Bases: nipy.core.image.image_list.ImageList
Class to implement image list interface for FMRI time series
Allows metadata such as volume and slice times
Methods
from_image(klass, fourdimage[, axis, ...]) | Create an FmriImageList from a 4D Image |
get_list_data([axis]) | Return data in ndarray with list dimension at position axis |
next() |
An implementation of an fMRI image as in ImageList
Parameters: | images : iterable
volume_start_times: None or float or (N,) ndarray :
slice_times: None or (N,) ndarray :
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See also
nipy.core.image_list.ImageList
Examples
>>> from nipy.testing import funcfile
>>> from nipy.io.api import load_image
>>> from nipy.core.api import iter_axis
>>> funcim = load_image(funcfile)
>>> iterable_img = iter_axis(funcim, 't')
>>> fmrilist = FmriImageList(iterable_img)
>>> print fmrilist.get_list_data(axis=0).shape
(20, 17, 21, 3)
>>> print fmrilist[4].shape
(17, 21, 3)
Create an FmriImageList from a 4D Image
Get images by extracting 3d images along the ‘t’ axis.
Parameters: | fourdimage : Image instance
volume_start_times: None or float or (N,) ndarray :
slice_times: None or (N,) ndarray :
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Returns: | filist : FmriImageList instance |
Return data in ndarray with list dimension at position axis
Parameters: | axis : int
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Returns: | data : ndarray
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Examples
>>> from nipy.testing import funcfile
>>> from nipy.io.api import load_image
>>> funcim = load_image(funcfile)
>>> ilist = ImageList.from_image(funcim, axis='t')
>>> ilist.get_list_data(axis=0).shape
(20, 17, 21, 3)
Takes array-like data, returning slices over axes > 0
This function takes an array-like object data and yields tuples of slicing thing and slices like:
[slicer, np.asarray(data)[:,slicer] for slicer in slicer]
which in the default (slicers is None) case, boils down to:
[i, np.asarray(data)[:,i] for i in range(data.shape[1])]
This can be used to get arrays of time series out of an array if the time axis is axis 0.
Parameters: | data : array-like
slicers : None or sequence
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