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19
LICENSE
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19
LICENSE
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MIT License Copyright (c) 2020 xenofem <xenofematxenodotscience>
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is furnished
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to do so, subject to the following conditions:
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The above copyright notice and this permission notice (including the next
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paragraph) shall be included in all copies or substantial portions of the
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Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS
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OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF
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OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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25
README
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25
README
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# diceware
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This is the janky python code I used to help me generate the wordlist
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I describe at [https://xeno.science/dice](https://xeno.science/dice).
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This code is very bad and will probably crash if your terminal window
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is too small. Also I'm doing something wrong with ncurses, and after
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it loads it'll show you a blank screen until you press a key.
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## Dependencies
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You'll need Python 3 and NumPy.
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## Usage
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First, [download the English-language ConceptNet Numberbatch
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dataset](https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-en-19.08.txt.gz),
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and then run `pickle-vectors.py` to parse the Numberbatch vectors and
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store them in a pickle for later access. This takes a while but
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you'll only need to do it once.
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Then, run
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```bash
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diceware-editor.py [word list file]
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```
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214
diceware-editor.py
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214
diceware-editor.py
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#!/usr/bin/env python3
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import curses
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import curses.ascii
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import curses.textpad
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import numpy
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import numpy.linalg
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import os.path
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import pickle
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import random
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import sys
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import textwrap
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TARGET_LIST_LENGTH = 6**4
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MAX_WORD_LENGTH = 25
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SUGGESTION_DISTANCE_THRESHOLD = 1.2
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def vectorize(word):
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return word.replace(' ', '_').replace('-', '_')
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vectors = {}
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distance_cache = {}
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def memoized_distance(w1, w2):
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w1 = vectorize(w1)
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w2 = vectorize(w2)
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if w1 not in vectors or w2 not in vectors:
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return None
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v1 = min(w1, w2)
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v2 = max(w1, w2)
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if v1 not in distance_cache:
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distance_cache[v1] = {}
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if v2 not in distance_cache[v1]:
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distance_cache[v1][v2] = numpy.linalg.norm(vectors[v1] - vectors[v2])
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return distance_cache[v1][v2]
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def prefix(word):
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return ''.join(c for c in word.lower() if curses.ascii.islower(c))[:3]
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def main(stdscr):
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global vectors
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stdscr.clear()
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stdscr.leaveok(False)
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filename = sys.argv[1]
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list_pad = curses.newpad(TARGET_LIST_LENGTH*2, MAX_WORD_LENGTH)
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status_line = curses.newwin(1, curses.COLS, curses.LINES - 1, 0)
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info_box = curses.newwin(curses.LINES - 1, MAX_WORD_LENGTH*2, 0, MAX_WORD_LENGTH)
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loading_message = "Loading conceptnet numberbatch vectors..."
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status_line.addstr(0, 0, loading_message)
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with open('numberbatch.pkl', 'rb') as f:
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vectors = pickle.load(f)
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status_line.clear()
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status_line.addstr(0, 0, "Vectors loaded!")
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status_line.refresh()
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words = {}
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if os.path.isfile(filename):
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with open(filename) as f:
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for line in f:
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word = line.strip().split(maxsplit=1)[-1]
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if len(prefix(word)) < 3:
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continue
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words[prefix(word)] = word
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pairs = {(w1, w2) for w1 in words.values() for w2 in words.values() if w2 > w1}
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loading_message = "Precomputing word vector distances: "
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status_line.clear()
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status_line.addstr(0, 0, loading_message)
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for (i, (w1, w2)) in enumerate(pairs):
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if i % 1000 == 0:
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status_line.addstr(0, len(loading_message), "{}/{} pairs".format(i, len(pairs)))
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status_line.refresh()
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memoized_distance(w1, w2)
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suggestion_candidates = list(vectors.keys())
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random.shuffle(suggestion_candidates)
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suggestion = ""
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pos = 0
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unsaved = False
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while True:
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status_line.clear()
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status_line.addstr(0, 0, "[a]dd/[d]elete/[j]down/[k]up/[/]search/[s]uggest/[w]write/[q]uit{}".format('*' if unsaved else ''))
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status_line.noutrefresh()
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list_pad.clear()
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for (i, w) in enumerate(sorted(words.values())):
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dice = "{}{}{}{}".format(i // 6**3 + 1, (i // 6**2)% 6 + 1, (i // 6) % 6 + 1, i % 6 + 1)
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entry = dice + " " + w
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list_pad.addstr(i, 0, entry)
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scroll_pos = max(0, min(len(words)-(curses.LINES-1), pos - curses.LINES//2))
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list_pad.noutrefresh(scroll_pos,0, 0,0, curses.LINES-2,MAX_WORD_LENGTH-1)
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unknown_cn_words = {w for w in words.values() if vectorize(w) not in vectors}
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worst_distances = []
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for w1 in words.values():
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for w2 in words.values():
