Brodie Gaslam brodieG

[Isolating Differences Between Files][paper]

Advantage over Other Algorithms

The diff output is more specific:

[I]f a whole block of text is moved, then all of it, rather than just the beginning and end, is detected as changed.

>The algorithm described here avoids these difficulties. It detects differences that correspond very closely to our intuitive notion of difference.

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

Convert MOV to GIF using FFmpeg and ImageMagick

I tried a few different techniques to make a GIF via command-line and the following gives me the best control of quality and size. Once you're all setup, you'll be pumping out GIFs in no time!

Preparation

Install FFmpeg

$ brew install ffmpeg [all your options]
- Example: $ brew install ffmpeg --with-fdk-aac --with-ffplay --with-freetype --with-frei0r --with-libass --with-libvo-aacenc --with-libvorbis --with-libvpx --with-opencore-amr --with-openjpeg --with-opus --with-rtmpdump --with-schroedinger --with-speex --with-theora --with-tools

Install ImageMagick

Default ggplot2 aesthetics by geom.

geom	alpha	colour	fill	linetype	size
GeomAbline	NA	black		1	0.5
GeomAnnotationMap	NA	NA	grey20	1	0.5
GeomArea	NA	NA	grey20	1	0.5
GeomBar	NA	NA	grey35	1	0.

	library(data.table)
	library(ggplot2)
	data(volcano)
	volcano <- as.data.table(melt(volcano, varnames = c("x", "y"),
	value.name = "h"))


	volcano[, c("dx", "dy") := metR::Derivate(h ~ x + y)] # from my package. It calculates directional derivatives.
	volcano[, angle := atan2(-dy, -dx)]

	library(USAboundaries)
	library(sf)
	library(dplyr)
	library(elevatr)
	library(raster)

	# Get map of lower 48 states
	usa_l48 <- us_boundaries() %>%
	filter(state_abbr != "HI" &
	state_abbr != "AK" &

	library(bench) # assess speed and memory
	library(data.table) # data.table for all of its stuff
	library(dplyr) # compare it to data.table
	library(profmem) # assess the process of R functions

	set.seed(84322)

	# Example Data
	d <- data.table(
	grp = sample(c(1,2), size = 1e6, replace = TRUE) %>% factor,

	library(ggplot2)
	set.seed(1221)

	dat <- data.frame(x=rnorm(100), y=rnorm(100), type=rep(1:2, each=50))
	p <- ggplot(dat) + geom_point(aes(x, y)) +
	facet_wrap(~type) + coord_fixed() +
	theme(plot.background = element_rect(fill='red'))

	# inspired by
	# egg::set_panel_size