Bot

A simple configurable bot for sending arXiv article alert by mail

arXiv-newsletter A simple configurable bot for sending arXiv article alert by mail. Prerequisites PyYAML>=5.3.1 arxiv>=1.4.0 Configuration All configurations can be found in config.yml. Domains: specify search domains that articles belong to. Keywords: specify article queries and support boolean expression. Note that one query sends out one mail. Query config: specify query parameters, such as sort_by, sort_order. See here for detailed documentation. Mail: specify sender’s mail configuration. After setting up basic configurations, you may configure crontab schedule using crontab -e. A […]

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Maps

A Python module that allows the user to select all coordinate reference systems

ProjPicker ProjPicker (projection picker) is a Python module that allows the user to select all coordinate reference systems (CRSs) whose extent completely contains given points, polylines, polygons, and bounding boxes using set-theoretic logical operators in a postfix notation. The goal is to make it easy and visual to select a desired projection by location. This project was motivated by a GRASS GIS feature request. A new GRASS GIS module g.projpicker that wraps around this project is available. It is a […]

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Calculator

A Simple Calculator made with kivy framework in python

Kivy Calculator A Simple Calculator made with kivy framework.Works on all platforms from Windows/linux to android. Simple kivy project to help new kivy users build android apps with python. Getting Started Dependencies Python, kivy and buildozer. Installing pip install kivy pip install buildozer Executing program After installing all the necessary modules. open project directory in your IDE and run main.py or from your terminal and type: cd /../../project_files python3 main.py Packaging Your App for Android Create a new folder and […]

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Benchmark

Raster processing benchmarks for Python and R packages

Raster processing benchmarks This repository contains a collection of raster processing benchmarks for Python and R packages. The tests cover the most common operations such as loading data, extracting values by points, downsampling, calculating NDVI, writing multilayer, cropping by extent and calculating zonal statistics. The comparison is made from the user’s perspective (the simplest functions are used and the code is not optimized), so the results do not represent the best performance. The detailed results are available at https://kadyb.github.io/raster-benchmark/report.html. Reproduction […]

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Monitoring

Datapane makes it simple to build shareable reports from Python

datapane Turn a Python analysis into a beautiful document in 3 lines of code.Datapane is a Python library which makes it simple to build reports from the common objects in your data analysis, such as pandas DataFrames, plots from Python visualisation libraries, and Markdown. Reports can be exported as standalone HTML documents, with rich components which allow data to be explored and visualisations to be used interactively. You can also publish reports to our free public community platform or share […]

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pip-tools

A collection of small pip update helpers

pipdate pipdate is a collection of small pip update helpers. The command pipdate # or python3.9 -m pipdate updates all your pip-installed packages. (Only works on Unix.) There’s a Python interface as well that can be used for update notifications.This import pipdate pipdate.check(“matplotlib”, “0.4.5”) will print This can, for example, be used by package authors to notify users of upgrades oftheir own packages. If you guard the check with import pipdate if pipdate.needs_checking(“matplotlib”): print(pipdate.check(“matplotlib”, “0.4.5”), end=””) then it will be […]

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Tool

A collection of python written hacking tools consisting of network scanner

Python Hacking Tools (PyHTools) (pht) is a collection of python written hacking tools consisting of network scanner, arp spoofer and detector, dns spoofer, code injector, packet sniffer, network jammer, email sender, downloader, wireless password harvester credential harvester, keylogger, download&execute, and reverse_backdoor along with website login bruteforce, scraper, web spider etc. PHT also includes malwares which are undetectable by the antiviruses. The tools provided are for educational purposes only. The developers are no way responsible for misuse of information and tools […]

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Neural Network

Graph Neural Networks meet Personalized PageRank

APPNP A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood cannot be easily extended. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We […]

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Neural Network

A system for training neural networks to be provably robust and for proving that they are robust

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the 2018 ICML paper and the 2019 ArXiV Paper. Background By now, it is well known that otherwise working networks can be tricked by clever attacks. For example Goodfellow et al. demonstrated a network with high classification accuracy which classified one image of a panda correctly, and a seemingly identical attack pictureincorrectly. Many defenses […]

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Machine Learning

An Adversarial Framework for (non-) Parametric Image Stylization

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper “Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization” available at http://arxiv.org/abs/1811.09236. This code allows to generate image stylisation using an adversarial approach combining parametric and non-parametric elements. Tested to work on Ubuntu 16.04, Pytorch 0.4, Python 3.6. Nvidia GPU p100. It is recommended to have a GPU with 12, 16GB, or more of VRAM. Parameters Our method has many possible settings. You can specify them […]

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