emhart

library(lme4)
library(ggplot2)

#create some levels
levs <- as.factor(c("l1","l2","l3","l4","l5"))

#set the factor means
f_means <- c(6,16,2,10,13)

# set individual as a factor

emhart

library(shiny)
library(ggplot2)
library(rgbif)

## Set up server output

shinyServer(function(input, output) {
# define function for server plot output
output$gbifplot <- reactivePlot(function() {
splist <- input$spec

emhart

'''
import csv
open("test.csv","w").close()
dat = ['one potato','two potato']
dat2 = ['3 potato','four']
dat3 = ['8 potatos','9 potatoes']
mydat = [dat,dat2,dat3]
with open("test.csv","a") as csvfile:
  mywriter = csv.writer(csvfile, delimiter=",")
	for x in mydat:

emhart

theme.blank = function(size=12) { 
  theme(
    #axis.line=element_blank(), 
    axis.text.x=element_text(size=size), 
    axis.text.y=element_text(size=size), 
    #axis.ticks.y=element_text(size=size),
    #   axis.ticks=element_blank(),
    axis.ticks.length=unit(0.1, "lines"), 
    axis.ticks.margin=unit(0.5, "lines"), 
    axis.title.x=element_text(size=size*1), 

emhart

library(ggplot2)
# n is the number of iterations for the test
# j is the number of matrices to create
# m is the size of the square matrix to solve
loops <- function(n,j,m){
  for(i in 1:n){
    mylist <- list()
    for(x in 1:j){mylist[[x]]<- matrix(rnorm(m^2),nrow=m,ncol=m)}
     
    for(x in 1:j) {z <- solve(mylist[[x]])}

emhart

#' Description: Code using deSolve for some simple ecological ODE models
#' There are 3 examples, one is a simple LV Pred-Prey example with plots
#' The next is a Macarthur-Wilson model with plots of both the species curve and the equlibrium points
#' The last plot is a quick crack at putting stochasticity into the model by adding oscillations in I with t.
#' LV code is modified from a blog post here: http://assemblingnetwork.wordpress.com/2013/01/31/two-species-predator-prey-systems-with-r/
#' Other additions are my own
#' 
#' Author: Edmund Hart
#' Date: 3/28/2013
#' E-mail: [email protected]

emhart

import random as rn
from ipythonblocks import BlockGrid
 
def draw_srpk_tri(dim = 100, iterations = 1000, background_color = (255,255,255), triangle_color = (0,0,0) ,block_size = 3):
    '''
    Draws a Sierpinski triangle using the chaos game approach (http://en.wikipedia.org/wiki/Chaos_game)
    
    Parameters:
        dim -- integer, the dimensions of the square to draw the triangle in, default is 100
        iterations -- integer, the number of points to create the triangle with, the higher the value, the more resolution, default is 1000

emhart

emhart

require(xtable)
insane <- data.frame(Name=c("Ampersand","Greater than","Less than","Underscore","Per cent","Dollar","Backslash","Hash", "Caret", "Tilde","Left brace","Right brace"), Character = I(c("&",">", "<", "_", "%", "$", "\\", "#", "^", "~","{","}")))
colnames(insane)[2] <- paste(insane[,2],collapse="")
print( xtable(insane),type='html',sanitize.text.function = function(x){x})

emhart

library(rWBclimate)
library(rgbif)

splist <- c("Junco hyemalis", "Aix sponsa")
out <- occurrencelist_many(splist, coordinatestatus = TRUE, maxresults = 20)
options(kmlpath = "/Users/edmundhart/kmltemp")
sp.map.df <- create_map_df(c("USA","MEX"))
sp.map <- ggplot(sp.map.df,aes(x=long,y=lat,group=group))+geom_polygon(fill="white",colour="black")+xlim(-130,-65)+ylim(12,50)
sp.map + geom_point(data=gbifdata(out),aes(y=decimalLatitude,x=decimalLongitude,group=taxonName,colour=taxonName))