/usr/share/pyshared/mvpa/mappers/boxcar.py is in python-mvpa 0.4.8-3.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Data mapper"""
__docformat__ = 'restructuredtext'
import numpy as N
from mvpa.base.dochelpers import enhancedDocString
from mvpa.mappers.base import Mapper
from mvpa.misc.support import isInVolume
if __debug__:
from mvpa.base import debug
class BoxcarMapper(Mapper):
"""Mapper to combine multiple samples into a single sample.
.. note::
This mapper is somewhat unconventional since it doesn't preserve number
of samples (ie the size of 0-th dimension).
"""
_COLLISION_RESOLUTIONS = ['mean']
def __init__(self, startpoints, boxlength, offset=0,
collision_resolution='mean'):
"""
:Parameters:
startpoints: sequence
Index values along the first axis of 'data'.
boxlength: int
The number of elements after 'startpoint' along the first axis of
'data' to be considered for the boxcar.
offset: int
The offset between the provided starting point and the actual start
of the boxcar.
collision_resolution : 'mean'
if a sample belonged to multiple output samples, then on reverse,
how to resolve the value
"""
Mapper.__init__(self)
startpoints = N.asanyarray(startpoints)
if N.issubdtype(startpoints.dtype, 'i'):
self.startpoints = startpoints
else:
if __debug__:
debug('MAP', "Boxcar: obtained startpoints are not of int type."
" Rounding and changing dtype")
self.startpoints = N.asanyarray(N.round(startpoints), dtype='i')
# Sanity checks
if boxlength < 1:
raise ValueError, "Boxlength lower than 1 makes no sense."
if boxlength - int(boxlength) != 0:
raise ValueError, "boxlength must be an integer value."
self.boxlength = int(boxlength)
self.offset = offset
self.__selectors = None
if not collision_resolution in self._COLLISION_RESOLUTIONS:
raise ValueError, "Unknown method to resolve the collision." \
" Valid are %s" % self._COLLISION_RESOLUTIONS
self.__collision_resolution = collision_resolution
__doc__ = enhancedDocString('BoxcarMapper', locals(), Mapper)
def __repr__(self):
s = super(BoxcarMapper, self).__repr__()
return s.replace("(", "(boxlength=%d, offset=%d, startpoints=%s, "
"collision_resolution='%s'" %
(self.boxlength, self.offset, str(self.startpoints),
str(self.__collision_resolution)), 1)
def forward(self, data):
"""Project an ND matrix into N+1D matrix
This method also handles the special of forward mapping a single 'raw'
sample. Such a sample is extended (by concatenating clones of itself) to
cover a full boxcar. This functionality is only availably after a full
data array has been forward mapped once.
:Returns:
array: (#startpoint, ...)
"""
# in case the mapper is already charged
if not self.__selectors is None:
# if we have a single 'raw' sample (not a boxcar)
# extend it to cover the full box -- useful if one
# wants to forward map a mask in raw dataspace (e.g.
# fMRI ROI or channel map) into an appropriate mask vector
if data.shape == self._outshape[2:]:
return N.asarray([data] * self.boxlength)
self._inshape = data.shape
startpoints = self.startpoints
offset = self.offset
boxlength = self.boxlength
# check for illegal boxes
for sp in self.startpoints:
if ( sp + offset + boxlength - 1 > len(data)-1 ) \
or ( sp + offset < 0 ):
raise ValueError, \
'Illegal box: start: %i, offset: %i, length: %i' \
% (sp, offset, boxlength)
# build a list of list where each sublist contains the indexes of to be
# averaged data elements
self.__selectors = [ N.arange(i + offset, i + offset + boxlength) \
for i in startpoints ]
selected = N.asarray([ data[ box ] for box in self.__selectors ])
self._outshape = selected.shape
return selected
def reverse(self, data):
"""Uncombine features back into original space.
Samples which were not touched by forward will get value 0 assigned
"""
if data.shape == self._outshape:
# reconstruct to full input space from the provided data
# done below
pass
elif data.shape == self._outshape[1:]:
# single sample was given, simple return it again.
# this is done because other mappers also work with 'single'
# samples
return data
else:
raise ValueError, "BoxcarMapper operates either on single samples" \
" %s or on the full dataset in 'reverse()' which must have " \
"shape %s. Got data of shape %s" \
% (self._outshape[1:], self._outshape, data.shape)
# the rest of this method deals with reconstructing the full input
# space from the boxcar samples
assert(data.shape[0] == len(self.__selectors)) # am I right? :)
output = N.zeros(self._inshape, dtype=data.dtype)
output_counts = N.zeros((self._inshape[0],), dtype=int)
for i, selector in enumerate(self.__selectors):
output[selector, ...] += data[i, ...]
output_counts[selector] += 1
# scale output
if self.__collision_resolution == 'mean':
# which samples how multiple sources?
g1 = output_counts > 1
# average them
# doing complicated transposing to be able to process array with
# nd > 2
output_ = output[g1].T
output_ /= output_counts[g1]
output[g1] = output_.T
return output
def getInSize(self):
"""Returns the number of original samples which were combined.
"""
return self._inshape[0]
def isValidOutId(self, outId):
"""Validate if OutId is valid
"""
try:
return isInVolume(outId, self._outshape[1:])
except:
return False
def isValidInId(self, inId):
"""Validate if InId is valid
"""
try:
return isInVolume(inId, self._inshape[1:])
except:
return False
def getOutSize(self):
"""Returns the number of output samples.
"""
return N.prod(self._outshape[1:])
def selectOut(self, outIds):
"""Just complain for now"""
raise NotImplementedError, \
"For feature selection use MaskMapper on output of the %s mapper" \
% self.__class__.__name__
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