Graph

A Python package that provides bioinformatics utilities for analyzing omics datasets

PyIOmica (pyiomica) This repository contains PyIOmica, a Python package that provides bioinformatics utilities for analyzing (dynamic) omics datasets. PyIOmica extends MathIOmica usage to Python and implements new visualizations and computational tools for graph analyses. A. INSTALLATION Pre-Installation Requirements To install PyIOmica on any platform you need Python version 3.7 or higher Installation Instructions To install the current release from PyPI (Python Package Index) use pip: pip install pyiomica Alternatively, you can install directly from github using: pip install git+https://github.com/gmiaslab/pyiomica/ or […]

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Raytracer

A simple Raytracer written in pure Python

Sombra Sombra is simple Raytracer written in pure Python. It’s main purpose is to help understand how raytracing works with a clean code. If you are looking for an efficient Raytracer you should probably use something like C++. The equations used are taken from the Image Synthesis class at Texas A&M University taught by professor Ergun Akleman. The main program creates a raytraced image of a scene with simple objects like spheres and planes. It’s using a resolution of 280x192px […]

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GUI

A GUI for identifying visages in an image

Visage Differentiation Visage Differentiation is a GUI application for outlining and labeling the visages in an image. The main functionality is provided by the tkinter and face-recognition Python modules. Images Usage Select a path to a directory containing images of known visages to be used in identifying unknown visages. The filenames are used as labels for identified visages. Select a path to an image containing visages to identify. Click the “Identify Visages” button. GitHub https://github.com/grantranda/visage-differentiation    

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Algorithms

Algorithm for Cutting Stock Problem using Google OR-Tools

Cutting Stock Problem Cutting Stock Problem (CSP) deals with planning the cutting of items (rods / sheets) from given stock items (which are usually of fixed size). Quick Usage This is how CSP Tools looks in action. Click CSP Tool to use it Libraries Quick Start Install Pipenv, if not already installed $ pip3 install –user pipenv Clone this project and install packages $ git clone https://github.com/emadehsan/csp $ cd csp $ pipenv install # activate env $ pipenv shell Run […]

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Python

Python’s filter(): Extract Values From Iterables

Python’s filter() is a built-in function that allows you to process an iterable and extract those items that satisfy a given condition. This process is commonly known as a filtering operation. With filter(), you can apply a filtering function to an iterable and produce a new iterable with the items that satisfy the condition at hand. In Python, filter() is one of the tools you can use for functional programming. In this tutorial, you’ll learn how to: Use Python’s filter() […]

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Command-line Tools

A simple CLI tool for tracking Pikud Ha’oref alarms

Pikud Ha’oref Alarm Tracking Polls the unofficial API endpoint every second for incoming alarms. Prints active alarms as they occur. Prints routine messages once every 5 minutes by default. Install Pip The easiest way to install is from PyPI with pip: $ pip install alarmpy You can then run the alarmpy executable directly: $ alarmpy –help Pipenv For development usage it’s recommended to clone the git repo and use pipenv: $ git clone https://github.com/yuvadm/alarmpy $ cd alarmpy $ pipenv sync […]

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Framework

A probabilistic gradient boosting framework in Python

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch, developed by Airlab in Amsterdam. It provides the following advantages over existing frameworks: Probabilistic regression estimates instead of only point estimates. Auto-differentiation of custom loss functions. Native GPU-acceleration. It is aimed at users interested in solving large-scale tabular probabilistic regression problems, such as probabilistic time series forecasting. For more details, read our paper or check out the examples. Installation Run pip install pgbm […]

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Deep Learning

A deep learning framwork dedicated to side-channel attacks

SCAAML SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x. Available compoments scaaml/: The SCAAML framework code. Its used by the various tools. scaaml_intro/: A Hacker Guide To Deep Learning Based Side Channel Attacks.Code, dataset and models used in our step by step tutorial on how to use deep-learning to perform AES side-channel attacks in practice. Install Dependencies To use […]

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Machine Learning

Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning

Openspeech Openspeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recognition. We aim to make ASR technology easier to use for everyone. Openspeech is backed by the two powerful libraries — PyTorch-Lightning and Hydra.Various features are available in the above two libraries, including Multi-GPU and TPU training, Mixed-precision, and hierarchical configuration management. Get Started We use Hydra to control all the training configurations.If you are not familiar with Hydra we […]

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