python3
and scrapy
(pip install scrapy
)
scrapy runspider -o items.csv -a site="https://yoursite.org" 1spider.py
python3 2format_results.py
""" | |
FST (Full Steiner tree) generator Version 3 parser | |
Documentation: http://geosteiner.com/geosteiner-5.1-manual.pdf (page 186) | |
How to use: "./rand_points 10 | ./efst | ./bb -f | python3 fst2json.py" | |
To note, an alternate method is to grep for the " % @C XXX YYY" output from `bb`, | |
to get the Steiner points. (page 158 of the manual) | |
""" |
import os, glob | |
import diff_match_patch as dmp_module | |
dmp = dmp_module.diff_match_patch() | |
stashed = False | |
for file in glob.glob('textes_des_reglements/**/*.md'): | |
# (FR) commit à partir duquel les corrections ont été manuelles |
import mwpersistence | |
import deltas | |
import mwreverts | |
import sys | |
from pathlib import Path | |
import git |
[].slice.call(document.querySelectorAll('.replies-to .stream-items:first-child .ProfileTweet-action--favorite .ProfileTweet-actionButton .ProfileTweet-actionCountForPresentation')) | |
.map(x => parseInt(x.textContent)) | |
.filter(x=>x) | |
.sort((a, b) => b - a); | |
// to paste into the dev console when viewing a tweet replies | |
// output is the sorted list of number of likes for each reply |
from collections import Counter | |
users = [1, 2, 3, 4, 5, 6, 7, 8] | |
delegs = { | |
4: 1, | |
5: 2, | |
6: 3, | |
7: 5, | |
8: 5, |
import sys, os, collections, string, math | |
Result = collections.namedtuple('Result', | |
['file', 'matches', 'n_lines', | |
'n_exact_match', 'n_exact_length', | |
'title_match']) | |
def remove_accents(s): | |
table = collections.defaultdict(lambda: None) |
pays - 90 values, 4 distincts | |
---- | |
95.6% (86) FRANCE | |
2.2% (2) France | |
1.1% (1) ROYAUME-UNI | |
1.1% (1) | |
categorieOrganisation.label - 90 values, 7 distincts |
import sys | |
""" | |
X = WALL | |
P = Player | |
C = Box | |
E = Emplacement for Box | |
# = E + P | |
$ = E + C | |
""" |