Bringing sanity to world of messed-up data

Overview

Sanitize

Build Status Coverage Status Downloads Version Egg? Wheel? Format License

sanitize is a Python module for making sure various things (e.g. HTML) are safe to use. It was originally written by Mark Pilgrim and is distributed under the BSD license.

Usage

>>> from sanitize import HTML
>>> HTML('<b>hello')
'<b>hello</b>'
>>> HTML('<img>')
'<img />'
>>> HTML(("<b><b><b>hello")
... )
'<b><b><b>hello</b></b></b>'
>>> HTML('<img src="foo"/')
''
>>> HTML('<input type="checkbox" checked>')
'<input type="checkbox" checked="checked" />'
>>> # dangerous tags (a small sample)
... 
>>> HTML('safe<applet code="foo.class" codebase="http://example.com/"></applet> <b>description</b>')
'safe <b>description</b>'
>>> HTML('safe<frameset rows="*"><frame src="http://example.com/"></frameset> <b>description</b>')
'safe <b>description</b>'
>>> # bad protocols (a small sample)
>>> HTML('<a href="java' + chr(1) + 'script:foo">bar</a>')
'<a href="#foo">bar</a>'
>>> HTML('<a href="vbscript:foo">bar</a>')
'<a href="#foo">bar</a>'
>>> 

To see more usage examples see tests/test_sanitize_html.py.

Installation

python-sanitize is available on pypi

http://pypi.python.org/pypi/sanitize

So easily install it by pip:

pip install sanitize

Or by easy_install:

$ easy_install sanitize

Another way is by cloning python-sanitize's git repository

$ git clone git://github.com/Alir3z4/python-sanitize.git

Then install it by running

$ python setup.py install

Tests

To run unit tests:

$ python setup.py test

License

Sanitize is distributed under BSD license.

You might also like...
PyTorch CZSL framework containing GQA, the open-world setting, and the CGE and CompCos methods.
PyTorch CZSL framework containing GQA, the open-world setting, and the CGE and CompCos methods.

Compositional Zero-Shot Learning This is the official PyTorch code of the CVPR 2021 works Learning Graph Embeddings for Compositional Zero-shot Learni

PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021]

piglet PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021] This repo contains code and data for PIGLeT. If you like

The first dataset on shadow generation for the foreground object in real-world scenes.
The first dataset on shadow generation for the foreground object in real-world scenes.

Object-Shadow-Generation-Dataset-DESOBA Object Shadow Generation is to deal with the shadow inconsistency between the foreground object and the backgr

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

Open-World Entity Segmentation
Open-World Entity Segmentation

Open-World Entity Segmentation Project Website Lu Qi*, Jason Kuen*, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia This projec

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)

Code for HDR Video Reconstruction HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021) Guanying Chen, Cha

Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021
Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images This is the reference implementation of "Learning Generative Models of Texture

[CVPR2021] De-rendering the World's Revolutionary Artefacts

De-rendering the World's Revolutionary Artefacts Project Page | Video | Paper In CVPR 2021 Shangzhe Wu1,4, Ameesh Makadia4, Jiajun Wu2, Noah Snavely4,

Learning Open-World Object Proposals without Learning to Classify
Learning Open-World Object Proposals without Learning to Classify

Learning Open-World Object Proposals without Learning to Classify Pytorch implementation for "Learning Open-World Object Proposals without Learning to

Releases(2014.10.7)
  • 2014.10.7(Oct 7, 2014)

    Version 2014.10.7 - 2014-10-07


    • Feature: Add ChangeLog.rst file.
    • Feature: Add AUTHORS.rst file.
    • Feature: Add setup.cfg for wheel support.`
    • Feature #2: Add travis-ci testing.
    • Feature #4: Using unittest for testing.
    • Feature #7: Add coveralls support.
    • Feature #8: Add MANIFEST.in file.
    • Feature #5: Better Readme and documentation.
    • Feature #1: Python packaging done right.
    • Feature #9: Change version numbering.
    Source code(tar.gz)
    Source code(zip)
Owner
Alireza Savand
I am Alireza Savand, a Software Architect.
Alireza Savand
This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer.

MOTR: End-to-End Multiple-Object Tracking with TRansformer This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object

348 Jan 07, 2023
performing moving objects segmentation using image processing techniques with opencv and numpy

Moving Objects Segmentation On this project I tried to perform moving objects segmentation using background subtraction technique. the introduced meth

Mohamed Magdy 15 Dec 12, 2022
HuSpaCy: industrial-strength Hungarian natural language processing

HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti

HuSpaCy 120 Dec 14, 2022
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition

KIND (Kessler Italian Named-entities Dataset) KIND is an Italian dataset for Named-Entity Recognition. It contains more than one million tokens with t

Digital Humanities 5 Jun 21, 2022
BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

BitPack is a practical tool that can efficiently save quantized neural network models with mixed bitwidth.

Zhen Dong 36 Dec 02, 2022
Character Grounding and Re-Identification in Story of Videos and Text Descriptions

Character in Story Identification Network (CiSIN) This project hosts the code for our paper. Youngjae Yu, Jongseok Kim, Heeseung Yun, Jiwan Chung and

8 Dec 09, 2022
A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".

Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y

hoshi-hiyouga 85 Dec 26, 2022
Code release for NeRF (Neural Radiance Fields)

NeRF: Neural Radiance Fields Project Page | Video | Paper | Data Tensorflow implementation of optimizing a neural representation for a single scene an

6.5k Jan 01, 2023
Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.

Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu

THUDM 66 Dec 22, 2022
Neural network chess engine trained on Gary Kasparov's games.

Neural Chess It's not the best chess engine, but it is a chess engine. Proof of concept neural network chess engine (feed-forward multi-layer perceptr

3 Jun 22, 2022
This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

HollyLee 13 Dec 08, 2022
The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Deep Levelset for Box-supervised Instance Segmentation in Aerial Images Wentong Li, Yijie Chen, Wenyu Liu, Jianke Zhu* Any questions or discussions ar

sunshine.lwt 112 Jan 05, 2023
LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice,

LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and eval

Ahmet Erdem 691 Dec 23, 2022
This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

Dugh-NeurIPS-2021 This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroi

Ali Hashemi 5 Jul 12, 2022
DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures Dataset: https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polar

Ashish Salunkhe 37 Dec 17, 2022
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022.

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars This repository is the official implementation of Colar. In this work,

LeYang 246 Dec 13, 2022
Dynamic Capacity Networks using Tensorflow

Dynamic Capacity Networks using Tensorflow Dynamic Capacity Networks (DCN; http://arxiv.org/abs/1511.07838) implementation using Tensorflow. DCN reduc

Taeksoo Kim 8 Feb 23, 2021
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Code Release for Learning to Adapt to Evolving Domains

EAML Code release for "Learning to Adapt to Evolving Domains" (NeurIPS 2020) Prerequisites PyTorch = 0.4.0 (with suitable CUDA and CuDNN version) tor

23 Dec 07, 2022
Machine learning, in numpy

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

David Bourgin 11.6k Dec 30, 2022