pearcemc

plot.aic <- function(fit, new=T, sd=0.1)
{ # code to plot AIC against order of AR model fitted
  if (new) plot((1:length(fit$aic))-1,fit$aic,type="l",xlab="Order",ylab="AIC") else
  lines((1:length(fit$aic))-1,fit$aic,type="l")
  points(rnorm(1,fit$order,sd=sd),rnorm(1,sd=sd),pch=16,col="red")
}

plot.stat_ic <- function(stat, new=T, sd=0.1)
{ # code to plot AIC against order of AR model fitted
  if (new) plot((1:length(stat))-1,stat,type="l",xlab="Order",ylab="AIC") else

pearcemc

from pylab import *
import numpy

def grid_sq(fr,ncol,bs,npx=2,bval=0):
  Z = fr.shape[0]
  z=0
  vbar = bval*ones((bs[0],npx))
  hbar = bval*ones((npx, (bs[1] + npx)*ncol + npx)) 
  grid = [hbar]
  row = [vbar]

pearcemc

from pylab import *

import scipy.stats as stats

########################################################################################
#
#       QUICK ILLUSTRATION OF USING SCIPY.STATS FOR GAMMA AND INV-GAMMA
#       HOW TO DO SHAPE-RATE (alpha, beta) PARAMETERISATION CORRECTLY
#
########################################################################################

pearcemc

# context: http://stackoverflow.com/questions/39448808/julia-tcp-server-and-connection

# Use fn to process messages from sock.
# Loop till sock is open and fn returns true.
function processor(fn, sock)
    proc = true
    try
        while proc && ((nb_available(sock) > 0) || isopen(sock))
            proc = fn(sock)
        end