Maksim Terpilovskii maximtrp

Many tools do not fully remove metadata, but just remove the link with in the metadata table. The data are thus still available in the PDF file itself.

While a lot of people rely on Exiftool to remove metadata, it actually does the same in PDFs. If you remove metadata with exiftool -all= some.pdf, you can always restore the data with exiftool -pdf-update:all= some.pdf.

There are several options to remove PDF metadata safely:

Option 1 : Exiftool with qpdf

Remove metadata with exiftool : exiftool -all= some.pdf
Then remove ununsed objects with qpdf : qpdf --linearize some.pdf - > some.cleaned.pdf

Random, unique Wallpaper for every tag in awesome wm

Awesome version >= 3.5

About

This is a way to get a random and unique wallpaper for every tag (or rather the last selected tab, since multiple tags can be selected) in the awesome window manager. The wallpapers are selected randomly from a provided path (which should only contain suitable pictures) and the algorithm guarantees a unique wallpaper for every tag.

Where to place

In the config file for awesome wm, which should be located at

	from pyglui import ui

	import file_methods as fm
	import player_methods as pm
	from gaze_producer.gaze_producer_base import GazeProducerBase
	from methods import denormalize
	from player_methods import transparent_circle


	class GazeFromRecordingWithOffsetCorrection(GazeProducerBase):

	"""
	Bi-Term Topic Model (BTM) for very short texts.

	Literature Reference:
	Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng:
	"A biterm topic model for short texts"
	In Proceedings of WWW '13, Rio de Janeiro, Brazil, pp. 1445-1456.
	ACM, DOI: https://doi.org/10.1145/2488388.2488514

	This module requires pre-processing of textual data,


	import math
	import pyjags
	import numpy as np
	import pandas as pd

	np.random.seed(0)
	np.set_printoptions(precision=3)

	def pyjags_trace():

	import numpy as np
	from scipy import stats
	from itertools import combinations
	from statsmodels.stats.multitest import multipletests
	from statsmodels.stats.libqsturng import psturng
	import warnings


	def kw_nemenyi(groups, to_compare=None, alpha=0.05, method='tukey'):
	"""

	import numpy as np
	from scipy import stats
	from itertools import combinations
	from statsmodels.stats.multitest import multipletests
	from statsmodels.stats.libqsturng import psturng
	import warnings


	def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):
	"""