Projects in Awesome Lists by ArangoGutierrez

This is a short repo to experiment with Kubernetes (Using minikube) on Fedora. This is just an automated way to install and set up a Minikube following the instructions on the MiniKube oficial repo

kubernetes minikube

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/dev-artifacts

Personal vault for development artifacts

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/arch-dummy

A dummy server that retrieves basic info from the host

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/grive

A Container implementation for the Google Drive Client

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/vagrant-devboxes

Vagrantfiles and common scripts to quickly setup VMs for Singularity test purposes under multiple distributions

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/k-foundry

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/pi

Calculate Pi with montecarlo

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/relaxacionmethod

relaxation methods are iterative methods for solving systems of equations, including nonlinear systems

cellular-automata climate iot temperature

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/goreduce

A map reduce example made in pure Go

data-science golang mapreduce twitter twitter-bot

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/metropolis

Metropolis–Hastings algorithm In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (e.g., to generate a histogram), or to compute an integral (such as an expected value). Metropolis–Hastings and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, other methods are usually available (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and are free from the problem of autocorrelated samples that is inherent in MCMC methods.

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/handsoncontainers

Hands on Linux Containers

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/dockertest

Quick experiment in Go to pull and run docker containers

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/lolcow-def

lolcow singularity definition file

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/kwant_container

Container def files for Kwant

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/arangogutierrez

Config files for my GitHub personal profile.

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/miscellaneous

Here you'll see some Miscellaneous code I'm using into my current work. Just have fun ;)

Last synced: 29 Oct 2024

https://github.com/arangogutierrez/streeter-phelps

The Streeter–Phelps equation is used in the study of water pollution as a water quality modelling tool. The model describes how dissolved oxygen (DO) decreases in a river or stream along a certain distance by degradation of biochemical oxygen demand (BOD). The equation was derived by H. W. Streeter, a sanitary engineer, and Earle B. Phelps, a consultant for the U.S. Public Health Service, in 1925, based on field data from the Ohio River. The equation is also known as the DO sag equation.

Last synced: 29 Oct 2024

Ecosyste.ms: Awesome