richfitz

## Empty list -- just use the empty environment for this.
nil <- function() {
  emptyenv()
}

## Test if a list is the empty list:
is_empty <- function(lis) {
  identical(lis, nil())
}

yamaguchiyuto

import sys
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

def plot(data,filename,degreetype):
    """ Plot Distribution """
    plt.plot(range(len(data)),data,'bo')
    plt.yscale('log')
    plt.xscale('log')

tleonardi

tanimoto <- function(x, similarity=F) {
	res<-sapply(x, function(x1){
		sapply(x, function(x2) {i=length(which(x1 & x2)) / length(which(x1 | x2)); ifelse(is.na(i), 0, i)})
	})
	if(similarity==T) return(res)
	else return(1-res)
}

x <- data.frame(Samp1=c(0,0,0,1,1,1,0,0,1), Samp2=c(1,1,1,1,1,1,0,0,1), Samp3=c(1,0,0,1,0,0,0,0,0), Samp4=c(1,1,1,1,1,1,1,1,1))
tanimoto(x)

dcomtois

printname	c.entry	file	line_no
-	do_arith	R-4.0.3\src\main\arithmetic.c	411
!	do_logic	R-4.0.3\src\main\logic.c	40
!=	do_relop	R-4.0.3\src\main\relop.c	51
$	do_subset3	R-4.0.3\src\main\subset.c	1206
$<-	do_subassign3	R-4.0.3\src\main\subassign.c	2081
%%	do_arith	R-4.0.3\src\main\arithmetic.c	411
%*%	do_matprod	R-4.0.3\src\main\array.c	1228
%/%	do_arith	R-4.0.3\src\main\arithmetic.c	411
&	do_logic	R-4.0.3\src\main\logic.c	40

statguy

library(INLA)
m = 50
points = matrix(runif(m*2), m, 2)
mesh = inla.mesh.2d(loc=points,cutoff=0.05,offset=c(0.1, 0.4),max.edge=c(0.05, 0.5) )
bnd = inla.nonconvex.hull(points, convex=0.12)
mesh = inla.mesh.2d(boundary=bnd,cutoff=0.05,offset = c(1, 0.5),max.edge=c(0.1, 0.5) )
A = inla.spde.make.A(mesh, loc=points)
sigma0 = 1 ## Field std.dev. for theta=0
size = min(c(diff(range(mesh$loc[,1])), diff(range(mesh$loc[,2]))))
range0 = size/5 ## A fifth of the approximate domain width.

dpsanders

abstract Person

type Adult <: Person
    age::Int
    height::Float64
    position::Int
end

type Child <: Person
    age::Int

jeroen

# Find all CRAN package with FORTRAN code
packages <- c()
base_url <- 'https://api.github.com/search/repositories?q=user:cran%20language:fortran'
for(page in 1:100){
  url <- paste0(base_url, "&page=", page)
  cat("Reading", url, "\n")
  repos <- jsonlite::fromJSON(url)
  if(!length(repos$items)) 
    break
  packages <- c(packages, repos$items$name)

kyrcha

# function needed for visualization purposes
sigmoid = function(params, x) {
  params[1] / (1 + exp(-params[2] * (x - params[3])))
}

x = 1:53
y = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.18,0.18,0.18,0.33,0.33,0.33,0.33,0.41,
  0.41,0.41,0.41,0.41,0.41,0.5,0.5,0.5,0.5,0.68,0.58,0.58,0.68,0.83,0.83,0.83,
  0.74,0.74,0.74,0.83,0.83,0.9,0.9,0.9,1,1,1,1,1,1,1)

mick001

#Load library mass and set seed
library(MASS)
set.seed(100)

# We are going to use 3 random variables
m <- 3

# Number of samples to be drawn
n <- 2000

thomasp85

library(ggraph)
library(gganimate)
library(igraph)
# Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious
infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE)
infect$V3 <- NULL
names(infect) <- c('from', 'to', 'time')
infect$timebins <- as.numeric(cut(infect$time, breaks = 100))

# We want that nice fading effect so we need to add extra data for the trailing