emhart

###############Food web plotting function################
### Date: 12/13/2011
### Author: Edmund Hart (edmund.m.hart@gmail.com)
### Description: A function to create grahps of trophic networks using ggplot2
### Plots food webs in a circular graph
### requires ggplot2 
#########################################################
library(ggplot2)

########## support function create.xy

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: edmund.m.hart@gmail.com

emhart

library(ggplot2)

### Imagine a coin is flipped 100 times
### You get 60 heads
### How can you assess if the coin is fair or not?

# Set up the plot
binom_density <- dbinom(0:100,100,.5)
flips <- data.frame(flip_count = 0:100, density = binom_density)

emhart

num2letter = {"2":"abc", "3":"def", "4":"ghi", "5":"jkl", "6":"mno", 
                "7":"pqrs", "8":"tuv", "9":"wxyz"}


def keys2letters(key_presses):
  results = [x for x in num2letter[key_presses[0]]]
  if len(key_presses) <= 1:
    return(results)
  else:
    for x in key_presses[1:]:

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

### Define terms to understand investment potentional
### This function will simulate scenarios to compare renting to buying and allow us to make an informed financial decision
## Parameters of the model (some are hard coded but can still be changed)
## r - Annual appreciation rate
## term - Term you want to own the house for (in months)
## house_cos - cost of the house in dollars
## ir - Interest rate of my mortgage
## closing - total closing costs

emhart

### Define terms to understand investment potentional
### This function will simulate scenarios to compare renting to buying and allow us to make an informed financial decision
## Parameters of the model (some are hard coded but can still be changed)
## r - Annual appreciation rate
## term - Term you want to own the house for (in months)
## house_cos - cost of the house in dollars
## ir - Interest rate of my mortgage
## closing - total closing costs

emhart

library("dplyr")
library("audio")
notes <- c(A = 0, B = 2, C = 3, D = 5, E = 7, F = 8, G = 10)
pitch <- "D D E D G F# D D E D A G D D D5 B G F# E C5 C5 B G A G"
duration <- c(rep(c(0.75, 0.25, 1, 1, 1, 2), 2),
              0.75, 0.25, 1, 1, 1, 1, 1, 0.75, 0.25, 1, 1, 1, 2)
bday <- data_frame(pitch = strsplit(pitch, " ")[[1]],
                   duration = duration)

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

### "Smart brute force" algorithm.  Works in most mortal situations.
### Works by looping through a matrix that is m by k and putting numbers in 
### Is aware of the top and left cell, and works by sampling based on the probabilities of 
### numbers that are left in the set of valid possible numbers, e.g. those not excluded by the neighborhood rules (von Neumann)
### Parameters:
### k -- The number of rows
### m -- The number of columns
### n -- 1:N vector of desired outcomes (plants in the blog post), e.g. if N = 3, your n parameter would be n <- 1:3
### maxiter -- The maximum number of iterations you want to use in the algorithm (default is 100.