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if w1 >= w2:
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continue
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d = memoized_distance(w1, w2)
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if d is not None and (len(worst_distances) == 0 or d < worst_distances[-1][2]):
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worst_distances.append((w1, w2, d))
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worst_distances.sort(key=lambda x: x[2])
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worst_distances = worst_distances[:8]
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current_word = None if len(words) == 0 else list(sorted(words.values()))[pos]
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worst_current_distances = []
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for w in words.values():
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if w == current_word:
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continue
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d = memoized_distance(current_word, w)
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if d is not None and (len(worst_current_distances) == 0 or d < worst_current_distances[-1][1]):
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worst_current_distances.append((w, d))
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worst_current_distances.sort(key=lambda x: x[1])
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worst_current_distances = worst_current_distances[:8]
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info_box.clear()
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info_box.addstr(0, 0, """{count}/{target} words;
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Worst overall distances:
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{worst}
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Worst distances from current word:
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{worstc}
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Unknown (to ConceptNet) words:
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{unk_c}
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Suggestion:
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{sug}"""
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.format(
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count=len(words),
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target=TARGET_LIST_LENGTH,
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worst='\n'.join(' {} to {}, {:.2}'.format(*x) for x in worst_distances),
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worstc='\n'.join(' {}, {:.2}'.format(*x) for x in worst_current_distances),
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unk_c='\n '.join(unknown_cn_words if len(unknown_cn_words) <= 3 else list(unknown_cn_words)[:2] + ["..."]),
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sug=suggestion
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)
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)
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info_box.noutrefresh()
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curses.doupdate()
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stdscr.move(pos - scroll_pos, 0)
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ch = stdscr.getch()
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if ch == ord('-') or ch == ord('d'):
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if current_word:
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del words[prefix(current_word)]
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pos = min(max(0, len(words)-1), pos)
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unsaved = True
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elif ch == ord('+') or ch == ord('a'):
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status_line.clear()
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status_line.refresh()
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input_box = curses.textpad.Textbox(status_line)
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input_box.edit()
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word = input_box.gather().strip()
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if len(prefix(word)) >= 3:
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old = words.get(prefix(word), None)
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if old:
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status_line.clear()
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status_line.addstr(0,0, "Replace {}? [y/n]".format(old))
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status_line.refresh()
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if not old or stdscr.getch() == ord('y'):
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words[prefix(word)] = word
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pos = sorted(words.values()).index(word)
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unsaved = True
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elif ch == curses.KEY_DOWN or ch == ord('j'):
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pos = min(max(0, len(words)-1), pos+1)
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elif ch == curses.KEY_UP or ch == ord('k'):
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pos = max(0, pos-1)
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elif ch == ord('/'):
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status_line.clear()
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status_line.refresh()
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input_box = curses.textpad.Textbox(status_line)
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input_box.edit()
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word = input_box.gather()
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word = ''.join(c for c in word.lower() if curses.ascii.islower(c))
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if len(prefix(word)) >= 3 and prefix(word) in words:
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pos = sorted(words.values()).index(words[prefix(word)])
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elif ch == ord('s'):
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while True:
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candidate = suggestion_candidates.pop()
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if len(prefix(candidate)) < 3 or prefix(candidate) in words:
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continue
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min_dist = None
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for word in words.values():
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d = memoized_distance(word, candidate)
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if d is not None and (min_dist is None or d < min_dist):
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min_dist = d
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if min_dist is None or min_dist > SUGGESTION_DISTANCE_THRESHOLD:
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suggestion = candidate
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break
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elif ch == ord('w'):
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with open(filename, "w") as f:
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for (i, w) in enumerate(sorted(words.values())):
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dice = "{}{}{}{}".format(i // 6**3 + 1, (i // 6**2)% 6 + 1, (i // 6) % 6 + 1, i % 6 + 1)
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entry = dice + " " + w
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f.write(entry + "\n")
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unsaved = False
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elif ch == ord('q'):
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if unsaved:
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status_line.clear()
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status_line.addstr(0,0, "Quit with unsaved data? [y/n]")
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status_line.refresh()
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if not unsaved or stdscr.getch() == ord('y'):
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return
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if len(sys.argv) != 2:
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print("usage: {} <filename>".format(sys.argv[0]), file=sys.stderr)
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exit(1)
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curses.wrapper(main)
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17
pickle-vectors.py
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17
pickle-vectors.py
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#!/usr/bin/env python3
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import gzip
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import numpy
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import pickle
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vectors = {}
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with gzip.open("numberbatch-en-19.08.txt.gz", mode="rt") as f:
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count = int(next(f).split()[0])
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for (i, line) in enumerate(f):
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if i % 1000 == 0:
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print("{}/{}".format(i, count))
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[word, *vec] = line.split()
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vectors[word] = numpy.array([float(x) for x in vec])
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with open('numberbatch.pkl', 'wb') as f:
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pickle.dump(vectors, f)
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