Class ERNiftiDataset
source code
Dataset with event-defined samples from a NIfTI timeseries image.
This is a convenience dataset to facilitate the analysis of event-related
fMRI datasets. Boxcar-shaped samples are automatically extracted from the
full timeseries using :class:`~mvpa.misc.support.Event` definition lists.
For each event all volumes covering that particular event in time
(including partial coverage) are used to form the corresponding sample.
The class supports the conversion of events defined in 'realtime' into the
descrete temporal space defined by the NIfTI image. Moreover, potentially
varying offsets between true event onset and timepoint of the first selected
volume can be stored as an additional feature in the dataset.
Additionally, the dataset supports masking. This is done similar to the
masking capabilities of :class:`~mvpa.datasets.nifti.NiftiDataset`. However,
the mask can either be of the same shape as a single NIfTI volume, or
can be of the same shape as the generated boxcar samples, i.e.
a samples consisting of three volumes with 24 slices and 64x64 inplane
resolution needs a mask with shape (3, 24, 64, 64). In the former case the
mask volume is automatically expanded to be identical in a volumes of the
boxcar.
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__init__(self,
samples=None,
events=None,
mask=None,
evconv=False,
storeoffset=False,
tr=None,
**kwargs)
If samples and mapper arguments are not None the mapper is
used to forward-map the samples array and the result is passed
to the Dataset constructor. |
source code
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map2Nifti(self,
data=None)
Maps a data vector into the dataspace and wraps it with a
NiftiImage. |
source code
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Inherited from mapped.MappedDataset :
mapForward ,
mapReverse ,
mapSelfReverse ,
selectFeatures
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__init__(self,
samples=None,
events=None,
mask=None,
evconv=False,
storeoffset=False,
tr=None,
**kwargs)
(Constructor)
| source code
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If samples and mapper arguments are not None the mapper is
used to forward-map the samples array and the result is passed
to the Dataset constructor.
- Overrides:
mapped.MappedDataset.__init__
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Maps a data vector into the dataspace and wraps it with a
NiftiImage. The header data of this object is used to initialize
the new NiftiImage.
Only the features corresponding to voxels are mapped back -- not
any additional features passed via the Event definitions.
- Parameters:
data (ndarray or Dataset) - The data to be wrapped into NiftiImage. If None (default), it
would wrap samples of the current dataset. If it is a Dataset
instance -- takes its samples for mapping
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niftihdr
- Value:
property(fget= lambda self: self._dsattr ['niftihdr'], doc= 'Access to
the NIfTI header dictionary.')
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