Discover @gnarlymedia Open Source projects

Simon Hudson (@gnarlymedia)

gnarlymedia

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Global Rank 438,984 (Top 16 %)
Followers 18
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Registered over 11 years ago
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26.7 %

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20.0 %

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13.3 %

HTML
13.3 %

Shell
6.7 %

Puppet
6.7 %

JavaScript
6.7 %

PHP
6.7 %
Location 🇦🇺 Australia
Country Total Rank 6,195
Country Ranking

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CSS
492

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laravel-website-with-store-locator

A complete Laravel website with store-locator

install-bot

An install script for node and composer projects

roadskin

A highly-customised WordPress child theme design

Monte-Carlo-study-of-the-2D-Ising-model

This project deals with the thermodynamics of the ferromagnetic Ising Model - a 2D array of spins with simple nearest neighbour interactions.

sillysleeves-wordpress-theme

Child theme for Wordpress (built on WooThemes Canvas)

simonhudson-wordpress-site

Website of Simon Hudson Band based on WordPress child theme

sillysleeves-design-your-own

Draw images in the browser and save to your computer or device

responsive-hand-coded-website

A complete hand-coded responsive website design

CLionScientificPlotting

A basic setup with CMakeLists.txt and Makefile for scientific computing on Ubuntu

Monte-Carlo-simulation-of-a-neutrino-beam

Monte Carlo methods are computational methods relying on repeated random sampling to obtain numerical results. Here we use them to look at a system which produces a neutrino beam through meson decay.

puppet

A framework for automated WordPress deployment

gnarlymedia-wordpress-site

Current website of GnarlyMedia based on WordPress child theme

Structure-of-white-dwarf-stars

White dwarf stars are incredibly dense objects composed of heavy nuclei and their electrons. This project finds the mass and radius of a white dwarf using two of the more important computational methods, Euler and Runge-Kutta.

Semiclassical-quantisation-of-molecular-vibrations

In this project we aim to find the energies of molecular vibration of the hydrogen molecule in the Bohr semi-classical approximation.

A-Fiery-Death-and-State-Based-Estimation

This project deals with the so-called Kalman filter, an algorithm for a recursive solution to the discrete- data linear filtering problem. Originally published in a 1960 paper by R.E. Kalman, the algorithm provides an efficient recursive computational means to estimate the state of a process, in a way that minimises the mean of the squared error (or variance).