/usr/share/pyshared/mvpa/mappers/array.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 operator import isSequenceType
from mvpa.mappers.mask import MaskMapper
from mvpa.mappers.metric import DescreteMetric, cartesianDistance
from mvpa.base.dochelpers import enhancedDocString
if __debug__:
from mvpa.base import warning
from mvpa.misc.support import isSorted
class DenseArrayMapper(MaskMapper):
"""Mapper for equally spaced dense arrays."""
"""TODO: yoh thinks we should move that 'metric' assignment into
MaskMapper, based on the fact if distance_function is given either
as an argument or may be class variable. That would pretty much
remove the need for a separate class of DenseArrayMapper and it
could become just a sugaring helper function which would initiate
MaskMapper (or some other mapper may be with appropriate
distance_function and/or mapper
Otherwise it is again -- orthogonality -- will we need to device
NonmaskedArrayMapper which has no mask assigned but might be a
good cartesian cube on its own or smth like that?
"""
def __init__(self, mask=None, metric=None,
distance_function=cartesianDistance,
elementsize=None, shape=None, **kwargs):
"""Initialize DenseArrayMapper
:Parameters:
mask : array
an array in the original dataspace and its nonzero elements are
used to define the features included in the dataset. alternatively,
the `shape` argument can be used to define the array dimensions.
metric : Metric
Corresponding metric for the space. No attempt is made to
determine whether a certain metric is reasonable for this
mapper. If `metric` is None -- `DescreteMetric`
is constructed that assumes an equal (1) spacing of all mask
elements with a `distance_function` given as a parameter listed
below.
distance_function : functor
Distance function to use as the parameter to
`DescreteMetric` if `metric` is not specified,
elementsize : list or scalar
Determines spacing within `DescreteMetric`. If it is given as a
scalar, corresponding value is assigned to all dimensions, which
are found within `mask`
shape: tuple
The shape of the array to be mapped. If `shape` is provided instead
of `mask`, a full mask (all True) of the desired shape is
constructed. If `shape` is specified in addition to `mask`, the
provided mask is extended to have the same number of dimensions.
:Note: parameters `elementsize` and `distance_function` are relevant
only if `metric` is None
"""
if mask is None:
if shape is None:
raise ValueError, \
"Either `shape` or `mask` have to be specified."
else:
# make full dataspace mask if nothing else is provided
mask = N.ones(shape, dtype='bool')
else:
if not shape is None:
# expand mask to span all dimensions but first one
# necessary e.g. if only one slice from timeseries of volumes is
# requested.
mask = N.array(mask, ndmin=len(shape))
# check for compatibility
if not shape == mask.shape:
raise ValueError, \
"The mask dataspace shape %s is not " \
"compatible with the provided shape %s." \
% (mask.shape, shape)
# configure the baseclass with the processed mask
MaskMapper.__init__(self, mask, metric=metric, **kwargs)
# We must have metric assigned
if self.metric == None:
if elementsize is None:
elementsize = [1]*len(mask.shape)
else:
if isSequenceType(elementsize):
if len(elementsize) != len(mask.shape):
raise ValueError, \
"Number of elements in elementsize [%d]" % \
len(elementsize) + " doesn't match shape " + \
"of the mask [%s]" % (`mask.shape`)
else:
elementsize = [ elementsize ] * len(mask.shape)
self.metric = DescreteMetric(elementsize=[1]*len(mask.shape),
distance_function=distance_function)
__doc__ = enhancedDocString('DenseArrayMapper', locals(), MaskMapper)
def __str__(self):
return "DenseArrayMapper: %d -> %d" \
% (self.getInSize(), self.getOutSize())
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