def plotallpnts(self, event): # wxGlade: MyFrame.<event_handler>
print "Event handler `plotallpnts' not implemented"
r = self.postcheck()
if r == -1: #nothing posted
return
print 'plotting ',self.datapdf
self.p = pdf.read(self.datapdf[0])
print self.p
ind=hstack([self.p.hs.index_lpa,self.p.hs.index_rpa,self.p.hs.index_nasion]).reshape(3,3)
s=sensors.locations(self.datapdf[0])
plotvtk.display(self.p.hs.hs_point, s.megchlpos, ind)
def __init__(self, path2pdf, leadfield, weight):
'''leadfield structured, grid X channels X 3'''
self.ext = 'pymwf'
reducedrank=2;
ts = time()
if len(weight.shape) == 1: #check winv and if 1D make 2D.
weight=array([weight])
if size(weight,0) != size(leadfield,1): #need to reshape weight to match leadfields
weight = weight.transpose()
print 'weight reshaped to match leadfields'
if size(weight,0) != size(leadfield,1): #something wrong, exit
print 'something wrong... weight and leadfields dont match'
return
self.winv = winv = copy(weight) #should be channels X numofweights
Lc=zeros((size(leadfield,1), size(leadfield,0)*2));
for d in range(0, size(leadfield, 0)): #for each gridpoint
u, s, vh = linalg.svd(leadfield[d,:,:]); v = vh.T
self.u=u
self.s=s
self.v=v
Lc[:,((d+1-1)*2):(d+1)*2] = u[:,0:2];
self.Lc=Lc
s=sensors.locations(path2pdf) #get sensor pos
chanlocs=s.megchlpos
chanlocsright = chanlocs[chanlocs[:,1] < 0]
chanlocsleft = chanlocs[chanlocs[:,1] > 0]
for i in range(0, size(winv,1)): #remove mean from matrix
winv[:,i] = winv[:,i] - mean(winv[:,i])
self.winv = winv
self.winv_std = winv_std = sqrt(sum(winv**2,0));
self.fn = fn=arange(size(leadfield,0));
self.f = f = fn
self.psi_reconstruct = psi_reconstruct = zeros((len(fn)*2,len(f)));
pbar = progressbar.ProgressBar().start()
for i in range(0,len(f)):
L = Lc[:,(i+1-1)*2+1-1:(i+1)*2]; #check indicies
self.L=L
for j in range(0,size(L,1)):
L[:,j] = L[:,j] - mean(L[:,j]);
self.psi_tmp = psi_tmp = zeros([2,size(winv,1)]);
self.corr_tmp = corr_tmp = zeros([size(winv,1)]);
for j in range(0, size(winv,1)):
#[Q,V] = linalg.eig(dot(dot(L.transpose(),winv[:,j]).reshape([2,1]),dot(winv[:,j].transpose(),L).reshape([1,2])),dot(L.transpose(),L)); # maximize normalized correlation
w1 = dot(dot(L.transpose(),winv[:,j]).reshape([2,1]),dot(winv[:,j].transpose(),L).reshape([1,2]))
w2 = dot(L.transpose(),L)
self.w1 = w1
self.w2 = w2
self.A = A = linalg.eig(w1)#,w2); # maximize normalized correlation
#[val,pos] = max(diag(V));
self.pos = pos = A[0].argmax()
self.poscopy = pos
Q = A[1]
if dot(Q[:,pos].transpose(),dot(L.transpose(),winv[:,j])) < 0:
Q[:,pos] = - Q[:,pos]; # correct the sign
self.Q = Q
self.psi_tmp[:,j] = psi_tmp[:,j] = Q[:,pos];
self.L_std = L_std = sqrt(sum(dot(L,Q[:,pos])**2,axis=0));
corr_tmp[j] = dot(dot(Q[:,pos].transpose(),L.transpose()),winv[:,j])/dot(L_std,winv_std[j]);
self.corr_tmp = corr_tmp
self.pos = pos = corr_tmp.argmax();
prange = array([0,2])
##print 'working on', i+1, 'out of', size(f), 'gridpoints'
psi_reconstruct[prange[0]+(i+1-1)*2:prange[1]+(i+1-1)*2,i] = psi_tmp[:,pos];
self.psi_reconstruct = psi_reconstruct
sys.stdout.flush()
pbar.update((float(i)/float(len(f)))*100)
sys.stdout.flush()
print('please wait, need to do some heavy number crunching');sys.stdout.flush()
self.L = L = dot(Lc,psi_reconstruct);
for i in range(0,size(L,1)):#remove mean
L[:,i] = L[:,i] - mean(L[:,i])
self.L = L
#compute standard deviation of each column
self.L_std = L_std = sqrt(sum(L**2,0));
#compute absolute value of normalized corrrelation coefficient between columns
#corr_mat[:,:] = NaN*ones(size(winv,1),size(leadfield, 0));
self.corr_mat = corr_mat = zeros([size(winv,1),size(leadfield, 0)]);
for i in range(0,len(f)):
for j in range(0,size(winv,1)):
corr_mat[j,f[i]] = (abs(dot(L[:,f[i]].transpose(),winv[:,j]))/dot(L_std[f[i]],winv_std[j]));
te = time()
print 'elapsed time', te-ts, 'seconds'
del self.L, self.Lc, self.psi_reconstruct, self.winv, self.winv_std
