Source Code for Module mvpa.datasets.masked

 1  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 
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 4  # 
 5  #   See COPYING file distributed along with the PyMVPA package for the 
 6  #   copyright and license terms. 
 7  # 
 8  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
 9  """Dataset with applied mask""" 
10   
11  __docformat__ = 'restructuredtext' 
12   
13  import numpy as N 
14   
15  from mvpa.datasets.mapped import MappedDataset 
16  from mvpa.mappers.array import DenseArrayMapper 
17   
18  if __debug__: 
19      from mvpa.base import debug 
20   
21 -class MaskedDataset(MappedDataset): 
22      """Helper class which is `MappedDataset` with using `MaskMapper`. 
23   
24      TODO: since what it does is simply some checkes/data_mangling in the 
25      constructor, it might be absorbed inside generic `MappedDataset` 
26   
27      """ 
28   
29 -    def __init__(self, samples=None, mask=None, **kwargs): 
30          """ 
31          :Parameters: 
32            mask: ndarray 
33              the chosen features equal the non-zero mask elements. 
34          """ 
35          # might contain the default mapper 
36          mapper = None 
37   
38          # need if clause here as N.array(None) != None 
39          if not samples is None: 
40              # XXX should be asanyarray? but then smth segfaults on unittests 
41              samples = N.asarray(samples) 
42              mapper = DenseArrayMapper(mask=mask, 
43                                        shape=samples.shape[1:]) 
44   
45          if not mapper is None: 
46              if samples is None: 
47                  raise ValueError, \ 
48                        "Constructor of MaskedDataset requires both a samples " \ 
49                        "array and a mask if one of both is provided." 
50              # init base class -- MappedDataset takes care of all the forward 
51              # mapping stuff 
52              MappedDataset.__init__( 
53                  self, 
54                  samples=samples, 
55                  mapper=mapper, 
56                  **(kwargs)) 
57          else: 
58              MappedDataset.__init__(self, **(kwargs)) 
59   
60   
61 -    def selectFeaturesByMask(self, mask, plain=False): 
62          """Use a mask array to select features from the current set. 
63   
64          :Parameters: 
65            mask : ndarray 
66              input mask 
67            plain : bool 
68              `True` directs to return a simple `Dataset`, 
69              `False` -- a new `MaskedDataset` object 
70   
71          Returns a new MaskedDataset object with a view of the original pattern 
72          array (no copying is performed). 
73          The final selection mask only contains features that are present in the 
74          current feature mask AND the selection mask passed to this method. 
75          """ 
76          # AND new and old mask to get the common features 
77          comb_mask = N.logical_and(mask != 0, 
78                                    self.mapper.getMask(copy=False) != 0) 
79          if __debug__: 
80              debug('DS', "VERY SUBOPTIMAL - do not rely on performance") 
81          # transform mask into feature space 
82          fmask = self.mapper.forward( comb_mask != 0 ) 
83          #TODO all this will be gone soon anyway -- need proper selectIn within 
84          # a mapper 
85          return self.selectFeatures(fmask.nonzero()[0], plain=plain) 
86