Hi,
I am currently analysing an event-related design with different event
durations (4, 6 or 8 sec.). As far as I can see, it is not possible to
use the ERNiftiDataset for this purpose cause it takes the maximum
boxlengths for "all" events.
boxlength = max(durations)
if __debug__:
if not max(durations) == min(durations):
warning('Boxcar mapper will use maximum boxlength (%i)
of all '
'provided Events.'% boxlength)
I was wondering if it would be 'theoretically' possible to modify the
'BoxcarMapper', so that it could also handle different event durations?
My idea was to modify mvpa/datasets/event.py like this:
# we need a regular array, so all events must have a common
# boxlength
# boxlength = max(durations)
# if __debug__:
# if not max(durations) == min(durations):
# warning('Boxcar mapper will use maximum boxlength (%i)
of all '
# 'provided Events.'% boxlength)
# loop over events and extract (different) event durations
boxlength = [e['duration'] for e in events]
and mvpa/mappers/boxcar.py like this:
<snip>
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)
boxlength = N.asanyarray(boxlength)
if N.issubdtype(boxlength.dtype, 'i'):
self.boxlength = boxlength
else:
if __debug__:
debug('MAP', "Boxcar duration error...")
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 __repr__(self):
s = super(BoxcarMapper, self).__repr__()
return s.replace("(", "(boxlength=%s, offset=%d, startpoints=%s, "
"collision_resolution='%s'" %
(str(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)
boxcounter=0
for sp in self.startpoints:
if ( sp + offset + boxlength[boxcounter] - 1 > len(data)-1 ) \
or ( sp + offset < 0 ):
raise ValueError, \
'Illegal box: start: %i, offset: %i, length: %i' \
% (sp, offset, boxlength[boxcounter])
boxcounter+= 1
# 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
# working with fixed boxlength value
# boxlength_dummy=4
# self.__selectors = [ N.arange(i + offset, i + offset +
boxlength_dummy \
# for i in startpoints ]
# build a list of the boxlengths where each boxlength value has
the same index as the
# corresponding startpoint
boxlengths = range(len(data))
boxcounter=0
for s in startpoints:
boxlengths[s] = boxlength[boxcounter]
boxcounter+= 1
# 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 +
boxlengths[i]) \
for i in startpoints ]
selected = N.asarray([ data[ box ] for box in self.__selectors ])
self._outshape = selected.shape
return selected
<snip>
But than the problem occurs that it is not possible to save lists like
[1 2 3 4]
[21 22]
[37 38 39]
[56 57 58 59]
[63 64]
[68 69 70 71]
[75 76]
[86 87 88]
[92 93]
[ 97 98 99 100]
[104 105]
[114 115 116]
[136 137 138 139]
[143 144]
with N.asarray
Now I am reaching the point where I have no idea how to handle this. Do
you have any idea, or future plans to implement a function which can
deal with different event durations?
I really appreciate your help.
Matthias Ekman
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