Kyle Thomas wetlife

I holistically engineer solutions from minimally viable products through their organic growth to robustly satisfy evolving specifications.

wetlife / minimal-template.tex

Created June 18, 2019 06:09 — forked from Michael0x2a/minimal-template.tex

Minimal LaTeX template

	\documentclass{article}
	% General document formatting
	\usepackage[margin=0.7in]{geometry}
	\usepackage[parfill]{parskip}
	\usepackage[utf8]{inputenc}

	% Related to math
	\usepackage{amsmath,amssymb,amsfonts,amsthm}

	\begin{document}

wetlife / latex-mimimal-working-example-of-a-book

Last active October 21, 2018 00:24

LaTeX MWE book from Fran's answer at https://tex.stackexchange.com/questions/302222/strategy-for-writing-a-book-using-latex

	\documentclass[a4paper,twoside]{scrbook}
	\begin{document}
	\title{Computer programing}
	\author{nedlud}
	\frontmatter
	\maketitle
	\tableofcontents
	\mainmatter
	\chapter{Introduction}
	Some text ...

wetlife / report_hrefs.py

Created September 9, 2017 02:09

Find hrefs in file_path. Distinguish absolute hrefs by the presence of '://' then give counts of relative- and absolute-hrefs.

	from bs4 import BeautifulSoup
	from requests import request
	from pprint import pprint
	import re

	file_path = './index.html'
	with open(file_path, encoding='utf8') as file_object:
	file_markup = file_object.read()
	file_soup = bs(file_markup, 'lxml')

wetlife / sklearn-data-consumption.txt

Last active October 21, 2018 16:44 — forked from anonymous/gist:b6e4c0c2aa3b90f82c49af9e640a050a

MWE: ingest data via sklearn

	>>> from sklearn import preprocessing
	>>> lb = preprocessing.LabelBinarizer()
	>>> lb.fit_transform(['cat', 'dog', 'bird', 'cat'])
	array([[0, 1, 0],
	[0, 0, 1],
	[1, 0, 0],
	[0, 1, 0]])

wetlife / mandelplot.py

Created April 22, 2017 05:58

This is a plot of Mandelbrot's completely incomputable set.

	######################################################
	######## mandelplot: plot the mandelbrot set #########
	######################################################
	import numpy as np # load numerical python
	import matplotlib.pyplot as plt # load plotting tools

	bailout = 10**62 # bound on the number of iterations
	radial_pixels = 512 # resolution of plot
	sequence = [-2 + 4/radial_pixels*count for count in range(radial_pixels)]
	image = np.zeros((radial_pixels,radial_pixels))