elements substantially to compensate for the useless entries.
  • -l, –lemmatize, True or False, Convert verbs to base form, ie, “asked” to “ask”. Default is true.
  • -p, –proper, True or False, Include proper nouns or not. Default is false.
  • -t, –print, True or False, Print results to terminal. This makes it easy to copy and paste without having to open up the saved file. Default is False.
  • -s, –second, True or False, Makes a second pass through the parts of speech filter. Also removes any adverbs not ending in ‘ly’. Set to False if you’re coming up short in the adverbs.

If you don’t want to mess with this stuff, just do:

python seedparser.py <filename>

Finally, edit the resulting lists down, replacing any unsuitable words. You can import the csv into Google Spreadsheets (and likely other spreadsheet programs) as is or use the python code for further processing.

Here’s an example from the first chapter of the Three Musketeers, completely uncurated (I can see at least a few that need to be swapped out), and copy pasted into a csv to markdown converter (don’t forget to select “comma-separated” if you use it too).

                   
1 accent 2 actor 3 addition 4 adversaries 5 attentions
6 calm 7 challenge 8 civilization 9 claim 10 complaints
11 conscience 12 contain 13 countries 14 dealer 15 desire
16 director 17 displeasing 18 doubts 19 effect 20 eminence
21 eyebrows 22 feather 23 fiery 24 foils 25 glass
26 godsend 27 group 28 hairs 29 half 30 haughty
31 haunting 32 heard 33 heron 34 hope 35 hostler
36 inclination 37 influence 38 inquiring 39 insolence 40 interlocutors
41 intrigue 42 look 43 love 44 lunge 45 masters
46 modesty 47 morbleu 48 mouth 49 muscles 50 necessity
51 newcomer 52 ninny 53 number 54 occasions 55 officer
56 openings 57 ordeal 58 organizations 59 panics 60 passage
61 people 62 perspicacity 63 physiognomy 64 piece 65 pink
66 places 67 protection 68 province 69 provinces 70 reason
71 regiment 72 remaining 73 remorse 74 riot 75 rosemary
76 s’blood 77 search 78 snare 79 society 80 soldiers
81 spit 82 stature 83 sticks 84 swordsmanship 85 tender
86 thickness 87 things 88 tomorrow 89 tone 90 tones
91 tongue 92 valise 93 valor 94 visitor 95 waiter
96 waves 97 whence 98 wonder 99 world 100 wrist
Seed.py

You can also just use the included seed.py to generate seeds immediately, if you’d like. Drop it in the directory with your output files from running seedparser.py.

python seed.py <filename> -a verb -b noun

You can optionally specify an “-a” and a “-b” for which of the four parts of speech you want to grab. If they aren’t specified they default to “verb” and “noun” for a Mythic-style result.

And there you go. A very simple, probably way more work to write than it would have been to just make a damn list by hand, unitasker script for turning texts into event lists.