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airflow

Apache Airflow

algorithms-and-data-structures

C and C++ implementations of well known algorithms and data structures

auction

Auction system using Zookeeper

automato

Automate linux software and environmet setups

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

black

The uncompromising Python code formatter

byte-world

A completely text-based physics sandbox world

computer-graphics

Graduation projects in C++ and GLSL made with Qt and OpenGL

computer-networks

coverage-badge

Create badges/shields for your Python test coverage!

cryptography

docker-pyenv

Dockerfile with Automated Builds that has pyenv built in for easy versioning of Python! :)

drone-s3

Drone plugin for publishing files to Amazon S3

embeddings-network

Embeddings as spatial networks

europa

Sistema gerenciador de matrículas UFABC

extra-trees

Extremely Randomized Trees algorithm implementation in Python

fastai

The fastai deep learning library, plus lessons and tutorials

flasgger

Easy Swagger UI for your Flask API

image-similarity

Image Similarity Deep Ranking in Pytorch

injectable

Python Dependency Injection for Humans™

kavosh

Motifs and metamotifs discovering tool

lazy-init

Provide objects that only initializes when actually used

mining-exporter

Prometheus mining metrics exporter

mockutil

Useful extensions to the standard Python unittest.mock features

modular-algorithms

Modular Genetic and Modular Particle Swarm Optimization Algorithms

mongobloom

Bloom for Spring-Data Mongo

monodreams

Monogame Daydreams

motionless

motionless is a Python library that takes the pain out of generating Google Static Map URLs.

optimization-algorithms

Combinatorial optimization algorithms written in Python 3.4 for solving timetabling problem scenarios. In this work algorithms for solving NP-Complete combinatorial problems of timetabling were made based on literature, which describes the relevant methods created until now for solving these kind of problems. The intention of this project was to compare the main algorithms described in the literature on solving timetabling problems. The scenarios provided for the algorithms are generated by another algorithm, also created in this work. By knowledge limitations of the author the structured programming paradigm was used to make all the algorithms, because of that the scenario generation algorithm demanded a lot of more time than expected to code it, this is also because of the complexity of variables that this algorithm is capable of generating in order to be as close as possible to reality. Provided that, not all studied algorithms were created, in fact just 2 algorithms came to be realeased, which comprimised the main goal of this project: comparing the computational efficiency of the main algorithms described in literature. Although the work on the development of the scenario generation algorithm was successful. This work is product of the undergraduation research program PDPD (in portuguese, Pesquisando Desde o Primeiro Dia) performed by Rodrigo Martins de Oliveira, with guidance of Jesús Pascual Mena-Chalco, at Universidade Federal do ABC, which the author thanks for the financial assitance over the period from November 2013 to July 2014.

optimization-models

SCIP Optimization Models