Martin Jung Martin-Jung

Git Submodules - Basic Explanation

Why submodules?

In Git you can add a submodule to a repository. This is basically a sub-repository embedded in your main repository. This can be very useful. A couple of usecases of submodules:

Separate big codebases into multiple repositories.

Package	Ordinal	Multinomial	Partial proportional odds	Scale Effects	Random Effects
nnet	no	`multinom`	N/A	N/A	No
MASS	`polr`	No	No	No	No
ordinal	`clm`/`clm2`	all X's in `nominal` (may not converge)	offending X's in `nominal`	via `scale`	`clmm`/`clmm2`
VGAM	yes	?	?	?	No
MCMCglmm	yes	?	?	?	?
Mixcat	yes	?	?		`npmlt`
mlogit	?	`mlogit`	N/A	N/

	library(LaCroixColoR)
	library(sf)
	library(fasterize)
	library(rayshader)
	library(raster)
	library(exactextractr)
	library(rayrender)

	# load raster into R
	elevation <- raster("Olympus_Mons_ortho-image.tif")

	library(shiny)
	xy <- c(784,479) #output of grDevices::dev.size("px")
	url <- "https://www.youtube.com/watch?v=Ef2jmf2vy00" #copy yt link here
	url <- gsub("watch\\?v=","embed/",url)
	ui <- fluidPage(
	HTML(paste0('<iframe width="',xy[1],'" height="',xy[2],'" src="',url,'" frameborder="0"></iframe>'))
	)
	server <- function(input, output, session) {
	}

	import numpy as np
	import pandas as pd

	def sklearn_tree_to_ee_string(estimator, feature_names):

	# extract out the information need to build the tree string
	n_nodes = estimator.tree_.node_count
	children_left = estimator.tree_.children_left
	children_right = estimator.tree_.children_right
	feature_idx = estimator.tree_.feature

	library(tidyverse)

	mod_full <- lm(mpg ~ cyl + disp + hp + drat + wt + qsec, data = mtcars)
	mod_g1 <- lm(mpg ~ cyl + disp + hp, data = mtcars)
	mod_g2 <- lm(mpg ~ drat + wt + qsec, data = mtcars)

	dat <- data.frame(full_r2 = summary(mod_full)$r.square,
	g1_r2 = summary(mod_g1)$r.square,
	g2_r2 = summary(mod_g2)$r.square) %>%
	mutate(g1_ind = full_r2 - g2_r2,

	library(stringr)
	library(spdep)
	library(rgdal)
	library(magrittr)
	library(ggplot2)
	library(sf)



	#======================================================

	<!DOCTYPE qgis_style>
	<qgis_style version="0">
	<symbols/>
	<colorramps>
	<colorramp type="gradient" name="Magma">
	<prop k="color1" v="0,0,3,255"/>
	<prop k="color2" v="251,252,191,255"/>
	<prop k="stops" v="0.04;4,4,21,255:0.08;14,10,42,255:0.12;26,16,65,255:0.16;40,17,89,255:0.20;57,15,110,255:0.23;74,16,121,255:0.27;90,21,126,255:0.31;105,28,128,255:0.35;121,34,129,255:0.39;137,40,129,255:0.43;153,45,127,255:0.47;169,50,124,255:0.51;185,55,120,255:0.55;202,62,114,255:0.59;217,70,106,255:0.62;230,81,98,255:0.66;240,96,93,255:0.70;247,113,91,255:0.74;251,132,96,255:0.78;253,151,104,255:0.82;254,170,116,255:0.86;254,188,130,255:0.90;253,207,146,255:0.94;253,225,163,255:0.98;252,243,181,255"/>
	</colorramp>
	<colorramp type="gradient" name="Inferno">

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	# Test of Significance, takes the same arguments as t.test() .
	signif.test <- function(x, ...) {
	p <- t.test(x, ...)$p.value

	# List of p excuses retrieved from http://mchankins.wordpress.com/2013/04/21/still-not-significant-2/
	p_excuses <- c(
	"(barely) not statistically significant <p>",
	"a barely detectable statistically significant difference <p>",
	"a borderline significant trend <p>",
	"a certain trend toward significance <p>",