Source Code for Module pylal.svd_tools

 1  # Copyright (C) 2011  Drew Keppel 
 2  # 
 3  # This program is free software; you can redistribute it and/or modify it 
 4  # under the terms of the GNU General Public License as published by the 
 5  # Free Software Foundation; either version 2 of the License, or (at your 
 6  # option) any later version. 
 7  # 
 8  # This program is distributed in the hope that it will be useful, but 
 9  # WITHOUT ANY WARRANTY; without even the implied warranty of 
10  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General 
11  # Public License for more details. 
12  # 
13  # You should have received a copy of the GNU General Public License along 
14  # with this program; if not, write to the Free Software Foundation, Inc., 
15  # 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA. 
16   
17 -def project_waveforms_onto_bases(T, U, S): 
18          """ 
19          A function to project waveforms given as rows of T onto the basis 
20          vectors given as rows of U and normalized by the singular values S. 
21          This returns the reconstruction coefficients associated with these 
22          waveforms, basis vectors, and singular values. 
23          """ 
24          V = [] 
25          for idx,t in enumerate(T): 
26                  print >> sys.stderr, "\r\ttemplate %i/%i"%(idx+1,len(T)), 
27                  template_copy = numpy.array([t for x in range(len(S))]) 
28                  V.append((U*template_copy).sum(axis=1)/S) 
29          print >> sys.stderr, '' 
30          return numpy.array(V) 
31   
32 -def reconstruct_waveforms_from_SVD(V, S, U, n, normalize=False): 
33          """ 
34          A function to reconstruct waveforms from the reconstruction 
35          coefficients V, singular values S, and basis vectors U. The top n 
36          basis vectors are used for the reconstruction. If the normalize flag 
37          is set to True, the waveforms are normalized when before being 
38          returned. 
39          """ 
40          U = numpy.dot(numpy.diag(S), U)[:n,:] 
41          T = numpy.tensordot(V[:,:n], U, axes=1) 
42          if normalize: 
43                  for idx,t in enumerate(T): 
44                          T[idx] /= sum(t*t)**.5 
45          return T 
46