Spencer Ewall (spencerewall)

spencerewall

Geek Repo

Company:Amazon.com

Location:Seattle, Washington

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Spencer Ewall's repositories

GEOCARBON

A java translation of the most recent form of Bob Berner's GEOCARBSULFVOLC with added functionality for testing variations in input parameters along their expected deviations. This version itself is based on a fortran translation of the original model, which was created by Jeff Park (http://earth.geology.yale.edu/~jjpark/Code/gcsv10_export.f) and was used in the 2011 Park & Royer paper (GEOLOGIC CONSTRAINTS ON THE GLACIAL AMPLIFICATION OF PHANEROZOIC CLIMATE SENSITIVITY). The function of the model is to estimate fluctuations in atmospheric CO2 over geologic history, specifically the past 570 million years, based on a variety of geologic data. Despite what github says, this repo is coded entirely in java and mysql; no scala was involved.

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LearnObjC

Objective-C exercises for WesFOSS tutorial

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alfred-hidden-files

Alfred workflow for working with hidden files

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batch-updater

framework system for applying batch updates to a remote json store

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codeSamples

Various code samples taken from projects, coursework, etc

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dfhack

Memory hacking library for Dwarf Fortress and a set of tools that use it

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dpl-final

Final project for Comp321

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GodotFractals

A simple godot project to create fractal drawings. Initial version is a button with a pretty simple recursive method that executes, drawing more buttons when you click it

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homebrew

The missing package manager for OS X.

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javierscript

Javier Guión is the world's formost authority on spanish language programming. Also an amusing twitterbot.

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learn-react

spencer follows the official react tutorial

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plotman

Chia plotting manager

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Project-Euler

My solutions to projecteuler problems

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seti-syntax

A subtle dark colored theme for Atom.

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snap-in-synth

Inspired by littleBits (http://littlebits.cc/kits/synth-kit), the intention of this project is to create a modular synthesizer out of javascript

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tensorflow

Computation using data flow graphs for scalable machine learning

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