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""" This script provides a toy example of the NBS """
import numpy as np
import cviewer.libs.pyconto.groupstatistics.nbs as nbs
from pylab import imshow, show, title
# Generate simulated data.
# Generate population X. In particular, generate 10 instantiations of a
# 20 x 20 connectivty matrix, where each element is sampled from a standard
# normal distribution. Each connectivity matrix represents a distinct member
# of population X. Elements below the main diagonal are never used.
X = np.random.random( (20,20,10) )
n = X.shape[0]
# Generate population Y in the same way as population X. However, this time,
# simulate a difference between the two populations at two distinct
# components. A component is a set of interconnected edges. Each of the two
# components is simulated by adding a constant factor, c, to the
# standard normal distribution of each edge comprising the component. This
# gives a contrasttonoise ratio of c, given that the variance of the noise
# is unity.
Y = np.random.random( (20,20,10) )
# Additive factor, also equal to the contrasttonoise ratio
c = 2
# Edges comprising component
set1 = np.array([1,3,1,2,2,3,2,5,
2,6,3,4,4,5,4,6])  1
set1.resize( (8,2) )
set2 = np.array([18,20,18,19,19,20,17,18,16,20,16,17,16,18])  1
set2.resize( (7,2) )
# Simulate the component.
for i in range(10):
Y[set1[:,0],set1[:,1]] = Y[set1[:,0],set1[:,1]] + c
Y[set2[:,0],set2[:,1]] = Y[set2[:,0],set2[:,1]] + c
# Run the NBS with the following parameter options:
# Set an appropriate threshold. It is difficult to provide a rule of thumb
# to guide the choice of this threshold. Trialanderror is always an option
# with the number of permutations generated per trial set low.
THRESH=3
# Generate 100 permutations. Many more permutations are required in practice
# to yield a reliable estimate.
K=100
# Set TAIL to left, and thus test the alternative hypothesis that mean of
# population X < mean of population Y
TAIL='left'
# Run the NBS
PVAL, ADJ, NULL = nbs.compute_nbs(X,Y,THRESH,K,TAIL);
print "pval", PVAL
print "null", NULL
imshow(ADJ, interpolation='nearest')
title('Edges identified by the NBS')
show()
# Index of true positives
#ind_tp=[ind_set1;ind_set2];
ind_tp = np.vstack( (set1, set2) )
# Index of positives idenfitied by the NBS
#ind_obs=find(adj);
ind_obs = np.array(np.where(np.triu(ADJ))).T
# False positive rate
#fp=length(setdiff(ind_obs,ind_tp))/(20*19/2);
fp_idx = nbs.setdiff2d(ind_obs, ind_tp)
fp = fp_idx.shape[0] / (n * (n1) / 2.) # only upper triangular matrix is taken into account
# True positive rate
#tp=length(intersect(ind_tp,ind_obs))/length(ind_tp);
tp_idx = nbs.intersect2d(ind_obs, ind_tp)
tp = tp_idx.shape[0] * 1. / len(ind_tp)
print "True positive rate # %0.3f. False positive rate: # %0.3f" % (tp, fp)
