tpoisot

\usepackage{mathspec}
\usepackage{fontspec,xltxtra,xunicode}
\defaultfontfeatures{Mapping=tex-text}
\setromanfont[Mapping=tex-text]{Warnock Pro Light}
\setsansfont[Scale=MatchLowercase,Mapping=tex-text]{Myriad Pro}
\setmonofont[Scale=MatchLowercase]{Andale Mono}
\setmathfont(Greek,Latin,Digits){Warnock Pro Light}

tpoisot

\setmainfont[
 UprightFeatures = { SmallCapsFont = {Andralis ND SC} },
 BoldFeatures = { SmallCapsFont = {Andralis ND SC Bold} },
 ItalicFeatures = { SmallCapsFont = {Andralis ND SC Italic} },
 BoldItalicFeatures = { SmallCapsFont = {Andralis ND SC Bold Italic} },
]{Andralis ND OsF}

tpoisot

from pyx import *
import numpy as np
import scipy as sp

def null_bernoulli(top,bot,nint):
	tint = top*bot
	zeroes = tint-nint
	## We only return a matrix with no non-interacting species
	hasNull = True
	trials = 0

tpoisot

gfreq = function(p) list(AA=p^2,Aa=2*p*(1-p),aa=(1-p)^2)

mfit = function(p,fit)
{
	with(as.list(fit),{
		mf = (p^2)*wAA+(2*p*(1-p)*wAa)+((1-p)^2)*waa
		return(mf)
	})
}

tpoisot

import numpy as np
import scipy as sp

from scipy.weave import inline

def along2Darray(rows,cols):
	arr = np.zeros((rows,cols))
	code = """
		int Pos;
		for(int Row = 0; Row < rows; Row++)

tpoisot

def COFM(comat,normal=True):
	CoFreq = []
	for sp1 in xrange(len(comat)-1):
		for sp2 in xrange(sp1,len(comat)):
			NCo = 0
			for nsite in xrange(len(comat[0])):
				if comat[sp1][nsite]*comat[sp2][nsite] > 0:
					NCo += 1
                        if normal:
                            NCo /= len(comat[0])

tpoisot

mat = matrix(abs(round(rnorm(100,15,8),0))+1,10)
diag(mat) = 0
print(mat)

permat = function(mat)
{
	mat[lower.tri(mat)+upper.tri(mat)==1] = sample(mat[lower.tri(mat)+upper.tri(mat)==1],replace=FALSE)
	return(mat)
}

tpoisot

\usepackage{MnSymbol}
\usepackage[mathlf,textlf,loosequotes]{MinionPro}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{textcomp}

tpoisot

import matplotlib.pyplot as plt
import networkx as nx
import scipy as sp
import numpy as np

class comm:
    """
    A CLASS FOR COMMUNITIES
    pos : tuple with the spatial position
    id : the name of the community

tpoisot

#!/usr/bin/env python
# encoding: utf-8

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

Patches = 100   # Number of patches
P_ext = 0.01    # Probability of extinction (e)
P_col = 0.014   # Probability of colonization (c)