f = 12. / np.max(p_species)
area = np.pi * (p_species*f)**2 # 0 to 12 point radiuses
pp.scatter(traits[0,:],traits[1,:],s=area,c=colors,alpha=0.6)
pp.xlim(0,1)
pp.ylim(0,1)
pp.axis('equal')
pl.show()
# sum of dendrogram branches
dist_species_v = fd.distance_v(traits)
pp.figure()
Z = ch.linkage(dist_species_v,method='single',metric='euclidean')
dend = ch.dendrogram(Z)
b = fd.branch_lengths(Z)
h = fd.branch_presence(Z)
div_fd = fd.fd(h,b)
# calculate rao's Q
dist_species_m = fd.distance_m(traits)
div_q = fd.rao(dist_species_m,p_species)
# calculate FAD
div_fad = fd.fad(dist_species_m)
# print diversity values
print "Functional Diversity: ", div_fd
print "Rao's Q: ", div_q
print "Functional Attribute Diversity: ", div_fad
# -*- coding: utf-8 -*- """ Created on Sun Jun 21 16:56:52 2015 imp @author: amandaprorok """ import numpy as np import pylab as pl import matplotlib.pyplot as pp import numpy.linalg as la import scipy.cluster.hierarchy as ch import funcdef_diversity as fd x = 10 # number of species t = 2 # number of traits / dimensions a = np.random.rand(t, x) d = fd.distance_v(a) Z = ch.linkage(d, method="single", metric="euclidean") dend = ch.dendrogram(Z) b = fd.branch_lengths(Z) h = fd.branch_presence(Z) fd = fd.fd(h, b) print fd
